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Abstract
Two years ago, in the early stages of the COVID-19 pandemic, there were widespread and grim predictions of an ensuing suicide epidemic. Not only has this not happened but also by the end of 2021 in the majority of countries and regions with available data, the suicide rates had, if anything, declined. We discuss four reasons why the predictions of suicide models were exaggerated: (1) government intervention reduced the economic and mental costs of lockdowns, (2) the pandemic itself and lockdowns had less of an effect on mental health than assumed, (3) the evidence for a link between economic downturns, distress and suicide is weaker and less consistent than the models assumed and (4) predicting suicide is generally hard. Predictive models have an important place, but their strong modelling assumptions need to acknowledge the inherent high degree of uncertainty which has been further augmented by behavioural responses of pandemic management.
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Affiliation(s)
- Nick Glozier
- Central Clinical School, Faculty of
Medicine and Health, The University of Sydney, Sydney, NSW, Australia,ARC Centre of Excellence for Children
and Families over the Life Course, Indooroopilly, QLD, Australia,Nick Glozier, Central Clinical School,
Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006,
Australia.
| | - Richard Morris
- Central Clinical School, Faculty of
Medicine and Health, The University of Sydney, Sydney, NSW, Australia,ARC Centre of Excellence for Children
and Families over the Life Course, Indooroopilly, QLD, Australia,School of Psychology, Faculty of
Science, The University of Sydney, Sydney, NSW, Australia
| | - Stefanie Schurer
- ARC Centre of Excellence for Children
and Families over the Life Course, Indooroopilly, QLD, Australia,School of Economics, The University of
Sydney, Sydney, NSW, Australia
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2
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Nilsson A, Peters JM, Meimetis N, Bryson B, Lauffenburger DA. Artificial neural networks enable genome-scale simulations of intracellular signaling. Nat Commun 2022; 13:3069. [PMID: 35654811 PMCID: PMC9163072 DOI: 10.1038/s41467-022-30684-y] [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: 09/18/2021] [Accepted: 05/11/2022] [Indexed: 12/14/2022] Open
Abstract
Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.
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Affiliation(s)
- Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Joshua M Peters
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bryan Bryson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA.
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3
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Newmiwaka T, Engelhardt B, Wendland P, Kahl D, Fröhlich H, Kschischo M. SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology. Bioinformatics 2021; 37:1330-1331. [PMID: 32931565 DOI: 10.1093/bioinformatics/btaa786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/05/2020] [Accepted: 09/03/2020] [Indexed: 11/14/2022] Open
Abstract
SUMMARY Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data. AVAILABILITY AND IMPLEMENTATION For the R-package seeds, see the CRAN server https://cran.r-project.org/package=seeds.
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Affiliation(s)
- Tobias Newmiwaka
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Remagen 53424, Germany
| | - Benjamin Engelhardt
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Remagen 53424, Germany.,Bonn-Aachen International Center for IT (b-it), Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany.,AbbVie Deutschland GmbH & Co. KG, Clinical Pharmacology and Pharmacometrics, Knollstrasse, Ludwigshafen 67061, Germany
| | - Philipp Wendland
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Remagen 53424, Germany
| | - Dominik Kahl
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Remagen 53424, Germany
| | - Holger Fröhlich
- Bonn-Aachen International Center for IT (b-it), Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53115, Germany
| | - Maik Kschischo
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Remagen 53424, Germany
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4
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Bae J, Kim Y, Lee JM. Multirate moving horizon estimation combined with parameter subset selection. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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5
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Kahl D, Kschischo M. Searching for Errors in Models of Complex Dynamic Systems. Front Physiol 2021; 11:612590. [PMID: 33505318 PMCID: PMC7830364 DOI: 10.3389/fphys.2020.612590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/11/2020] [Indexed: 11/13/2022] Open
Abstract
Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations. Uncovering the location of such structural model errors in large networks can be a daunting task for a modeler. Here, we present a data driven method to search for structural model errors and to confine their position in large and complex dynamic networks. We introduce a coherence measure for pairs of network nodes, which indicates, how difficult it is to distinguish these nodes as sources of an error. By clustering network nodes into coherence groups and inferring the cluster inputs we can decide, which cluster is affected by an error. We demonstrate the utility of our method for the C. elegans neural network, for a signal transduction model for UV-B light induced morphogenesis and for synthetic examples.
