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Maizels RJ. A dynamical perspective: moving towards mechanism in single-cell transcriptomics. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230049. [PMID: 38432314 PMCID: PMC10909508 DOI: 10.1098/rstb.2023.0049] [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: 07/10/2023] [Accepted: 10/31/2023] [Indexed: 03/05/2024] Open
Abstract
As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Rory J. Maizels
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- University College London, London WC1E 6BT, UK
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2
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Ershova A, Minev D, Corea-Dilbert FE, Yu D, Deng J, Fontana W, Shih WM. Enzyme-Free Exponential Amplification via Growth and Scission of Crisscross Ribbons from Single-Stranded DNA Components. J Am Chem Soc 2024; 146:218-227. [PMID: 38133996 PMCID: PMC10785819 DOI: 10.1021/jacs.3c08205] [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: 07/30/2023] [Revised: 12/03/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
The self-assembly of DNA-based monomers into higher-order structures has significant potential for realizing various biomimetic behaviors including algorithmic assembly, ultrasensitive detection, and self-replication. For these behaviors, it is desirable to implement high energetic barriers to undesired spurious nucleation, where such barriers can be bypassed via seed-initiated assembly. Joint-neighbor capture is a mechanism enabling the construction of such barriers while allowing for algorithmic behaviors, such as bit-copying. Cycles of polymerization with division could accordingly be used for implementing exponential growth in self-replicating materials. Previously, we demonstrated crisscross polymerization, a strategy that attains robust seed-dependent self-assembly of single-stranded DNA and DNA-origami monomers via joint-neighbor capture. Here, we expand the crisscross assembly to achieve autonomous, isothermal exponential amplification of ribbons through their concurrent growth and scission via toehold-mediated strand displacement. We demonstrate how this crisscross chain reaction, or 3CR, can be used as a detection strategy through coupling to single- and double-stranded nucleic acid targets and introduce a rule-based stochastic modeling approach for simulating molecular self-assembly behaviors such as crisscross-ribbon scission.
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Affiliation(s)
- Anastasia Ershova
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Dionis Minev
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - F. Eduardo Corea-Dilbert
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
| | - Devon Yu
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
| | - Jie Deng
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Walter Fontana
- Department
of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - William M. Shih
- Department
of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
- Wyss
Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, United States
- Department
of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, United States
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3
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Lakin MR. Design and Simulation of a Multilayer Chemical Neural Network That Learns via Backpropagation. ARTIFICIAL LIFE 2023; 29:308-335. [PMID: 37141578 DOI: 10.1162/artl_a_00405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
The design and implementation of adaptive chemical reaction networks, capable of adjusting their behavior over time in response to experience, is a key goal for the fields of molecular computing and DNA nanotechnology. Mainstream machine learning research offers powerful tools for implementing learning behavior that could one day be realized in a wet chemistry system. Here we develop an abstract chemical reaction network model that implements the backpropagation learning algorithm for a feedforward neural network whose nodes employ the nonlinear "leaky rectified linear unit" transfer function. Our network directly implements the mathematics behind this well-studied learning algorithm, and we demonstrate its capabilities by training the system to learn a linearly inseparable decision surface, specifically, the XOR logic function. We show that this simulation quantitatively follows the definition of the underlying algorithm. To implement this system, we also report ProBioSim, a simulator that enables arbitrary training protocols for simulated chemical reaction networks to be straightforwardly defined using constructs from the host programming language. This work thus provides new insight into the capabilities of learning chemical reaction networks and also develops new computational tools to simulate their behavior, which could be applied in the design and implementations of adaptive artificial life.
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Affiliation(s)
- Matthew R Lakin
- University of New Mexico, Department of Computer Science, Department of Chemical and Biological Engineering, Center for Biomedical Engineering.
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4
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Modeling and formal analysis of meta-ecosystems with dynamic structure using graph transformation. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2022.101908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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5
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Fröhlich F, Gerosa L, Muhlich J, Sorger PK. Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance. Mol Syst Biol 2023; 19:e10988. [PMID: 36700386 PMCID: PMC9912026 DOI: 10.15252/msb.202210988] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 11/29/2022] [Accepted: 12/15/2022] [Indexed: 01/27/2023] Open
Abstract
BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAFV600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAFV600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAFV600E -driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.
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Affiliation(s)
- Fabian Fröhlich
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
| | - Luca Gerosa
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA,Present address:
Genentech, Inc.South San FranciscoCAUSA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems BiologyHarvard Medical SchoolBostonMAUSA
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6
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Fröhlich F. A Practical Guide for the Efficient Formulation and Calibration of Large, Energy- and Rule-Based Models of Cellular Signal Transduction. Methods Mol Biol 2023; 2634:59-86. [PMID: 37074574 DOI: 10.1007/978-1-0716-3008-2_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Aberrant signal transduction leads to complex diseases such as cancer. To rationally design treatment strategies with small molecule inhibitors, computational models have to be employed. Energy- and rule-based models allow the construction of mechanistic ordinary differential equation models based on structural insights. The detailed, energy-based description often generates large models, which are difficult to calibrate on experimental data. In this chapter, we provide a detailed, interactive protocol for the programmatic formulation and calibration of such large, energy- and rule-based models of cellular signal transduction based on an example model describing the action of RAF inhibitors on MAPK signaling. An interactive version of this chapter is available as Jupyter Notebook at github.com/FFroehlich/energy_modeling_chapter .
