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Musilova J, Vafek Z, Puniya BL, Zimmer R, Helikar T, Sedlar K. Augusta: From RNA-Seq to gene regulatory networks and Boolean models. Comput Struct Biotechnol J 2024; 23:783-790. [PMID: 38312198 PMCID: PMC10837063 DOI: 10.1016/j.csbj.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/06/2024] Open
Abstract
Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.
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Affiliation(s)
- Jana Musilova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno 61600, Czech Republic
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
| | - Zdenek Vafek
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
- Institute of Forensic Engineering, Brno University of Technology, Brno 61200, Czech Republic
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
| | - Ralf Zimmer
- Department of Informatics, Ludwig-Maximilians-Universität München, Munich 80539, Germany
| | - Tomas Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln 68588, NE, USA
| | - Karel Sedlar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno 61600, Czech Republic
- Department of Informatics, Ludwig-Maximilians-Universität München, Munich 80539, Germany
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Gottumukkala SB, Ganesan TS, Palanisamy A. Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer. NPJ Syst Biol Appl 2024; 10:53. [PMID: 38760412 PMCID: PMC11101644 DOI: 10.1038/s41540-024-00378-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 04/29/2024] [Indexed: 05/19/2024] Open
Abstract
Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.
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Affiliation(s)
| | - Trivadi Sundaram Ganesan
- Department of Medical Oncology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India
| | - Anbumathi Palanisamy
- Department of Biotechnology, National Institute of Technology Warangal, Warangal, India.
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Giannantoni L, Bardini R, Savino A, Di Carlo S. Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems. BMC Bioinformatics 2024; 25:166. [PMID: 38664639 PMCID: PMC11046772 DOI: 10.1186/s12859-024-05782-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems. RESULTS BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers. CONCLUSIONS BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.
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Affiliation(s)
- Leonardo Giannantoni
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
| | - Roberta Bardini
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy.
| | - Alessandro Savino
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
| | - Stefano Di Carlo
- Department of Control and Computer Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi, 24, 100129, Turin, TO, Italy
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Patil N, Mirveis Z, Byrne HJ. Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays. FEBS Open Bio 2024; 14:466-486. [PMID: 38217078 PMCID: PMC10909989 DOI: 10.1002/2211-5463.13765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/21/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
This study aims to demonstrate the benefits of augmenting commercially available, real-time, in vitro glycolysis assays with phenomenological rate equation-based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell-lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two-phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway-modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end-product. This study demonstrates that augmenting a relatively simple, real-time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems.
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Affiliation(s)
- Nitin Patil
- FOCAS Research InstituteTU DublinIreland
- School of Physics, Optometric and Clinical SciencesTU DublinIreland
| | - Zohreh Mirveis
- FOCAS Research InstituteTU DublinIreland
- School of Physics, Optometric and Clinical SciencesTU DublinIreland
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Zerrouk N, Alcraft R, Hall BA, Augé F, Niarakis A. Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis. NPJ Syst Biol Appl 2024; 10:10. [PMID: 38272919 PMCID: PMC10811231 DOI: 10.1038/s41540-024-00337-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
Macrophages play an essential role in rheumatoid arthritis. Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in rheumatoid arthritis is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we used a newly developed version of the BMA tool deployed to a high-performance computing cluster to identify the models' steady states. The identified attractors are then validated using gene expression data sets and prior knowledge. We successfully applied our framework to generate and calibrate the M1 and M2 macrophage Boolean models for rheumatoid arthritis. Using KO simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages and GSK3B as a promising target that could promote anti-inflammatory macrophages in rheumatoid arthritis.
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Affiliation(s)
- Naouel Zerrouk
- GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France
- Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 1, Av Pierre Brossolette, 91385, Chilly-Mazarin, France
| | - Rachel Alcraft
- Advanced Research Computing Centre, University College London, London, UK
| | - Benjamin A Hall
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Franck Augé
- Sanofi R&D Data and Data Science, Artificial Intelligence & Deep Analytics, Omics Data Science, 1, Av Pierre Brossolette, 91385, Chilly-Mazarin, France
| | - Anna Niarakis
- GenHotel, Laboratoire Européen de Recherche Pour La Polyarthrite Rhumatoïde, University Paris-Saclay, University Evry, Evry, France.
- Lifeware Group, Inria Saclay, Palaiseau, France.
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Weidner FM, Ikonomi N, Werle SD, Schwab JD, Kestler HA. GatekeepR: an R Shiny application for the identification of nodes with high dynamic impact in Boolean networks. Bioinformatics 2024; 40:btae007. [PMID: 38195862 DOI: 10.1093/bioinformatics/btae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/23/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024] Open
Abstract
MOTIVATION Boolean networks can serve as straightforward models for understanding processes such as gene regulation, and employing logical rules. These rules can either be derived from existing literature or by data-driven approaches. However, in the context of large networks, the exhaustive search for intervention targets becomes challenging due to the exponential expansion of a Boolean network's state space and the multitude of potential target candidates, along with their various combinations. Instead, we can employ the logical rules and resultant interaction graph as a means to identify targets of specific interest within larger-scale models. This approach not only facilitates the screening process but also serves as a preliminary filtering step, enabling the focused investigation of candidates that hold promise for more profound dynamic analysis. However, applying this method requires a working knowledge of R, thus restricting the range of potential users. We, therefore, aim to provide an application that makes this method accessible to a broader scientific community. RESULTS Here, we introduce GatekeepR, a graphical, web-based R Shiny application that enables scientists to screen Boolean network models for possible intervention targets whose perturbation is likely to have a large impact on the system's dynamics. This application does not require a local installation or knowledge of R and provides the suggested targets along with additional network information and visualizations in an intuitive, easy-to-use manner. The Supplementary Material describes the underlying method for identifying these nodes along with an example application in a network modeling pancreatic cancer. AVAILABILITY AND IMPLEMENTATION https://www.github.com/sysbio-bioinf/GatekeepR https://abel.informatik.uni-ulm.de/shiny/GatekeepR/.
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Affiliation(s)
- Felix M Weidner
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Nensi Ikonomi
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Silke D Werle
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Julian D Schwab
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
| | - Hans A Kestler
- Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany
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Veyssiere M, Sadat Aghamiri S, Hernandez Cervantes A, Henry T, Soumelis V. A mathematical model of Familial Mediterranean Fever predicts mechanisms controlling inflammation. Clin Immunol 2023; 257:109839. [PMID: 37952562 DOI: 10.1016/j.clim.2023.109839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Familial Mediterranean Fever (FMF) is a monogenic disease caused by gain-of-function mutations in the MEditerranean FeVer (MEFV) gene. The molecular dysregulations induced by these mutations and the associated causal mechanisms are complex and intricate. OBJECTIVE We sought to provide a computational model capturing the mechanistic details of biological pathways involved in FMF physiopathology and enabling the study of the patient's immune cell dynamics. METHODS We carried out a literature survey to identify experimental studies published from January 2000 to December 2020, and integrated its results into a molecular map and a mathematical model. Then, we studied the network of molecular interactions and the dynamic of monocytes to identify key players for inflammation phenotype in FMF patients. RESULTS We built a molecular map of FMF integrating in a structured manner the current knowledge regarding pathophysiological processes participating in the triggering and perpetuation of the disease flares. The mathematical model derived from the map reproduced patient's monocyte behavior, in particular its proinflammatory role via the Pyrin inflammasome activation. Network analysis and in silico experiments identified NF-κB and JAK1/TYK2 as critical to modulate IL-1β- and IL-18-mediated inflammation. CONCLUSION The in silico model of FMF monocyte proved its ability to reproduce in vitro observations. Considering the difficulties related to experimental settings and financial investments to test combinations of stimuli/perturbation in vitro, this model could be used to test complex hypotheses in silico, thus narrowing down the number of in vitro and ex vivo experiments to perform.