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Affiliation(s)
- Dominik Kahl
- Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
| | - Maik Kschischo
- Mathematics and Technology, University of Applied Sciences Koblenz, Koblenz, Germany
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6
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Lee D, Jayaraman A, Kwon JS. Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling. PLoS Comput Biol 2020; 16:e1008472. [PMID: 33315899 PMCID: PMC7769624 DOI: 10.1371/journal.pcbi.1008472] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/28/2020] [Accepted: 10/26/2020] [Indexed: 12/30/2022] Open
Abstract
Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively. An intracellular signaling pathway is often represented by a set of nonlinear ordinary differential equations, which translate our current knowledge about the signaling pathway into a testable mathematical model. However, predictions from such models are often subject to high uncertainty since many signaling pathways are only partially known beforehand. In this study, we propose a systematic approach to develop a hybrid model to improve model accuracy by combining machine learning and the first-principle modeling. Specifically, model correction terms are learned from discrepancy between model predictions and measurements, and these terms are added to the first-principle model to enhance the prediction accuracy. Once these correction terms are learned from the data, an artificial neural network (ANN) model is developed to find an empirical relation between the model and the correction terms so that the developed ANN can be used to posses improved predictive capabilities even in new operating conditions (i.e., generalizability). The final hybrid model is then constructed by coupling the first-principle model with the developed ANN.
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Affiliation(s)
- Dongheon Lee
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas, USA
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, Texas, USA
| | - Joseph S. Kwon
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA
- Texas A&M Energy Institute, Texas A&M University, College Station, Texas, USA
- * E-mail:
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7
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Yazdani A, Lu L, Raissi M, Karniadakis GE. Systems biology informed deep learning for inferring parameters and hidden dynamics. PLoS Comput Biol 2020; 16:e1007575. [PMID: 33206658 PMCID: PMC7710119 DOI: 10.1371/journal.pcbi.1007575] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/02/2020] [Accepted: 10/11/2020] [Indexed: 01/23/2023] Open
Abstract
Mathematical models of biological reactions at the system-level lead to a set of ordinary differential equations with many unknown parameters that need to be inferred using relatively few experimental measurements. Having a reliable and robust algorithm for parameter inference and prediction of the hidden dynamics has been one of the core subjects in systems biology, and is the focus of this study. We have developed a new systems-biology-informed deep learning algorithm that incorporates the system of ordinary differential equations into the neural networks. Enforcing these equations effectively adds constraints to the optimization procedure that manifests itself as an imposed structure on the observational data. Using few scattered and noisy measurements, we are able to infer the dynamics of unobserved species, external forcing, and the unknown model parameters. We have successfully tested the algorithm for three different benchmark problems.
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Affiliation(s)
- Alireza Yazdani
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Lu Lu
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Maziar Raissi
- Department of Applied Mathematics, University of Colorado, Boulder, Colorado, USA
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8
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Lee D, Jayaraman A, Sang-Il Kwon J. Identification of a time-varying intracellular signalling model through data clustering and parameter selection: application to NF-[inline-formula removed]B signalling pathway induced by LPS in the presence of BFA. IET Syst Biol 2019; 13:169-179. [PMID: 31318334 PMCID: PMC8687386 DOI: 10.1049/iet-syb.2018.5079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 01/02/2023] Open
Abstract
Developing a model for a signalling pathway requires several iterations of experimentation and model refinement to obtain an accurate model. However, the implementation of such an approach to model a signalling pathway induced by a poorly-known stimulus can become labour intensive because only limited information on the pathway is available beforehand to formulate an initial model. Therefore, a large number of iterations are required since the initial model is likely to be erroneous. In this work, a numerical scheme is proposed to construct a time-varying model for a signalling pathway induced by a poorly-known stimulus when its nominal model is available in the literature. Here, the nominal model refers to one that describes the signalling dynamics under a well-characterised stimulus. First, global sensitivity analysis is implemented on the nominal model to identify the most important parameters, which are assumed to be piecewise constants. Second, measurement data are clustered to determine temporal subdomains where the parameters take different values. Finally, a least-squares problem is solved to estimate the parameter values in each temporal subdomain. The effectiveness of this approach is illustrated by developing a time-varying model for NF-[inline-formula removed]B signalling dynamics induced by lipopolysaccharide in the presence of brefeldin A.
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Affiliation(s)
- Dongheon Lee
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
| | - Arul Jayaraman
- Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Joseph Sang-Il Kwon
- Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA.
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9
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Villaverde AF, Tsiantis N, Banga JR. Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models. J R Soc Interface 2019; 16:20190043. [PMID: 31266417 PMCID: PMC6685009 DOI: 10.1098/rsif.2019.0043] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
In this paper, we address the system identification problem in the context of biological modelling. We present and demonstrate a methodology for (i) assessing the possibility of inferring the unknown quantities in a dynamic model and (ii) effectively estimating them from output data. We introduce the term Full Input-State-Parameter Observability (FISPO) analysis to refer to the simultaneous assessment of state, input and parameter observability (note that parameter observability is also known as identifiability). This type of analysis has often remained elusive in the presence of unmeasured inputs. The method proposed in this paper can be applied to a general class of nonlinear ordinary differential equations models. We apply this approach to three models from the recent literature. First, we determine whether it is theoretically possible to infer the states, parameters and inputs, taking only the model equations into account. When this analysis detects deficiencies, we reformulate the model to make it fully observable. Then we move to numerical scenarios and apply an optimization-based technique to estimate the states, parameters and inputs. The results demonstrate the feasibility of an integrated strategy for (i) analysing the theoretical possibility of determining the states, parameters and inputs to a system and (ii) solving the practical problem of actually estimating their values.