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Affiliation(s)
- Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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7
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Wysocka EM, Page M, Snowden J, Simpson TI. Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network. PeerJ 2022; 10:e14516. [PMID: 36540795 PMCID: PMC9760030 DOI: 10.7717/peerj.14516] [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: 04/26/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.
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Affiliation(s)
- Emilia M. Wysocka
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | | | | | - T. Ian Simpson
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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8
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Waites W, Cavaliere M, Danos V, Datta R, Eggo RM, Hallett TB, Manheim D, Panovska-Griffiths J, Russell TW, Zarnitsyna VI. Compositional modelling of immune response and virus transmission dynamics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210307. [PMID: 35965463 PMCID: PMC9376723 DOI: 10.1098/rsta.2021.0307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - M. Cavaliere
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - V. Danos
- Département d’Informatique, École Normale Supérieure, Paris, France
| | - R. Datta
- Datta Enterprises LLC, San Francisco, CA, USA
| | - R. M. Eggo
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - T. B. Hallett
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - D. Manheim
- Technion, Israel Institute of Technology, Haifa, Israel
| | - J. Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen’s College, University of Oxford, Oxford, UK
| | - T. W. Russell
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - V. I. Zarnitsyna
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
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9
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Eriksson O, Bhalla US, Blackwell KT, Crook SM, Keller D, Kramer A, Linne ML, Saudargienė A, Wade RC, Hellgren Kotaleski J. Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows. eLife 2022; 11:e69013. [PMID: 35792600 PMCID: PMC9259018 DOI: 10.7554/elife.69013] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/13/2022] [Indexed: 12/22/2022] Open
Abstract
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.
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Affiliation(s)
- Olivia Eriksson
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
| | - Upinder Singh Bhalla
- National Center for Biological Sciences, Tata Institute of Fundamental ResearchBangaloreIndia
| | - Kim T Blackwell
- Department of Bioengineering, Volgenau School of Engineering, George Mason UniversityFairfaxUnited States
| | - Sharon M Crook
- School of Mathematical and Statistical Sciences, Arizona State UniversityTempeUnited States
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrei Kramer
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere UniversityTampereFinland
| | - Ausra Saudargienė
- Neuroscience Institute, Lithuanian University of Health SciencesKaunasLithuania
- Department of Informatics, Vytautas Magnus UniversityKaunasLithuania
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
- Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of HeidelbergHeidelbergGermany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg UniversityHeidelbergGermany
| | - Jeanette Hellgren Kotaleski
- Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of TechnologyStockholmSweden
- Department of Neuroscience, Karolinska InstituteStockholmSweden
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10
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Erdem C, Mutsuddy A, Bensman EM, Dodd WB, Saint-Antoine MM, Bouhaddou M, Blake RC, Gross SM, Heiser LM, Feltus FA, Birtwistle MR. A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling. Nat Commun 2022; 13:3555. [PMID: 35729113 PMCID: PMC9213456 DOI: 10.1038/s41467-022-31138-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 06/07/2022] [Indexed: 02/01/2023] Open
Abstract
Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.
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Affiliation(s)
- Cemal Erdem
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
| | - Arnab Mutsuddy
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Ethan M Bensman
- Computer Science, School of Computing, Clemson University, Clemson, SC, USA
| | - William B Dodd
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Michael M Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA
| | - Mehdi Bouhaddou
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA
| | - Robert C Blake
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
- Biomedical Data Science and Informatics Program, Clemson University, Clemson, SC, USA
- Center for Human Genetics, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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11
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Shahidi N, Pan M, Tran K, Crampin EJ, Nickerson DP. A semantics, energy-based approach to automate biomodel composition. PLoS One 2022; 17:e0269497. [PMID: 35657966 PMCID: PMC9165793 DOI: 10.1371/journal.pone.0269497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/20/2022] [Indexed: 11/19/2022] Open
Abstract
Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model's components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.
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Affiliation(s)
- Niloofar Shahidi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Victoria, Australia
| | - Kenneth Tran
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Victoria, Australia
- School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - David P. Nickerson
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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12
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Guimapi RA, Niassy S, Mudereri BT, Abdel-Rahman EM, Tepa-Yotto GT, Subramanian S, Mohamed SA, Thunes KH, Kimathi E, Agboka KM, Tamò M, Rwaburindi JC, Hadi B, Elkahky M, Sæthre MG, Belayneh Y, Ekesi S, Kelemu S, Tonnang HE. Harnessing data science to improve integrated management of invasive pest species across Africa: An application to Fall armyworm (Spodoptera frugiperda) (J.E. Smith) (Lepidoptera: Noctuidae). Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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13
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Probabilistic Inference with Polymerizing Biochemical Circuits. ENTROPY 2022; 24:e24050629. [PMID: 35626513 PMCID: PMC9140500 DOI: 10.3390/e24050629] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 03/24/2022] [Accepted: 04/26/2022] [Indexed: 02/01/2023]
Abstract
Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially “predictive” behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.