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Affiliation(s)
| | - Sara Sadat Aghamiri
- Université Paris Cité, INSERM U976, Paris, France; University of Nebraska-Lincoln, Lincoln, NE, United States
| | | | - Thomas Henry
- CIRI, Centre International de Recherche en Infectiologie, Inserm U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Univ Lyon, Lyon F-69007, France
| | - Vassili Soumelis
- Université Paris Cité, INSERM U976, Paris, France; Owkin, 14 boulevard Poissonniere, Paris 75009, France.
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Aghakhani S, Silva-Saffar SE, Soliman S, Niarakis A. Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts. Comput Struct Biotechnol J 2023; 21:4196-4206. [PMID: 37705596 PMCID: PMC10495551 DOI: 10.1016/j.csbj.2023.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Cancer-associated fibroblasts (CAFs) are amongst the key players of the tumor microenvironment (TME) and are involved in cancer initiation, progression, and resistance to therapy. They exhibit aggressive phenotypes affecting extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining a cell- and disease-specific asynchronous Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, in light of our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Sacha E Silva-Saffar
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Anna Niarakis
- GenHotel – European Research Laboratory for Rheumatoid Arthritis, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
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Singh V, Naldi A, Soliman S, Niarakis A. A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint. NPJ Syst Biol Appl 2023; 9:33. [PMID: 37454172 DOI: 10.1038/s41540-023-00294-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023] Open
Abstract
Rheumatoid arthritis (RA) is a complex autoimmune disease with an unknown aetiology. However, rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) play a significant role in initiating and perpetuating destructive joint inflammation by expressing immuno-modulating cytokines, adhesion molecules, and matrix remodelling enzymes. In addition, RA-FLS are primary drivers of inflammation, displaying high proliferative rates and an apoptosis-resistant phenotype. Thus, RA-FLS-directed therapies could become a complementary approach to immune-directed therapies by predicting the optimal conditions that would favour RA-FLS apoptosis, limit inflammation, slow the proliferation rate and minimise bone erosion and cartilage destruction. In this paper, we present a large-scale Boolean model for RA-FLS that consists of five submodels focusing on apoptosis, cell proliferation, matrix degradation, bone erosion and inflammation. The five-phenotype-specific submodels can be simulated independently or as a global model. In silico simulations and perturbations reproduced the expected biological behaviour of the system under defined initial conditions and input values. The model was then used to mimic the effect of mono or combined therapeutic treatments and predict novel targets and drug candidates through drug repurposing analysis.
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Affiliation(s)
- Vidisha Singh
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, Univ Evry, Evry, France
| | - Aurelien Naldi
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, Univ Evry, Evry, France.
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France.
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Trinh VG, Benhamou B, Henzinger T, Pastva S. Trap spaces of multi-valued networks: definition, computation, and applications. Bioinformatics 2023; 39:i513-i522. [PMID: 37387165 DOI: 10.1093/bioinformatics/btad262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Boolean networks are simple but efficient mathematical formalism for modelling complex biological systems. However, having only two levels of activation is sometimes not enough to fully capture the dynamics of real-world biological systems. Hence, the need for multi-valued networks (MVNs), a generalization of Boolean networks. Despite the importance of MVNs for modelling biological systems, only limited progress has been made on developing theories, analysis methods, and tools that can support them. In particular, the recent use of trap spaces in Boolean networks made a great impact on the field of systems biology, but there has been no similar concept defined and studied for MVNs to date. RESULTS In this work, we generalize the concept of trap spaces in Boolean networks to that in MVNs. We then develop the theory and the analysis methods for trap spaces in MVNs. In particular, we implement all proposed methods in a Python package called trapmvn. Not only showing the applicability of our approach via a realistic case study, we also evaluate the time efficiency of the method on a large collection of real-world models. The experimental results confirm the time efficiency, which we believe enables more accurate analysis on larger and more complex multi-valued models. AVAILABILITY AND IMPLEMENTATION Source code and data are freely available at https://github.com/giang-trinh/trap-mvn.
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Affiliation(s)
| | | | - Thomas Henzinger
- Institute of Science and Technology, Klosterneuburg 3400, Austria
| | - Samuel Pastva
- Institute of Science and Technology, Klosterneuburg 3400, Austria
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Hemedan AA, Schneider R, Ostaszewski M. Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses. FRONTIERS IN BIOINFORMATICS 2023; 3:1189723. [PMID: 37325771 PMCID: PMC10267406 DOI: 10.3389/fbinf.2023.1189723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Abstract
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
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Argyris GA, Lluch Lafuente A, Tribastone M, Tschaikowski M, Vandin A. Reducing Boolean networks with backward equivalence. BMC Bioinformatics 2023; 24:212. [PMID: 37221494 DOI: 10.1186/s12859-023-05326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis. RESULTS We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance. CONCLUSIONS BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.
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Affiliation(s)
- Georgios A Argyris
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Alberto Lluch Lafuente
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | | | - Max Tschaikowski
- Department of Computer Science, University of Aalborg, Aalborg, Denmark
| | - Andrea Vandin
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
- Department of Excellence EMbeDS and Institute of Economics, Sant'Anna School for Advanced Studies, Pisa, Italy.
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Aghamiri SS, Puniya BL, Amin R, Helikar T. A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery. Front Immunol 2023; 14:1112985. [PMID: 36993954 PMCID: PMC10040975 DOI: 10.3389/fimmu.2023.1112985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.
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Affiliation(s)
| | | | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
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14
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Buckler AJ, Marlevi D, Skenteris NT, Lengquist M, Kronqvist M, Matic L, Hedin U. In silico model of atherosclerosis with individual patient calibration to enable precision medicine for cardiovascular disease. Comput Biol Med 2023; 152:106364. [PMID: 36525832 DOI: 10.1016/j.compbiomed.2022.106364] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/01/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022]
Abstract
OBJECTIVE Guidance for preventing myocardial infarction and ischemic stroke by tailoring treatment for individual patients with atherosclerosis is an unmet need. Such development may be possible with computational modeling. Given the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Here, we aimed to develop a clinically relevant scale model of atherosclerosis, calibrate it with individual patient data, and use it to simulate optimized pharmacotherapy for individual patients. APPROACH AND RESULTS The study used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model for simulating individualized responses to pharmacotherapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. After calibrating the systems biology models for individual patients, we simulated intensive lipid-lowering, anti-inflammatory, and anti-diabetic drugs. We also simulated a combination therapy. Drug response was evaluated as the degree of change in plaque stability, where an improvement was defined as a reduction of plaque instability. In patients with initially unstable lesions, simulated responses varied from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement. CONCLUSION In this pilot study, proteomics-based system biology modeling was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%, providing a potential strategy for improved personalized management of patients with cardiovascular disease.