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Affiliation(s)
| | - Nikolaos Tsiantis
- 1 Bioprocess Engineering Group , IIM-CSIC , Vigo , Galicia 36208 , Spain.,2 Department of Chemical Engineering , University of Vigo , Vigo , Galicia 36310 , Spain
| | - Julio R Banga
- 1 Bioprocess Engineering Group , IIM-CSIC , Vigo , Galicia 36208 , Spain
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10
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Souza RT, Mayrink J, Leite DF, Costa ML, Calderon IM, Rocha EA, Vettorazzi J, Feitosa FE, Cecatti JG. Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential. Clinics (Sao Paulo) 2019; 74:e894. [PMID: 30916173 PMCID: PMC6438130 DOI: 10.6061/clinics/2019/e894] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 11/27/2018] [Indexed: 12/31/2022] Open
Abstract
The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.
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Affiliation(s)
- Renato Teixeira Souza
- Departamento de Ginecologia e Obstetricia, Faculdade de Ciencias Medicas, Universidade Estadual de Campinas, Campinas, SP, BR
| | - Jussara Mayrink
- Departamento de Ginecologia e Obstetricia, Faculdade de Ciencias Medicas, Universidade Estadual de Campinas, Campinas, SP, BR
| | - Débora Farias Leite
- Departamento de Ginecologia e Obstetricia, Faculdade de Ciencias Medicas, Universidade Estadual de Campinas, Campinas, SP, BR
- Departamento Materno Infantil, Faculdade de Medicina, Universidade Federal de Pernambuco, Pernambuco, PE, BR
| | - Maria Laura Costa
- Departamento de Ginecologia e Obstetricia, Faculdade de Ciencias Medicas, Universidade Estadual de Campinas, Campinas, SP, BR
| | - Iracema Mattos Calderon
- Departamento de Ginecologia e Obstetricia, Faculdade de Medicina de Botucatu, Universidade Estadual de Sao Paulo (UNESP), Botucatu, SP, BR
| | - Edilberto Alves Rocha
- Departamento Materno Infantil, Faculdade de Medicina, Universidade Federal de Pernambuco, Pernambuco, PE, BR
| | - Janete Vettorazzi
- Departamento de Ginecologia e Obstetricia, Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Rio Grande do Sul, RS, BR
| | - Francisco Edson Feitosa
- Departamento de Ginecologia e Obstetricia, Faculdade de Medicina, Universidade Federal do Ceara, Ceara, CE, BR
| | - José Guilherme Cecatti
- Departamento de Ginecologia e Obstetricia, Faculdade de Ciencias Medicas, Universidade Estadual de Campinas, Campinas, SP, BR
- Corresponding author. E-mail:
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11
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Janes KA, Chandran PL, Ford RM, Lazzara MJ, Papin JA, Peirce SM, Saucerman JJ, Lauffenburger DA. An engineering design approach to systems biology. Integr Biol (Camb) 2018; 9:574-583. [PMID: 28590470 DOI: 10.1039/c7ib00014f] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.
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Affiliation(s)
- Kevin A Janes
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
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12
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Tsiantis N, Balsa-Canto E, Banga JR. Optimality and identification of dynamic models in systems biology: an inverse optimal control framework. Bioinformatics 2018. [DOI: 10.1093/bioinformatics/bty139] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Nikolaos Tsiantis
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
- Department of Chemical Engineering, University of Vigo Vigo, Spain
| | - Eva Balsa-Canto
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
| | - Julio R Banga
- Bioprocess Engineering Group, Spanish National Research Council, IIM-CSIC Vigo, Spain
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13
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Engelhardt B, Kschischo M, Fröhlich H. A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models. J R Soc Interface 2018; 14:rsif.2017.0332. [PMID: 28615495 PMCID: PMC5493809 DOI: 10.1098/rsif.2017.0332] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 05/23/2017] [Indexed: 11/12/2022] Open
Abstract
Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α-Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data.
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Affiliation(s)
- Benjamin Engelhardt
- Rheinische Friedrich-Wilhelms-Universität Bonn, Algorithmic Bioinformatics, Bonn, Germany .,DFG Research Training Group 1873, Rheinische Friedrich-Wilhelms-Universität Bonn, Germany
| | - Maik Kschischo
- Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Remagen, Germany
| | - Holger Fröhlich
- Rheinische Friedrich-Wilhelms-Universität Bonn, Algorithmic Bioinformatics, Bonn, Germany.,UCB Biosciences GmbH, Monheim, Germany
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14
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Tsigkinopoulou A, Baker SM, Breitling R. Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology. Trends Biotechnol 2017; 35:518-529. [PMID: 28094080 DOI: 10.1016/j.tibtech.2016.12.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 12/05/2016] [Accepted: 12/15/2016] [Indexed: 10/20/2022]
Abstract
Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.
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Affiliation(s)
- Areti Tsigkinopoulou
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - Syed Murtuza Baker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
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