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14
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Data-driven learning how oncogenic gene expression locally alters heterocellular networks. Nat Commun 2022; 13:1986. [PMID: 35418177 PMCID: PMC9007999 DOI: 10.1038/s41467-022-29636-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/22/2022] [Indexed: 11/21/2022] Open
Abstract
Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results. While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.
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15
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Fitzpatrick R, Stefan MI. Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways. Neuroinformatics 2022; 20:277-284. [PMID: 35543917 PMCID: PMC9537119 DOI: 10.1007/s12021-022-09584-5] [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] [Accepted: 03/17/2022] [Indexed: 01/09/2023]
Abstract
Computational modelling of biochemical reaction pathways is an increasingly important part of neuroscience research. In order to be useful, computational models need to be valid in two senses: First, they need to be consistent with experimental data and able to make testable predictions (external validity). Second, they need to be internally consistent and independently reproducible (internal validity). Here, we discuss both types of validity and provide a brief overview of tools and technologies used to ensure they are met. We also suggest the introduction of new collaborative technologies to ensure model validity: an incentivised experimental database for external validity and reproducibility audits for internal validity. Both rely on FAIR principles and on collaborative science practices.
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Affiliation(s)
- Richard Fitzpatrick
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK ,School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Melanie I. Stefan
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK ,ZJU-UoE Institute, Zhejiang University, Haining, China
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16
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Waites W, Cavaliere M, Manheim D, Panovska-Griffiths J, Danos V. Rule-based epidemic models. J Theor Biol 2021; 530:110851. [PMID: 34343578 DOI: 10.1016/j.jtbi.2021.110851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 07/19/2021] [Accepted: 07/26/2021] [Indexed: 10/20/2022]
Abstract
Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. An important class of system that would be intractable if expressed as reactions or ordinary differential equations can be efficiently simulated when expressed as rules. In this paper we demonstrate the utility of the rule-based approach for epidemiological modelling presenting a suite of seven models illustrating the spread of infectious disease under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. Rule-based models allow to combine transparent modelling approach with scalability and compositionality and therefore can facilitate the study of aspects of infectious disease propagation in a richer context than would otherwise be feasible.
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Affiliation(s)
- W Waites
- School of Informatics, University of Edinburgh, Edinburgh, UK; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - M Cavaliere
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - D Manheim
- University of Haifa Health and Risk Communication Research Center, Haifa, Israel
| | - J Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Institute for Global Health, University College London, London, UK; The Queen's College, University of Oxford, Oxford, UK
| | - V Danos
- School of Informatics, University of Edinburgh, Edinburgh, UK; Département d'Informatique, École Normale Supérieure, Paris, France
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17
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Lubbock AL, Lopez CF. Programmatic modeling for biological systems. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 27:100343. [PMID: 34485764 PMCID: PMC8411905 DOI: 10.1016/j.coisb.2021.05.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with community standards used for interchange. Models undergo steady state or dynamic analysis, which can include simulation and calibration within a single application, or transfer across various tools. Here, we describe a novel programmatic modeling paradigm, whereby modeling is augmented with software engineering best practices. We focus on Python - a popular programming language with a large scientific package ecosystem. Models can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators, while still being extensible and exportable to standardized formats for use with external tools if desired. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility.
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Affiliation(s)
- Alexander L.R. Lubbock
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37212, United States of America
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville Tennessee 37212, United States of America
| | - Carlos F. Lopez
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37212, United States of America
- Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville Tennessee 37212, United States of America
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37212, United States of America
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18
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Lauber N, Flamm C, Ruiz-Mirazo K. "Minimal metabolism": A key concept to investigate the origins and nature of biological systems. Bioessays 2021; 43:e2100103. [PMID: 34426986 DOI: 10.1002/bies.202100103] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/18/2021] [Accepted: 07/20/2021] [Indexed: 11/07/2022]
Abstract
The systems view on life and its emergence from complex chemistry has remarkably increased the scientific attention on metabolism in the last two decades. However, during this time there has not been much theoretical discussion on what constitutes a metabolism and what role it actually played in biogenesis. A critical and updated review on the topic is here offered, including some references to classical models from last century, but focusing more on current and future research. Metabolism is considered as intrinsically related to the living but not necessarily equivalent to it. More precisely, the idea of "minimal metabolism", in contrast to previous, top-down conceptions, is formulated as a heuristic construct, halfway between chemistry and biology. Thus, rather than providing a complete or final characterization of metabolism, our aim is to encourage further investigations on it, particularly in the context of life's origin, for which some concrete methodological suggestions are provided. Also see the video abstract here: https://youtu.be/DP7VMKk2qpA.