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Affiliation(s)
- Andrew J Buckler
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Elucid Bioimaging Inc., Boston, MA, USA
| | - David Marlevi
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Nikolaos T Skenteris
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Mariette Lengquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Malin Kronqvist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Ljubica Matic
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Ulf Hedin
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
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15
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Aghakhani S, Soliman S, Niarakis A. Metabolic reprogramming in Rheumatoid Arthritis Synovial Fibroblasts: A hybrid modeling approach. PLoS Comput Biol 2022; 18:e1010408. [PMID: 36508473 PMCID: PMC9779668 DOI: 10.1371/journal.pcbi.1010408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/22/2022] [Accepted: 11/11/2022] [Indexed: 12/15/2022] Open
Abstract
Rheumatoid Arthritis (RA) is an autoimmune disease characterized by a highly invasive pannus formation consisting mainly of Synovial Fibroblasts (RASFs). This pannus leads to cartilage, bone, and soft tissue destruction in the affected joint. RASFs' activation is associated with metabolic alterations resulting from dysregulation of extracellular signals' transduction and gene regulation. Deciphering the intricate mechanisms at the origin of this metabolic reprogramming may provide significant insight into RASFs' involvement in RA's pathogenesis and offer new therapeutic strategies. Qualitative and quantitative dynamic modeling can address some of these features, but hybrid models represent a real asset in their ability to span multiple layers of biological machinery. This work presents the first hybrid RASF model: the combination of a cell-specific qualitative regulatory network with a global metabolic network. The automated framework for hybrid modeling exploits the regulatory network's trap-spaces as additional constraints on the metabolic network. Subsequent flux balance analysis allows assessment of RASFs' regulatory outcomes' impact on their metabolic flux distribution. The hybrid RASF model reproduces the experimentally observed metabolic reprogramming induced by signaling and gene regulation in RASFs. Simulations also enable further hypotheses on the potential reverse Warburg effect in RA. RASFs may undergo metabolic reprogramming to turn into "metabolic factories", producing high levels of energy-rich fuels and nutrients for neighboring demanding cells through the crucial role of HIF1.
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Affiliation(s)
- Sahar Aghakhani
- GenHotel–Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Sylvain Soliman
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
| | - Anna Niarakis
- GenHotel–Laboratoire Européen de Recherche pour la Polyarthrite Rhumatoïde, Univ. Evry, Univ. Paris-Saclay, Evry, France
- Lifeware Group, Inria Saclay Île-de-France, Palaiseau, France
- * E-mail:
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16
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Lesage R, Ferrao Blanco MN, Narcisi R, Welting T, van Osch GJVM, Geris L. An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis. BMC Biol 2022; 20:253. [DOI: 10.1186/s12915-022-01451-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
Without the availability of disease-modifying drugs, there is an unmet therapeutic need for osteoarthritic patients. During osteoarthritis, the homeostasis of articular chondrocytes is dysregulated and a phenotypical transition called hypertrophy occurs, leading to cartilage degeneration. Targeting this phenotypic transition has emerged as a potential therapeutic strategy. Chondrocyte phenotype maintenance and switch are controlled by an intricate network of intracellular factors, each influenced by a myriad of feedback mechanisms, making it challenging to intuitively predict treatment outcomes, while in silico modeling can help unravel that complexity. In this study, we aim to develop a virtual articular chondrocyte to guide experiments in order to rationalize the identification of potential drug targets via screening of combination therapies through computational modeling and simulations.
Results
We developed a signal transduction network model using knowledge-based and data-driven (machine learning) modeling technologies. The in silico high-throughput screening of (pairwise) perturbations operated with that network model highlighted conditions potentially affecting the hypertrophic switch. A selection of promising combinations was further tested in a murine cell line and primary human chondrocytes, which notably highlighted a previously unreported synergistic effect between the protein kinase A and the fibroblast growth factor receptor 1.
Conclusions
Here, we provide a virtual articular chondrocyte in the form of a signal transduction interactive knowledge base and of an executable computational model. Our in silico-in vitro strategy opens new routes for developing osteoarthritis targeting therapies by refining the early stages of drug target discovery.
Graphical Abstract
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17
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Niarakis A, Waltemath D, Glazier J, Schreiber F, Keating SM, Nickerson D, Chaouiya C, Siegel A, Noël V, Hermjakob H, Helikar T, Soliman S, Calzone L. Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology. Brief Bioinform 2022; 23:bbac212. [PMID: 35671510 PMCID: PMC9294410 DOI: 10.1093/bib/bbac212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/20/2022] [Accepted: 05/06/2022] [Indexed: 11/14/2022] Open
Abstract
Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.
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Affiliation(s)
- Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Dagmar Waltemath
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - James Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Australia
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Anne Siegel
- Univ Rennes, CNRS, Inria - IRISA lab. Rennes
| | - Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Henning Hermjakob
- EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
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18
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Vignet P, Coquet J, Auber S, Boudet M, Siegel A, Théret N. Discrete modeling for integration and analysis of large-scale signaling networks. PLoS Comput Biol 2022; 18:e1010175. [PMID: 35696426 PMCID: PMC9232147 DOI: 10.1371/journal.pcbi.1010175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/24/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states. The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene). Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states.
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Affiliation(s)
- Pierre Vignet
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jean Coquet
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Sébastien Auber
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Matéo Boudet
- IGEPP, Agrocampus Ouest, INRAE, Université de Rennes 1, Le Rheu, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- * E-mail: (AS); (NT)
| | - Nathalie Théret
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- * E-mail: (AS); (NT)
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19
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Zhu AZX, Rogge M. Applications of Quantitative System Pharmacology Modeling to Model-Informed Drug Development. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:71-86. [PMID: 35437719 DOI: 10.1007/978-1-0716-2265-0_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Significant advances in analytical technologies have dramatically improved our ability to deconvolute disease biology at molecular, cellular, and tissue levels. Quantitative system pharmacology (QSP) modeling is a computational framework to systematically integrate pharmaceutical properties of a drug candidate with scientific understanding of that deeper disease etiology, target expression, genetic variability, and human physiological processes, thus enabling more insightful drug development decisions related to efficacy and safety. In this chapter, we discuss the key attributes of QSP models in comparison to traditional models. We discuss a recommended four-step process to construct a QSP model to support drug development decisions. A number of illustrative QSP examples related to high-value drug development questions and decisions impacting target identification, lead generation and optimization, first in human studies, and clinical dose and schedule optimization are covered in the chapter. The future perspectives of QSP in the context of potential regulatory acceptance are also discussed.