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Affiliation(s)
- Nino Lauber
- Biofisika Institute (CSIC, UPV/EHU), University of the Basque Country, Leioa, Spain.,Department of Philosophy, University of the Basque Country, Leioa, Spain
| | - Christoph Flamm
- Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Kepa Ruiz-Mirazo
- Biofisika Institute (CSIC, UPV/EHU), University of the Basque Country, Leioa, Spain.,Department of Philosophy, University of the Basque Country, Leioa, Spain
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19
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Budde K, Smith J, Wilsdorf P, Haack F, Uhrmacher AM. Relating simulation studies by provenance-Developing a family of Wnt signaling models. PLoS Comput Biol 2021; 17:e1009227. [PMID: 34351901 PMCID: PMC8407594 DOI: 10.1371/journal.pcbi.1009227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/31/2021] [Accepted: 06/29/2021] [Indexed: 12/28/2022] Open
Abstract
For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models. We revise a provenance ontology for simulation studies of cellular biochemical models. Provenance information is useful for understanding the creation of a simulation model because it not only contains information about the entities and activities that have led to a simulation model but also their relations, all of which can be visualized. It provides additional structure by explicitly recording research questions, assumptions, and requirements and relating them along with data, qualitative models, simulation models, and simulation experiments through a small set of predefined but extensible activities. We have applied our concept to a family of 19 Wnt signaling models and implemented a web-based tool (WebProv) to store the provenance information from these studies. The resulting provenance graph visualizes the story line of simulation studies and demonstrates the creation and calibration of simulation models, the successive attempts of validation and extension, and shows, beyond an individual simulation study, how the Wnt models are related. Thereby, the steps and sources that contributed to a simulation model are made explicit. Our approach complements other approaches aimed at facilitating the reuse and assessment of simulation products in systems biology such as model repositories as well as annotation and documentation guidelines.
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Affiliation(s)
- Kai Budde
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
- * E-mail:
| | - Jacob Smith
- Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
| | - Pia Wilsdorf
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Fiete Haack
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Adelinde M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
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20
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Fröhlich F, Weindl D, Schälte Y, Pathirana D, Paszkowski Ł, Lines GT, Stapor P, Hasenauer J. AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics 2021; 37:3676-3677. [PMID: 33821950 PMCID: PMC8545331 DOI: 10.1093/bioinformatics/btab227] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/18/2021] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Summary Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. Availabilityand implementation AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
| | | | | | - Paul Stapor
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany.,Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
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21
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Santibáñez R, Garrido D, Martin AJM. Atlas: automatic modeling of regulation of bacterial gene expression and metabolism using rule-based languages. Bioinformatics 2021; 36:5473-5480. [PMID: 33367504 PMCID: PMC8016457 DOI: 10.1093/bioinformatics/btaa1040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 11/19/2020] [Accepted: 12/12/2020] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Cells are complex systems composed of hundreds of genes whose products interact to produce elaborated behaviors. To control such behaviors, cells rely on transcription factors to regulate gene expression, and gene regulatory networks (GRNs) are employed to describe and understand such behavior. However, GRNs are static models, and dynamic models are difficult to obtain due to their size, complexity, stochastic dynamics and interactions with other cell processes. RESULTS We developed Atlas, a Python software that converts genome graphs and gene regulatory, interaction and metabolic networks into dynamic models. The software employs these biological networks to write rule-based models for the PySB framework. The underlying method is a divide-and-conquer strategy to obtain sub-models and combine them later into an ensemble model. To exemplify the utility of Atlas, we used networks of varying size and complexity of Escherichia coli and evaluated in silico modifications, such as gene knockouts and the insertion of promoters and terminators. Moreover, the methodology could be applied to the dynamic modeling of natural and synthetic networks of any bacteria. AVAILABILITY AND IMPLEMENTATION Code, models and tutorials are available online (https://github.com/networkbiolab/atlas). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rodrigo Santibáñez
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago 8580745, Chile
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Alberto J M Martin
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Universidad Mayor, Santiago 8580745, Chile
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22
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Nguyen N, Strnad O, Klein T, Luo D, Alharbi R, Wonka P, Maritan M, Mindek P, Autin L, Goodsell DS, Viola I. Modeling in the Time of COVID-19: Statistical and Rule-based Mesoscale Models. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:722-732. [PMID: 33055034 PMCID: PMC8642830 DOI: 10.1109/tvcg.2020.3030415] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/31/2020] [Accepted: 08/14/2020] [Indexed: 06/11/2023]
Abstract
We present a new technique for the rapid modeling and construction of scientifically accurate mesoscale biological models. The resulting 3D models are based on a few 2D microscopy scans and the latest knowledge available about the biological entity, represented as a set of geometric relationships. Our new visual-programming technique is based on statistical and rule-based modeling approaches that are rapid to author, fast to construct, and easy to revise. From a few 2D microscopy scans, we determine the statistical properties of various structural aspects, such as the outer membrane shape, the spatial properties, and the distribution characteristics of the macromolecular elements on the membrane. This information is utilized in the construction of the 3D model. Once all the imaging evidence is incorporated into the model, additional information can be incorporated by interactively defining the rules that spatially characterize the rest of the biological entity, such as mutual interactions among macromolecules, and their distances and orientations relative to other structures. These rules are defined through an intuitive 3D interactive visualization as a visual-programming feedback loop. We demonstrate the applicability of our approach on a use case of the modeling procedure of the SARS-CoV-2 virion ultrastructure. This atomistic model, which we present here, can steer biological research to new promising directions in our efforts to fight the spread of the virus.