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Affiliation(s)
- Andy Z X Zhu
- Preclinical and Translational Sciences, Takeda Pharmaceuticals International Co, Cambridge, MA, USA.
| | - Mark Rogge
- Center for Pharmacometrics and Systems Pharmacology, University of Florida, Lake Nona, FL, USA
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20
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Stoll G, Naldi A, Noël V, Viara E, Barillot E, Kroemer G, Thieffry D, Calzone L. UPMaBoSS: A Novel Framework for Dynamic Cell Population Modeling. Front Mol Biosci 2022; 9:800152. [PMID: 35309516 PMCID: PMC8924294 DOI: 10.3389/fmolb.2022.800152] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/21/2022] [Indexed: 11/13/2022] Open
Abstract
Mathematical modeling aims at understanding the effects of biological perturbations, suggesting ways to intervene and to reestablish proper cell functioning in diseases such as cancer or in autoimmune disorders. This is a difficult task for obvious reasons: the level of details needed to describe the intra-cellular processes involved, the numerous interactions between cells and cell types, and the complex dynamical properties of such populations where cells die, divide and interact constantly, to cite a few. Another important difficulty comes from the spatial distribution of these cells, their diffusion and motility. All of these aspects cannot be easily resolved in a unique mathematical model or with a unique formalism. To cope with some of these issues, we introduce here a novel framework, UPMaBoSS (for Update Population MaBoSS), dedicated to modeling dynamic populations of interacting cells. We rely on the preexisting tool MaBoSS, which enables probabilistic simulations of cellular networks. A novel software layer is added to account for cell interactions and population dynamics, but without considering the spatial dimension. This modeling approach can be seen as an intermediate step towards more complex spatial descriptions. We illustrate our methodology by means of a case study dealing with TNF-induced cell death. Interestingly, the simulation of cell population dynamics with UPMaBoSS reveals a mechanism of resistance triggered by TNF treatment. Relatively easy to encode, UPMaBoSS simulations require only moderate computational power and execution time. To ease the reproduction of simulations, we provide several Jupyter notebooks that can be accessed within the CoLoMoTo Docker image, which contains all software and models used for this study.
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Affiliation(s)
- Gautier Stoll
- Equipe Labellisée Par La Ligue Contre Le Cancer, Université de Paris, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Paris, France
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France
| | - Aurélien Naldi
- Institut de Biologie de L’ENS (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM U900, Paris, France
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France
| | | | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France
- INSERM U900, Paris, France
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France
| | - Guido Kroemer
- Equipe Labellisée Par La Ligue Contre Le Cancer, Université de Paris, Sorbonne Université, INSERM UMR1138, Centre de Recherche des Cordeliers, Paris, France
- Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Université Paris Saclay, Villejuif, France
- Pôle de Biologie, Hôpital européen Georges Pompidou, AP-HP, Paris, France
| | - Denis Thieffry
- Institut de Biologie de L’ENS (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM U900, Paris, France
- MINES ParisTech, CBIO-Centre for Computational Biology, PSL Research University, Paris, France
- *Correspondence: Laurence Calzone,
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21
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Sordo Vieira L, Laubenbacher RC. Computational models in systems biology: standards, dissemination, and best practices. Curr Opin Biotechnol 2022; 75:102702. [PMID: 35217296 DOI: 10.1016/j.copbio.2022.102702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 11/15/2021] [Accepted: 02/03/2022] [Indexed: 11/29/2022]
Abstract
Mathematical and computational models are a key technology in systems biology. Progress in the field depends on the replicability and reproducibility of their properties and behavior. For this, an essential requirement is a set of clear standards for model specification and dissemination. This review covers existing standards, and it highlights the most important areas where further work is required. This includes the specification of agent-based models, an increasingly common modeling approach.
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Affiliation(s)
- Luis Sordo Vieira
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, FL 32610, United States; Department of Psychiatry, University of Florida, Gainesville, FL 32610, United States
| | - Reinhard C Laubenbacher
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Florida, Gainesville, FL 32610, United States.
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22
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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23
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Tandon G, Yadav S, Kaur S. Pathway modeling and simulation analysis. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00007-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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24
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Hemedan AA, Niarakis A, Schneider R, Ostaszewski M. Boolean modelling as a logic-based dynamic approach in systems medicine. Comput Struct Biotechnol J 2022; 20:3161-3172. [PMID: 35782730 PMCID: PMC9234349 DOI: 10.1016/j.csbj.2022.06.035] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive, one for present or active. Because of this approximation, Boolean modelling is applicable to large diagrams, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
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Affiliation(s)
- Ahmed Abdelmonem Hemedan
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde – Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Corresponding author at: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg.
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Noël V, Ruscone M, Stoll G, Viara E, Zinovyev A, Barillot E, Calzone L. WebMaBoSS: A Web Interface for Simulating Boolean Models Stochastically. Front Mol Biosci 2021; 8:754444. [PMID: 34888352 PMCID: PMC8651056 DOI: 10.3389/fmolb.2021.754444] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/20/2021] [Indexed: 12/13/2022] Open
Abstract
WebMaBoSS is an easy-to-use web interface for conversion, storage, simulation and analysis of Boolean models that allows to get insight from these models without any specific knowledge of modeling or coding. It relies on an existing software, MaBoSS, which simulates Boolean models using a stochastic approach: it applies continuous time Markov processes over the Boolean network. It was initially built to fill the gap between Boolean and continuous formalisms, i.e., providing semi-quantitative results using a simple representation with a minimum number of parameters to fit. The goal of WebMaBoSS is to simplify the use and the analysis of Boolean models coping with two main issues: 1) the simulation of Boolean models of intracellular processes with MaBoSS, or any modeling tool, may appear as non-intuitive for non-experts; 2) the simulation of already-published models available in current model databases (e.g., Cell Collective, BioModels) may require some extra steps to ensure compatibility with modeling tools such as MaBoSS. With WebMaBoSS, new models can be created or imported directly from existing databases. They can then be simulated, modified and stored in personal folders. Model simulations are performed easily, results visualized interactively, and figures can be exported in a preferred format. Extensive model analyses such as mutant screening or parameter sensitivity can also be performed. For all these tasks, results are stored and can be subsequently filtered to look for specific outputs. This web interface can be accessed at the address: https://maboss.curie.fr/webmaboss/ and deployed locally using docker. This application is open-source under LGPL license, and available at https://github.com/sysbio-curie/WebMaBoSS.
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Affiliation(s)
- Vincent Noël
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Marco Ruscone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Gautier Stoll
- Equipe 11 labellisée Par la Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, INSERM U1138, Universite de Paris, Sorbonne Universite, Paris, France
| | | | - Andrei Zinovyev
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Paris, France
- INSERM, U900, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
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26
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SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models. Processes (Basel) 2021. [DOI: 10.3390/pr9101830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.