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Affiliation(s)
- Ngan Nguyen
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Ondřej Strnad
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | | | - Deng Luo
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Ruwayda Alharbi
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | - Peter Wonka
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
| | | | | | | | | | - Ivan Viola
- King Abdullah University of Science and Technology (KAUST)Saudi Arabia
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23
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24
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Johnson ME, Chen A, Faeder JR, Henning P, Moraru II, Meier-Schellersheim M, Murphy RF, Prüstel T, Theriot JA, Uhrmacher AM. Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry. Mol Biol Cell 2021; 32:186-210. [PMID: 33237849 PMCID: PMC8120688 DOI: 10.1091/mbc.e20-08-0530] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/13/2020] [Accepted: 11/17/2020] [Indexed: 12/29/2022] Open
Abstract
Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multimolecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases, we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.
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Affiliation(s)
- M. E. Johnson
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - A. Chen
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218
| | - J. R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260
| | - P. Henning
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
| | - I. I. Moraru
- Department of Cell Biology, Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030
| | - M. Meier-Schellersheim
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - R. F. Murphy
- Computational Biology Department, Department of Biological Sciences, Department of Biomedical Engineering, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15289
| | - T. Prüstel
- Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892
| | - J. A. Theriot
- Department of Biology and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195
| | - A. M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, 18055 Rostock, Germany
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25
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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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26
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Lasky JR, Hooten MB, Adler PB. What processes must we understand to forecast regional-scale population dynamics? Proc Biol Sci 2020; 287:20202219. [PMID: 33290672 PMCID: PMC7739927 DOI: 10.1098/rspb.2020.2219] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 11/12/2020] [Indexed: 12/14/2022] Open
Abstract
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
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Affiliation(s)
- Jesse R. Lasky
- Department of Biology, Pennsylvania State University, University Park, PA, USA
| | - Mevin B. Hooten
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, USA
- Department of Statistics, Colorado State University, Fort Collins, CO, USA
| | - Peter B. Adler
- Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA
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27
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Hernandez C, Thomas-Chollier M, Naldi A, Thieffry D. Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors. Front Physiol 2020; 11:558606. [PMID: 33101049 PMCID: PMC7554341 DOI: 10.3389/fphys.2020.558606] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Accepted: 08/19/2020] [Indexed: 12/31/2022] Open
Abstract
At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalization and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation," enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behavior of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.
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Affiliation(s)
- Céline Hernandez
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| | - Morgane Thomas-Chollier
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France.,Institut Universitaire de France, Paris, France
| | - Aurélien Naldi
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
| | - Denis Thieffry
- Institut de Biologie de l'ENS (IBENS), Département de Biologie, École Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France
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28
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Troják M, Šafránek D, Mertová L, Brim L. Executable biochemical space for specification and analysis of biochemical systems. PLoS One 2020; 15:e0238838. [PMID: 32915842 PMCID: PMC7485897 DOI: 10.1371/journal.pone.0238838] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 08/25/2020] [Indexed: 01/28/2023] Open
Abstract
Computational systems biology provides multiple formalisms for modelling of biochemical processes among which the rule-based approach is one of the most suitable. Its main advantage is a compact and precise mechanistic description of complex processes. However, state-of-the-art rule-based languages still suffer several shortcomings that limit their use in practice. In particular, the elementary (low-level) syntax and semantics of rule-based languages complicate model construction and maintenance for users outside computer science. On the other hand, mathematical models based on differential equations (ODEs) still make the most typical used modelling framework. In consequence, robust re-interpretation and integration of models are difficult, thus making the systems biology paradigm technically challenging. Though several high-level languages have been developed at the top of rule-based principles, none of them provides a satisfactory and complete solution for semi-automated description and annotation of heterogeneous biophysical processes integrated at the cellular level. We present the second generation of a rule-based language called Biochemical Space Language (BCSL) that combines the advantages of different approaches and thus makes an effort to overcome several problems of existing solutions. BCSL relies on the formal basis of the rule-based methodology while preserving user-friendly syntax of plain chemical equations. BCSL combines the following aspects: the level of abstraction that hides structural and quantitative details but yet gives a precise mechanistic view of systems dynamics; executable semantics allowing formal analysis and consistency checking at the level of the language; universality allowing the integration of different biochemical mechanisms; scalability and compactness of the specification; hierarchical specification and composability of chemical entities; and support for genome-scale annotation.
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Affiliation(s)
- Matej Troják
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - David Šafránek
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Lukrécia Mertová
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
| | - Luboš Brim
- Systems Biology Laboratory, Masaryk University, Brno, Czech Republic
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29
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Haack F, Budde K, Uhrmacher AM. Exploring the mechanistic and temporal regulation of LRP6 endocytosis in canonical WNT signaling. J Cell Sci 2020; 133:jcs243675. [PMID: 32661084 DOI: 10.1242/jcs.243675] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 07/03/2020] [Indexed: 12/17/2022] Open
Abstract
Endocytosis plays a pivotal regulatory role in canonical WNT signaling. Internalization of the low-density lipoprotein receptor-related protein 6 (LRP6) receptor complex can either promote or attenuate canonical WNT signaling, depending on the employed internalization pathway. Detailed analysis of the mechanism of LRP6 internalization and its temporal regulation is crucial for understanding the different cellular responses to WNT stimulation under varying conditions and in various cell types. Here, we elucidate the mechanisms involved in the internalization of LRP6 and re-evaluate existing, partly contradicting, theories on the regulation of LRP6 receptor internalization. We utilize a computational approach that aims at finding a set of mechanisms that accounts for the temporal dynamics of LRP6 receptor internalization upon WNT stimulation. Starting with a simple simulation model, we successively extend and probe the model's behavior based on quantitative measurements. The final model confirms that LRP6 internalization is clathrin independent in vertebrates, is not restricted to microdomains, and that signalosome formation delays LRP6 internalization within the microdomains. These findings partly revise the current understanding of LRP6 internalization in vertebrates.
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Affiliation(s)
- Fiete Haack
- Institute for Visual and Analytic Computing, Modeling and Simulation Group, Albert-Einstein-Str. 22, 18051 Rostock, Germany
| | - Kai Budde
- Institute for Visual and Analytic Computing, Modeling and Simulation Group, Albert-Einstein-Str. 22, 18051 Rostock, Germany
| | - Adelinde M Uhrmacher
- Institute for Visual and Analytic Computing, Modeling and Simulation Group, Albert-Einstein-Str. 22, 18051 Rostock, Germany
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30
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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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31
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Lakin MR, Phillips A. Domain-Specific Programming Languages for Computational Nucleic Acid Systems. ACS Synth Biol 2020; 9:1499-1513. [PMID: 32589838 DOI: 10.1021/acssynbio.0c00050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The construction of models of system behavior is of great importance throughout science and engineering. In bioengineering and bionanotechnology, these often take the form of dynamic models that specify the evolution of different species over time. To ensure that scientific observations and conclusions are consistent and that systems can be reliably engineered on the basis of model predictions, it is important that models of biomolecular systems can be constructed in a reliable, principled, and efficient manner. This review focuses on efforts to address this need by using domain-specific programming languages as the basis for custom design tools for researchers working on computational nucleic acid devices, where a domain-specific language is simply a programming language tailored to a particular application domain. The underlying thesis of our review is that there is a continuum of practical implementation strategies for computational nucleic acid systems, which can all benefit from appropriate domain-specific languages and software design tools. We emphasize the need for specialized yet flexible tools that can be realized using domain-specific languages that compile to more general-purpose representations.
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Affiliation(s)
- Matthew R. Lakin
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Department of Chemical & Biological Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
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32
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Varga MJ, Fu Y, Loggia S, Yogurtcu ON, Johnson ME. NERDSS: A Nonequilibrium Simulator for Multibody Self-Assembly at the Cellular Scale. Biophys J 2020; 118:3026-3040. [PMID: 32470324 DOI: 10.1016/j.bpj.2020.05.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 04/24/2020] [Accepted: 05/05/2020] [Indexed: 12/13/2022] Open
Abstract
Currently, a significant barrier to building predictive models of cellular self-assembly processes is that molecular models cannot capture minutes-long dynamics that couple distinct components with active processes, whereas reaction-diffusion models cannot capture structures of molecular assembly. Here, we introduce the nonequilibrium reaction-diffusion self-assembly simulator (NERDSS), which addresses this spatiotemporal resolution gap. NERDSS integrates efficient reaction-diffusion algorithms into generalized software that operates on user-defined molecules through diffusion, binding and orientation, unbinding, chemical transformations, and spatial localization. By connecting the fast processes of binding with the slow timescales of large-scale assembly, NERDSS integrates molecular resolution with reversible formation of ordered, multisubunit complexes. NERDSS encodes models using rule-based formatting languages to facilitate model portability, usability, and reproducibility. Applying NERDSS to steps in clathrin-mediated endocytosis, we design multicomponent systems that can form lattices in solution or on the membrane, and we predict how stochastic but localized dephosphorylation of membrane lipids can drive lattice disassembly. The NERDSS simulations reveal the spatial constraints on lattice growth and the role of membrane localization and cooperativity in nucleating assembly. By modeling viral lattice assembly and recapitulating oscillations in protein expression levels for a circadian clock model, we illustrate the adaptability of NERDSS. NERDSS simulates user-defined assembly models that were previously inaccessible to existing software tools, with broad applications to predicting self-assembly in vivo and designing high-yield assemblies in vitro.
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Affiliation(s)
- Matthew J Varga
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Yiben Fu
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Spencer Loggia
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Osman N Yogurtcu
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Margaret E Johnson
- TC Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland.