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27
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Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta‐Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frishman G, Monraz Gómez LC, Somers J, Hoch M, Kumar Gupta S, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Ponce de Leon M, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker JE, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex DAB, Slenter D, Martens M, Pham N, Haw R, Jassal B, Matthews L, Orlic‐Milacic M, Senff Ribeiro A, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel J, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, de Waard A, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban‐Medina M, Peña‐Chilet M, Rian K, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, De Meulder B, Ballereau S, Dugourd A, Naldi A, Noël V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Wilighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D'Eustachio P, Saez‐Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, Schneider R. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol 2021; 17:e10387. [PMID: 34664389 PMCID: PMC8524328 DOI: 10.15252/msb.202110387] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 12/13/2022] Open
Abstract
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Anna Niarakis
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
- Lifeware GroupInria Saclay‐Ile de FrancePalaiseauFrance
| | - Alexander Mazein
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Inna Kuperstein
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Robert Phair
- Integrative Bioinformatics, Inc.Mountain ViewCAUSA
| | - Aurelio Orta‐Resendiz
- Institut PasteurUniversité de Paris, Unité HIVInflammation et PersistanceParisFrance
- Bio Sorbonne Paris CitéUniversité de ParisParisFrance
| | - Vidisha Singh
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
| | - Sara Sadat Aghamiri
- Inserm‐ Institut national de la santé et de la recherche médicaleParisFrance
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Andreas Ruepp
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Gisela Fobo
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Corinna Montrone
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Barbara Brauner
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Goar Frishman
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Luis Cristóbal Monraz Gómez
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Julia Somers
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | - Matti Hoch
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | | | - Julia Scheel
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Hanna Borlinghaus
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
| | - Tobias Czauderna
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | - Falk Schreiber
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | | | | | - Akira Funahashi
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Yusuke Hiki
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Noriko Hiroi
- Graduate School of Media and GovernanceResearch Institute at SFCKeio UniversityKanagawaJapan
| | - Takahiro G Yamada
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
- German Center for Infection Research (DZIF), partner siteTübingenGermany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
| | - Muhammad Naveez
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
- Institute of Applied Computer SystemsRiga Technical UniversityRigaLatvia
| | - Zsolt Bocskei
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | - Francesco Messina
- Dipartimento di Epidemiologia Ricerca Pre‐Clinica e Diagnostica AvanzataNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.S.RomeItaly
- COVID‐19 INMI Network Medicine for IDs Study GroupNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.SRomeItaly
| | - Daniela Börnigen
- Bioinformatics Core FacilityUniversitätsklinikum Hamburg‐EppendorfHamburgGermany
| | - Liam Fergusson
- Royal (Dick) School of Veterinary MedicineThe University of EdinburghEdinburghUK
| | - Marta Conti
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Marius Rameil
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Vanessa Nakonecnij
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Jakob Vanhoefer
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Leonard Schmiester
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
- Center for MathematicsChair of Mathematical Modeling of Biological SystemsTechnische Universität MünchenGarchingGermany
| | - Muying Wang
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Emily E Ackerman
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Jason E Shoemaker
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
- Department of Computational and Systems BiologyUniversity of PittsburghPittsburghPAUSA
| | | | | | | | | | | | - Kristina Hanspers
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Martina Kutmon
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Susan Coort
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Lars Eijssen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | | | - Denise Slenter
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Marvin Martens
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Nhung Pham
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Robin Haw
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Bijay Jassal
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | | | - Andrea Senff Ribeiro
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Universidade Federal do ParanáCuritibaBrasil
| | - Karen Rothfels
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | - Ralf Stephan
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Cristoffer Sevilla
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Thawfeek Varusai
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Jean‐Marie Ravel
- INSERM UMR_S 1256Nutrition, Genetics, and Environmental Risk Exposure (NGERE)Faculty of Medicine of NancyUniversity of LorraineNancyFrance
- Laboratoire de génétique médicaleCHRU NancyNancyFrance
| | - Rupsha Fraser
- Queen's Medical Research InstituteThe University of EdinburghEdinburghUK
| | - Vera Ortseifen
- Senior Research Group in Genome Research of Industrial MicroorganismsCenter for BiotechnologyBielefeld UniversityBielefeldGermany
| | - Silvia Marchesi
- Department of Surgical ScienceUppsala UniversityUppsalaSweden
| | - Piotr Gawron
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
- Institute of Computing SciencePoznan University of TechnologyPoznanPoland
| | - Ewa Smula
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Guanming Wu
- Department of Medical Informatics and Clinical EpidemiologyOregon Health & Science UniversityPortlandORUSA
| | - Anders Riutta
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | | | - Stuart Owen
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Carole Goble
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Xiaoming Hu
- Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
| | - Rupert W Overall
- German Center for Neurodegenerative Diseases (DZNE) DresdenDresdenGermany
- Center for Regenerative Therapies Dresden (CRTD)Technische Universität DresdenDresdenGermany
- Institute for BiologyHumboldt University of BerlinBerlinGermany
| | | | | | - Benjamin M Gyori
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - John A Bachman
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - Carlos Vega
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Valentin Grouès
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | | | - Pablo Porras
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Luana Licata
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | - Francesca Sacco
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | | | | | - Denes Turei
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Augustin Luna
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Ozgun Babur
- Computer Science DepartmentUniversity of Massachusetts BostonBostonMAUSA
| | | | - Alberto Valdeolivas
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Marina Esteban‐Medina
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Maria Peña‐Chilet
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
| | - Kinza Rian
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Tomáš Helikar
- Department of BiochemistryUniversity of Nebraska‐LincolnLincolnNEUSA
| | | | - Dezso Modos
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Agatha Treveil
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Marton Olbei
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Stephane Ballereau
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Aurélien Dugourd
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Institute of Experimental Medicine and Systems BiologyFaculty of Medicine, RWTHAachen UniversityAachenGermany
| | | | - Vincent Noël
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Laurence Calzone
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Chris Sander
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Emek Demir
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | | | - Tom C Freeman
- The Roslin InstituteUniversity of EdinburghEdinburghUK
| | - Franck Augé
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | | | - Jan Hasenauer
- Helmholtz Zentrum München – German Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
- Interdisciplinary Research Unit Mathematics and Life SciencesUniversity of BonnBonnGermany
| | - Olaf Wolkenhauer
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Egon L Wilighagen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Alexander R Pico
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Chris T Evelo
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Marc E Gillespie
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- St. John’s University College of Pharmacy and Health SciencesQueensNYUSA
| | - Lincoln D Stein
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Henning Hermjakob
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | | | | | - Joaquin Dopazo
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
- FPS/ELIXIR‐esHospital Virgen del RocíoSevillaSpain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Hiroaki Kitano
- Systems Biology InstituteTokyoJapan
- Okinawa Institute of Science and Technology Graduate SchoolOkinawaJapan
| | - Emmanuel Barillot
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Charles Auffray
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Rudi Balling
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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29
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Shaikh B, Marupilla G, Wilson M, Blinov ML, Moraru II, Karr JR. RunBioSimulations: an extensible web application that simulates a wide range of computational modeling frameworks, algorithms, and formats. Nucleic Acids Res 2021; 49:W597-W602. [PMID: 34019658 PMCID: PMC8262693 DOI: 10.1093/nar/gkab411] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/18/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
Comprehensive, predictive computational models have significant potential for science, bioengineering, and medicine. One promising way to achieve more predictive models is to combine submodels of multiple subsystems. To capture the multiple scales of biology, these submodels will likely require multiple modeling frameworks and simulation algorithms. Several community resources are already available for working with many of these frameworks and algorithms. However, the variety and sheer number of these resources make it challenging to find and use appropriate tools for each model, especially for novice modelers and experimentalists. To make these resources easier to use, we developed RunBioSimulations (https://run.biosimulations.org), a single web application for executing a broad range of models. RunBioSimulations leverages community resources, including BioSimulators, a new open registry of simulation tools. These resources currently enable RunBioSimulations to execute nine frameworks and 44 algorithms, and they make RunBioSimulations extensible to additional frameworks and algorithms. RunBioSimulations also provides features for sharing simulations and interactively visualizing their results. We anticipate that RunBioSimulations will foster reproducibility, stimulate collaboration, and ultimately facilitate the creation of more predictive models.
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Affiliation(s)
- Bilal Shaikh
- Icahn Institute for Data Science & Genomic Technology and Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
| | - Gnaneswara Marupilla
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Mike Wilson
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Michael L Blinov
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Ion I Moraru
- Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, 263 Farmington Avenue, Farmington, CT 06030, USA
| | - Jonathan R Karr
- Icahn Institute for Data Science & Genomic Technology and Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
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30
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Kitano H. Nobel Turing Challenge: creating the engine for scientific discovery. NPJ Syst Biol Appl 2021; 7:29. [PMID: 34145287 PMCID: PMC8213706 DOI: 10.1038/s41540-021-00189-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/03/2021] [Indexed: 12/15/2022] Open
Abstract
Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the "science of science" needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by "AI Scientists" may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.
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Affiliation(s)
- Hiroaki Kitano
- The Systems Biology Institute, Tokyo, Japan; Okinawa Institute of Science and Technology Graduate School, Okinawa, Japan; Sony Computer Science Laboratories, Inc., Tokyo, Japan; Sony AI, Inc., Tokyo, Japan; and The Alan Turing Institute, London, UK.
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31
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Shin W, Hellerstein JL. Isolating structural errors in reaction networks in systems biology. Bioinformatics 2021; 37:388-395. [PMID: 32790862 PMCID: PMC8058775 DOI: 10.1093/bioinformatics/btaa720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 07/10/2020] [Accepted: 08/07/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The growing complexity of reaction-based models necessitates early detection and resolution of model errors. Considerable work has been done on the detection of mass balance errors, especially atomic mass analysis (AMA) (which compares the counts of atoms in the reactants and products) and Linear Programming analysis (which detects stoichiometric inconsistencies). This article extends model error checking to include: (i) certain structural errors in reaction networks and (ii) error isolation. First, we consider the balance of chemical structures (moieties) between reactants and products. This balance is expected in many biochemical reactions, but the imbalance of chemical structures cannot be detected if the analysis is done in units of atomic masses. Second, we improve on error isolation for stoichiometric inconsistencies by identifying a small number of reactions and/or species that cause the error. Doing so simplifies error remediation. RESULTS We propose two algorithms that address isolating structural errors in reaction networks. Moiety analysis finds imbalances of moieties using the same algorithm as AMA, but moiety analysis works in units of moieties instead of atomic masses. We argue for the value of checking moiety balance, and discuss two approaches to decomposing chemical species into moieties. Graphical Analysis of Mass Equivalence Sets (GAMES) provides isolation for stoichiometric inconsistencies by constructing explanations that relate errors in the structure of the reaction network to elements of the reaction network. We study the effectiveness of moiety analysis and GAMES on curated models in the BioModels repository. We have created open source codes for moiety analysis and GAMES. AVAILABILITY AND IMPLEMENTATION Our project is hosted at https://github.com/ModelEngineering/SBMLLint, which contains examples, documentation, source code files and build scripts used to create SBMLLint. Our source code is licensed under the MIT open source license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Woosub Shin
- eScience Institute, University of Washington, Seattle, WA 98195-5061, USA.,Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, 6229 ER Maastricht, The Netherlands
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32
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Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits. Methods Mol Biol 2021; 2229:1-40. [PMID: 33405215 DOI: 10.1007/978-1-0716-1032-9_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Qualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.
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33
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Gjerga E, Trairatphisan P, Gabor A, Koch H, Chevalier C, Ceccarelli F, Dugourd A, Mitsos A, Saez-Rodriguez J. Converting networks to predictive logic models from perturbation signalling data with CellNOpt. Bioinformatics 2021; 36:4523-4524. [PMID: 32516357 PMCID: PMC7575044 DOI: 10.1093/bioinformatics/btaa561] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/26/2020] [Accepted: 06/03/2020] [Indexed: 11/27/2022] Open
Abstract
Summary The molecular changes induced by perturbations such as drugs and ligands are highly informative of the intracellular wiring. Our capacity to generate large datasets is increasing steadily. A useful way to extract mechanistic insight from the data is by integrating them with a prior knowledge network of signalling to obtain dynamic models. CellNOpt is a collection of Bioconductor R packages for building logic models from perturbation data and prior knowledge of signalling networks. We have recently developed new components and refined the existing ones to keep up with the computational demand of increasingly large datasets, including (i) an efficient integer linear programming, (ii) a probabilistic logic implementation for semi-quantitative datasets, (iii) the integration of a stochastic Boolean simulator, (iv) a tool to identify missing links, (v) systematic post-hoc analyses and (vi) an R-Shiny tool to run CellNOpt interactively. Availability and implementation R-package(s): https://github.com/saezlab/cellnopt. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Enio Gjerga
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)
| | - Panuwat Trairatphisan
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany
| | - Attila Gabor
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany
| | - Hermann Koch
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE).,Aachener Verfahrenstechnik, Process Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Celine Chevalier
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE).,University Paris-Saclay, Espace Technologique Bat. Discovery,91190 Saint-Aubin, France
| | - Franceco Ceccarelli
- Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE).,Computer Laboratory, University of Cambridge, Cambridge CB2 1TN, UK
| | - Aurelien Dugourd
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)
| | - Alexander Mitsos
- Aachener Verfahrenstechnik, Process Systems Engineering, RWTH Aachen University, Aachen, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University, Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuant 69120 Heidelberg, Germany.,Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE)
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34
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Park JC, Jang SY, Lee D, Lee J, Kang U, Chang H, Kim HJ, Han SH, Seo J, Choi M, Lee DY, Byun MS, Yi D, Cho KH, Mook-Jung I. A logical network-based drug-screening platform for Alzheimer's disease representing pathological features of human brain organoids. Nat Commun 2021; 12:280. [PMID: 33436582 PMCID: PMC7804132 DOI: 10.1038/s41467-020-20440-5] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/19/2020] [Indexed: 01/29/2023] Open
Abstract
Developing effective drugs for Alzheimer's disease (AD), the most common cause of dementia, has been difficult because of complicated pathogenesis. Here, we report an efficient, network-based drug-screening platform developed by integrating mathematical modeling and the pathological features of AD with human iPSC-derived cerebral organoids (iCOs), including CRISPR-Cas9-edited isogenic lines. We use 1300 organoids from 11 participants to build a high-content screening (HCS) system and test blood-brain barrier-permeable FDA-approved drugs. Our study provides a strategy for precision medicine through the convergence of mathematical modeling and a miniature pathological brain model using iCOs.