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33
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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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Boemo MA, Cardelli L, Nieduszynski CA. The Beacon Calculus: A formal method for the flexible and concise modelling of biological systems. PLoS Comput Biol 2020; 16:e1007651. [PMID: 32150540 PMCID: PMC7082070 DOI: 10.1371/journal.pcbi.1007651] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 03/19/2020] [Accepted: 01/10/2020] [Indexed: 01/08/2023] Open
Abstract
Biological systems are made up of components that change their actions (and interactions) over time and coordinate with other components nearby. Together with a large state space, the complexity of this behaviour can make it difficult to create concise mathematical models that can be easily extended or modified. This paper introduces the Beacon Calculus, a process algebra designed to simplify the task of modelling interacting biological components. Its breadth is demonstrated by creating models of DNA replication dynamics, the gene expression dynamics in response to DNA methylation damage, and a multisite phosphorylation switch. The flexibility of these models is shown by adapting the DNA replication model to further include two topics of interest from the literature: cooperative origin firing and replication fork barriers. The Beacon Calculus is supported with the open-source simulator bcs (https://github.com/MBoemo/bcs.git) to allow users to develop and simulate their own models. Simulating a model of a biological system can suggest ideas for future experiments and help ensure that conclusions about a mechanism are consistent with data. The Beacon Calculus is a new language that makes modelling simple by allowing users to simulate a biological system in only a few lines of code. This simplicity is critical as it allows users the freedom to come up with new ideas and rapidly test them. Models written in the Beacon Calculus are also easy to modify and extend, allowing users to add new features to the model or incorporate it into a larger biological system. We demonstrate the breadth of applications in this paper by applying the Beacon Calculus to DNA replication and DNA damage repair, both of which have implications for genome stability and cancer. We also apply it to multisite phosphorylation, which is important for cellular signalling. To enable users to create their own models, we created the open-source Beacon Calculus simulator bcs (https://github.com/MBoemo/bcs.git) which is easy to install and is well-supported by documentation and examples.
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Affiliation(s)
- Michael A. Boemo
- Department of Pathology, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| | - Luca Cardelli
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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35
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Ewald J, Sieber P, Garde R, Lang SN, Schuster S, Ibrahim B. Trends in mathematical modeling of host-pathogen interactions. Cell Mol Life Sci 2020; 77:467-480. [PMID: 31776589 PMCID: PMC7010650 DOI: 10.1007/s00018-019-03382-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 11/05/2019] [Accepted: 11/12/2019] [Indexed: 12/18/2022]
Abstract
Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.
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Affiliation(s)
- Jan Ewald
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Patricia Sieber
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Ravindra Garde
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
- Max Planck Institute for Chemical Ecology, Hans-Knöll-Str. 8, 07745, Jena, Germany
| | - Stefan N Lang
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Stefan Schuster
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
| | - Bashar Ibrahim
- Matthias Schleiden Institute, Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
- Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, 32093, Hawally, Kuwait.
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36
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Behr N, Krivine J. Rewriting Theory for the Life Sciences: A Unifying Theory of CTMC Semantics. GRAPH TRANSFORMATION 2020. [PMCID: PMC7314866 DOI: 10.1007/978-3-030-51372-6_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The Kappa biochemistry and the MØD organo-chemistry frameworks are amongst the most intensely developed applications of rewriting theoretical methods in the life sciences to date. A typical feature of these types of rewriting theories is the necessity to implement certain structural constraints on the objects to be rewritten (a protein is empirically found to have a certain signature of sites, a carbon atom can form at most four bonds, ...). In this paper, we contribute to the theoretical foundations of these types of rewriting theory a number of conceptual and technical developments that permit to implement a universal theory of continuous-time Markov chains (CTMCs) for stochastic rewriting systems. Our core mathematical concepts are a novel rule algebra construction for the relevant setting of rewriting rules with conditions, both in Double- and in Sesqui-Pushout semantics, augmented by a suitable stochastic mechanics formalism extension that permits to derive dynamical evolution equations for pattern-counting statistics.
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37
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Ortega OO, Lopez CF. Interactive Multiresolution Visualization of Cellular Network Processes. iScience 2019; 23:100748. [PMID: 31884165 PMCID: PMC6941861 DOI: 10.1016/j.isci.2019.100748] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 11/08/2019] [Accepted: 11/25/2019] [Indexed: 12/24/2022] Open
Abstract
Visualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.
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Affiliation(s)
- Oscar O Ortega
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, USA
| | - Carlos F Lopez
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, USA; Biochemistry Department, Vanderbilt University, Nashville, TN, USA.
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38
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Berenger F, Zhang KYJ, Yamanishi Y. Chemoinformatics and structural bioinformatics in OCaml. J Cheminform 2019; 11:10. [PMID: 30719579 PMCID: PMC6689879 DOI: 10.1186/s13321-019-0332-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 01/22/2019] [Indexed: 11/10/2022] Open
Abstract
Background OCaml is a functional programming language with strong static types, Hindley–Milner type inference and garbage collection. In this article, we share our experience in prototyping chemoinformatics and structural bioinformatics software in OCaml. Results First, we introduce the language, list entry points for chemoinformaticians who would be interested in OCaml and give code examples. Then, we list some scientific open source software written in OCaml. We also present recent open source libraries useful in chemoinformatics. The parallelization of OCaml programs and their performance is also shown. Finally, tools and methods useful when prototyping scientific software in OCaml are given. Conclusions In our experience, OCaml is a programming language of choice for method development in chemoinformatics and structural bioinformatics.