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Affiliation(s)
- Jong-Chan Park
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.83440.3b0000000121901201Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG United Kingdom
| | - So-Yeong Jang
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Dongjoon Lee
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Jeongha Lee
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Uiryong Kang
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Hongjun Chang
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Haeng Jun Kim
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Sun-Ho Han
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Jinsoo Seo
- grid.417736.00000 0004 0438 6721Department of Brain and Cognitive Science, Daegu Gyeongbuk Institute of Sciences and Technology (DGIST), Daegu, 42988 Republic of Korea
| | - Murim Choi
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
| | - Dong Young Lee
- grid.31501.360000 0004 0470 5905Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Psychiatry, College of medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.412484.f0000 0001 0302 820XDepartment of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080 Republic of Korea
| | - Min Soo Byun
- grid.412480.b0000 0004 0647 3378Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, 13620 Republic of Korea
| | - Dahyun Yi
- grid.31501.360000 0004 0470 5905Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul, 03080 Republic of Korea
| | - Kwang-Hyun Cho
- grid.37172.300000 0001 2292 0500Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 Republic of Korea
| | - Inhee Mook-Jung
- grid.31501.360000 0004 0470 5905Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea ,grid.31501.360000 0004 0470 5905SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080 Republic of Korea
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35
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Musilova J, Sedlar K. Tools for time-course simulation in systems biology: a brief overview. Brief Bioinform 2021; 22:6076933. [PMID: 33423059 DOI: 10.1093/bib/bbaa392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 11/13/2022] Open
Abstract
Dynamic modeling of biological systems is essential for understanding all properties of a given organism as it allows us to look not only at the static picture of an organism but also at its behavior under various conditions. With the increasing amount of experimental data, the number of tools that enable dynamic analysis also grows. However, various tools are based on different approaches, use different types of data and offer different functions for analyses; so it can be difficult to choose the most suitable tool for a selected type of model. Here, we bring a brief overview containing descriptions of 50 tools for the reconstruction of biological models, their time-course simulation and dynamic analysis. We examined each tool using test data and divided them based on the qualitative and quantitative nature of the mathematical apparatus they use.
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Affiliation(s)
- Jana Musilova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Karel Sedlar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
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36
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Manica M, Polig R, Purandare M, Mathis R, Hagleitner C, Martinez MR. FPGA Accelerated Analysis of Boolean Gene Regulatory Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2141-2147. [PMID: 31494553 DOI: 10.1109/tcbb.2019.2936836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Boolean models are a powerful abstraction for qualitative modeling of gene regulatory networks. With the recent availability of advanced high-throughput technologies, Boolean models have increasingly grown in size and complexity, posing a challenge for existing software simulation tools that have not scaled at the same speed. Field Programmable Gate Arrays (FPGAs) are powerful reconfigurable integrated circuits that can offer massive performance improvements. Due to their highly parallel nature, FPGAs are well suited to simulate complex molecular networks. We present here a new simulation framework for Boolean models, which first converts the model to Verilog, a standardized hardware description language, and then connects it to an execution core that runs on an FPGA coherently attached to a POWER8 processor. We report an order of magnitude speedup over a multi-threaded software simulation tool running on the same processor on a selection of Boolean models. Analysis on a T-cell large granular lymphocyte leukemia (T-LGL) demonstrates that our framework achieves consistent performance improvements resulting in new biological insights. In addition, we show that our solution allows to perform attractor detection at an unprecedented speed, exhibiting a speedup ranging from one to three orders of magnitude compared to alternative software solutions.
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37
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Niarakis A, Helikar T. A practical guide to mechanistic systems modeling in biology using a logic-based approach. Brief Bioinform 2020; 22:5925256. [PMID: 33064138 DOI: 10.1093/bib/bbaa236] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/10/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process-the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.
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Affiliation(s)
- Anna Niarakis
- GenHotel, Univ Evry, University of Paris-Saclay, Genopole, 91025 Evry, France and Lifeware Group, Inria Saclay-île de France, Palaiseau 91120, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
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38
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Fontanarrosa P, Doosthosseini H, Borujeni AE, Dorfan Y, Voigt CA, Myers C. Genetic Circuit Dynamics: Hazard and Glitch Analysis. ACS Synth Biol 2020; 9:2324-2338. [PMID: 32786351 DOI: 10.1021/acssynbio.0c00055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.
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Affiliation(s)
- Pedro Fontanarrosa
- Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Hamid Doosthosseini
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amin Espah Borujeni
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Yuval Dorfan
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Christopher A. Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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39
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A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. Biochem Soc Trans 2020; 48:1889-1903. [DOI: 10.1042/bst20190840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/16/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.
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40
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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41
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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Niarakis A, Kuiper M, Ostaszewski M, Malik Sheriff RS, Casals-Casas C, Thieffry D, Freeman TC, Thomas P, Touré V, Noël V, Stoll G, Saez-Rodriguez J, Naldi A, Oshurko E, Xenarios I, Soliman S, Chaouiya C, Helikar T, Calzone L. Setting the basis of best practices and standards for curation and annotation of logical models in biology-highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop. Brief Bioinform 2020; 22:1848-1859. [PMID: 32313939 DOI: 10.1093/bib/bbaa046] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/20/2020] [Accepted: 03/08/2020] [Indexed: 12/14/2022] Open
Abstract
The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled 'Annotation and curation of computational models in biology', organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.
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Montagud A, Traynard P, Martignetti L, Bonnet E, Barillot E, Zinovyev A, Calzone L. Conceptual and computational framework for logical modelling of biological networks deregulated in diseases. Brief Bioinform 2020; 20:1238-1249. [PMID: 29237040 DOI: 10.1093/bib/bbx163] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 10/24/2017] [Indexed: 01/02/2023] Open
Abstract
Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network, and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline.
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AEON: Attractor Bifurcation Analysis of Parametrised Boolean Networks. COMPUTER AIDED VERIFICATION 2020. [PMCID: PMC7363220 DOI: 10.1007/978-3-030-53288-8_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Boolean networks (BNs) provide an effective modelling tool for various phenomena from science and engineering. Any long-term behaviour of a BN eventually converges to a so-called attractor. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a tool for analysing bifurcations in asynchronous parametrised Boolean networks. To fight the state-space and parameter-space explosion problem the tool uses a parallel semi-symbolic algorithm.
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Kwon M, Yim S, Kim G, Lee S, Jeong C, Lee D. CODA-ML: context-specific biological knowledge representation for systemic physiology analysis. BMC Bioinformatics 2019; 20:248. [PMID: 31138123 PMCID: PMC6538558 DOI: 10.1186/s12859-019-2812-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Computational analysis of complex diseases involving multiple organs requires the integration of multiple different models into a unified model. Different models are often constructed in heterogeneous formats. Thus, the integration of the models requires a standard language format that can effectively represent essential biological information. However, the previously introduced formats have limitations that prevent from adequately representing essential biological information, particularly specifications of bio-molecules and biological contexts. Results We defined an XML-based markup language called context-oriented directed association markup language (CODA-ML), which better represents essential biological information. The CODA-ML has two major strengths in designating molecular specifications and biological contexts. It can cover heterogeneous entity types involved in biological events (e.g. gene/protein, compound, cellular function, disease). Molecular types of entities can have molecular specifications which include detailed information of a molecule from isoforms to modifications, enabling high-resolution representation of molecules. In addition, it can distinguish biological events that vary depending on different biological contexts such as cell types or disease conditions. Especially representation of inter-cellular events as well as intra-cellular events is available. These two major strengths can resolve contradictory associations when different models are integrated into one unified model, which improves the accuracy of the model. Conclusions With the CODA-ML, diverse models such as signaling pathways, metabolic pathways, and gene regulatory pathways can be represented in a unified language format. Heterogeneous entity types can be covered by the CODA-ML, thus it enables detailed description for the mechanisms of diseases or drugs from multiple perspectives (e.g., molecule, function or disease). The CODA-ML is expected to help integrate different models into one systemic model in an efficient and effective. The unified model can be used to perform computational analysis not only for cancer but also for other complex diseases involving multiple organs beyond a single cell. Electronic supplementary material The online version of this article (10.1186/s12859-019-2812-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mijin Kwon
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Soorin Yim
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Saehwan Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Chungsun Jeong
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea. .,Bio-Synergy Research Center, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
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Lemos A, Lynce I, Monteiro PT. Repairing Boolean logical models from time-series data using Answer Set Programming. Algorithms Mol Biol 2019; 14:9. [PMID: 30962813 PMCID: PMC6434824 DOI: 10.1186/s13015-019-0145-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 03/14/2019] [Indexed: 11/20/2022] Open
Abstract
Background Boolean models of biological signalling-regulatory networks are increasingly used to formally describe and understand complex biological processes. These models may become inconsistent as new data become available and need to be repaired. In the past, the focus has been shed on the inference of (classes of) models given an interaction network and time-series data sets. However, repair of existing models against new data is still in its infancy, where the process is still manually performed and therefore slow and prone to errors. Results In this work, we propose a method with an associated tool to suggest repairs over inconsistent Boolean models, based on a set of atomic repair operations. Answer Set Programming is used to encode the minimal repair problem as a combinatorial optimization problem. In particular, given an inconsistent model, the tool provides the minimal repairs that render the model capable of generating dynamics coherent with a (set of) time-series data set(s), considering either a synchronous or an asynchronous updating scheme. Conclusions The method was validated using known biological models from different species, as well as synthetic models obtained from randomly generated networks. We discuss the method’s limitations regarding each of the updating schemes and the considered minimization algorithm. Electronic supplementary material The online version of this article (10.1186/s13015-019-0145-8) contains supplementary material, which is available to authorized users.
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Béal J, Montagud A, Traynard P, Barillot E, Calzone L. Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients. Front Physiol 2019; 9:1965. [PMID: 30733688 PMCID: PMC6353844 DOI: 10.3389/fphys.2018.01965] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 12/31/2018] [Indexed: 12/26/2022] Open
Abstract
Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, Proliferation and Apoptosis, are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians.
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Affiliation(s)
| | | | | | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
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48
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Naldi A. BioLQM: A Java Toolkit for the Manipulation and Conversion of Logical Qualitative Models of Biological Networks. Front Physiol 2018; 9:1605. [PMID: 30510517 PMCID: PMC6254088 DOI: 10.3389/fphys.2018.01605] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 10/25/2018] [Indexed: 12/13/2022] Open
Abstract
Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks. BioLQM provides core modeling operations as building blocks for the development of integrated modeling software, or for the assembly of heterogeneous analysis workflows involving several complementary tools. Based on the definition of multi-valued logical models, bioLQM implements import and export facilities, notably for the recent SBML qual exchange format, as well as for formats used by several popular tools, facilitating the design of workflows combining these tools. Model modifications enable the definition of various perturbations, as well as model reduction, easing the analysis of large models. Another modification enables the study of multi-valued models with tools limited to the Boolean case. Finally, bioLQM provides a framework for the development of novel analysis tools. The current version implements various updating modes for model simulation (notably synchronous, asynchronous, and random asynchronous), as well as some static analysis features for the identification of attractors. The bioLQM software can be integrated into analysis workflows through command line and scripting interfaces. As a Java library, it further provides core data structures to the GINsim and EpiLog interactive tools, which supply graphical interfaces and additional analysis methods for cellular and multi-cellular qualitative models.
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Affiliation(s)
- Aurélien Naldi
- Computational Systems Biology Team, Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, CNRS, INSERM, PSL Université, Paris, France
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Neufeld E, Lloyd B, Schneider B, Kainz W, Kuster N. Functionalized Anatomical Models for Computational Life Sciences. Front Physiol 2018; 9:1594. [PMID: 30505279 PMCID: PMC6250781 DOI: 10.3389/fphys.2018.01594] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 10/24/2018] [Indexed: 11/20/2022] Open
Abstract
The advent of detailed computational anatomical models has opened new avenues for computational life sciences (CLS). To date, static models representing the anatomical environment have been used in many applications but are insufficient when the dynamics of the body prevents separation of anatomical geometrical variability from physics and physiology. Obvious examples include the assessment of thermal risks in magnetic resonance imaging and planning for radiofrequency and acoustic cancer treatment, where posture and physiology-related changes in shape (e.g., breathing) or tissue behavior (e.g., thermoregulation) affect the impact. Advanced functionalized anatomical models can overcome these limitations and dramatically broaden the applicability of CLS in basic research, the development of novel devices/therapies, and the assessment of their safety and efficacy. Various forms of functionalization are discussed in this paper: (i) shape parametrization (e.g., heartbeat, population variability), (ii) physical property distributions (e.g., image-based inhomogeneity), (iii) physiological dynamics (e.g., tissue and organ behavior), and (iv) integration of simulation/measurement data (e.g., exposure conditions, “validation evidence” supporting model tuning and validation). Although current model functionalization may only represent a small part of the physiology, it already facilitates the next level of realism by (i) driving consistency among anatomy and different functionalization layers and highlighting dependencies, (ii) enabling third-party use of validated functionalization layers as established simulation tools, and (iii) therefore facilitating their application as building blocks in network or multi-scale computational models. Integration in functionalized anatomical models thus leverages and potentiates the value of sub-models and simulation/measurement data toward ever-increasing simulation realism. In our o2S2PARC platform, we propose to expand the concept of functionalized anatomical models to establish an integration and sharing service for heterogeneous computational models, ranging from the molecular to the organ level. The objective of o2S2PARC is to integrate all models developed within the National Institutes of Health SPARC initiative in a unified anatomical and computational environment, to study the role of the peripheral nervous system in controlling organ physiology. The functionalization concept, as outlined for the o2S2PARC platform, could form the basis for many other application areas of CLS. The relationship to other ongoing initiatives, such as the Physiome Project, is also presented.
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Affiliation(s)
- Esra Neufeld
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | - Bryn Lloyd
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | | | - Wolfgang Kainz
- Division of Biomedical Physics, OSEL, CDRH, Food and Drug Administration, Silver Spring, MD, United States
| | - Niels Kuster
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland.,Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland
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50
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Bekkar A, Estreicher A, Niknejad A, Casals-Casas C, Bridge A, Xenarios I, Dorier J, Crespo I. Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4960931. [PMID: 29688381 PMCID: PMC5887269 DOI: 10.1093/database/bay031] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 03/07/2018] [Indexed: 12/13/2022]
Abstract
Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. Database URL: http://biomodels.caltech.edu
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Affiliation(s)
- Amel Bekkar
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
| | - Anne Estreicher
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland
| | - Anne Niknejad
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland.,Swiss-Prot group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland
| | - Cristina Casals-Casas
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland
| | - Alan Bridge
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland
| | - Ioannis Xenarios
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland.,Swiss-Prot group, SIB Swiss Institute of Bioinformatics, 1 Michel Servet, 1211 Geneva 4, Switzerland
| | - Julien Dorier
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
| | - Isaac Crespo
- Vital-IT group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, 1015 Lausanne, Switzerland
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