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Affiliation(s)
- Francois Berenger
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.,PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
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39
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Welkenhuysen N, Schnitzer B, Österberg L, Cvijovic M. Robustness of Nutrient Signaling Is Maintained by Interconnectivity Between Signal Transduction Pathways. Front Physiol 2019; 9:1964. [PMID: 30719010 PMCID: PMC6348271 DOI: 10.3389/fphys.2018.01964] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 12/31/2018] [Indexed: 12/16/2022] Open
Abstract
Systems biology approaches provide means to study the interplay between biological processes leading to the mechanistic understanding of the properties of complex biological systems. Here, we developed a vector format rule-based Boolean logic model of the yeast S. cerevisiae cAMP-PKA, Snf1, and the Snf3-Rgt2 pathway to better understand the role of crosstalk on network robustness and function. We identified that phosphatases are the common unknown components of the network and that crosstalk from the cAMP-PKA pathway to other pathways plays a critical role in nutrient sensing events. The model was simulated with known crosstalk combinations and subsequent analysis led to the identification of characteristics and impact of pathway interconnections. Our results revealed that the interconnections between the Snf1 and Snf3-Rgt2 pathway led to increased robustness in these signaling pathways. Overall, our approach contributes to the understanding of the function and importance of crosstalk in nutrient signaling.
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Affiliation(s)
- Niek Welkenhuysen
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Barbara Schnitzer
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Linnea Österberg
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Marija Cvijovic
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
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40
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Hlavacek WS, Csicsery-Ronay JA, Baker LR, Ramos Álamo MDC, Ionkov A, Mitra ED, Suderman R, Erickson KE, Dias R, Colvin J, Thomas BR, Posner RG. A Step-by-Step Guide to Using BioNetFit. Methods Mol Biol 2019; 1945:391-419. [PMID: 30945257 DOI: 10.1007/978-1-4939-9102-0_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.
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Affiliation(s)
- William S Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Jennifer A Csicsery-Ronay
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Lewis R Baker
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
- Department of Applied Mathematics, University of Colorado, Boulder, CO, USA
| | - María Del Carmen Ramos Álamo
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Alexander Ionkov
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Eshan D Mitra
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ryan Suderman
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA
- Immunetrics, Inc., Pittsburgh, PA, USA
| | - Keesha E Erickson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Raquel Dias
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Joshua Colvin
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Brandon R Thomas
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Richard G Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
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Tapia JJ, Saglam AS, Czech J, Kuczewski R, Bartol TM, Sejnowski TJ, Faeder JR. MCell-R: A Particle-Resolution Network-Free Spatial Modeling Framework. Methods Mol Biol 2019; 1945:203-229. [PMID: 30945248 PMCID: PMC6580425 DOI: 10.1007/978-1-4939-9102-0_9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multistate, multicomponent systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially resolved individual molecules over biologically relevant time and length scales.
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Affiliation(s)
- Jose-Juan Tapia
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ali Sinan Saglam
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jacob Czech
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Robert Kuczewski
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Thomas M. Bartol
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Terrence J. Sejnowski
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
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42
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Andrei O, Fernández M, Kirchner H, Pinaud B. Strategy-Driven Exploration for Rule-Based Models of Biochemical Systems with PORGY. Methods Mol Biol 2019; 1945:43-70. [PMID: 30945242 DOI: 10.1007/978-1-4939-9102-0_3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
This chapter presents PORGY-an interactive visual environment for rule-based modelling of biochemical systems. We model molecules and molecule interactions as port graphs and port graph rewrite rules, respectively. We use rewriting strategies to control which rules to apply, and where and when to apply them. Our main contributions to rule-based modelling of biochemical systems lie in the strategy language and the associated visual and interactive features offered by PORGY. These features facilitate an exploratory approach to test different ways of applying the rules while recording the model evolution, and tracking and plotting parameters. We illustrate PORGY's features with a study of the role of a scaffold protein in RAF/MEK/ERK signalling.
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Affiliation(s)
- Oana Andrei
- School of Computing Science, University of Glasgow, Glasgow, UK
| | | | | | - Bruno Pinaud
- University of Bordeaux, CNRS UMR5800 LaBRI, Talence, France.
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Berro J. "Essentially, all models are wrong, but some are useful"-a cross-disciplinary agenda for building useful models in cell biology and biophysics. Biophys Rev 2018; 10:1637-1647. [PMID: 30421276 PMCID: PMC6297095 DOI: 10.1007/s12551-018-0478-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 10/30/2018] [Indexed: 12/21/2022] Open
Abstract
Intuition alone often fails to decipher the mechanisms underlying the experimental data in Cell Biology and Biophysics, and mathematical modeling has become a critical tool in these fields. However, mathematical modeling is not as widespread as it could be, because experimentalists and modelers often have difficulties communicating with each other, and are not always on the same page about what a model can or should achieve. Here, we present a framework to develop models that increase the understanding of the mechanisms underlying one's favorite biological system. Development of the most insightful models starts with identifying a good biological question in light of what is known and unknown in the field, and determining the proper level of details that are sufficient to address this question. The model should aim not only to explain already available data, but also to make predictions that can be experimentally tested. We hope that both experimentalists and modelers who are driven by mechanistic questions will find these guidelines useful to develop models with maximum impact in their field.
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Affiliation(s)
- Julien Berro
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
- Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA.
- Nanobiology Institute, Yale University, West Haven, CT, USA.
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