Respectful modelling: Addressing uncertainty in dynamic system models for molecular biology

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.

[1]  Maik Kschischo,et al.  Learning (from) the errors of a systems biology model , 2016, Scientific Reports.

[2]  Leonardo Noto,et al.  Parameter Uncertainty in Shallow Rainfall-triggered Landslide Modeling at Basin Scale: A Probabilistic Approach , 2014 .

[3]  R. Breitling,et al.  Synthetic Biology of Antibiotic Production , 2014 .

[4]  J. Nielsen,et al.  Advancing metabolic engineering through systems biology of industrial microorganisms. , 2015, Current opinion in biotechnology.

[5]  Stefan Finsterle,et al.  Making sense of global sensitivity analyses , 2014, Comput. Geosci..

[6]  E. Ruppin,et al.  Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease , 2016, Nature Communications.

[7]  Johannes Schemmel,et al.  Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons , 2014, Front. Comput. Neurosci..

[8]  W. Stahel,et al.  Log-normal Distributions across the Sciences: Keys and Clues , 2001 .

[9]  Michael P H Stumpf,et al.  Topological sensitivity analysis for systems biology , 2014, Proceedings of the National Academy of Sciences.

[10]  A. Wagner,et al.  Automatic Generation of Predictive Dynamic Models Reveals Nuclear Phosphorylation as the Key Msn2 Control Mechanism , 2013, Science Signaling.

[11]  Yiling Lu,et al.  Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. , 2010, Cancer research.

[12]  Hugh D. Spence,et al.  Minimum information requested in the annotation of biochemical models (MIRIAM) , 2005, Nature Biotechnology.

[13]  Jan Schellenberger,et al.  Use of Randomized Sampling for Analysis of Metabolic Networks* , 2009, Journal of Biological Chemistry.

[14]  Walter Kolch,et al.  Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients , 2015, Science Signaling.

[15]  John Lygeros,et al.  Designing experiments to understand the variability in biochemical reaction networks , 2013, Journal of The Royal Society Interface.

[16]  Marija Cvijovic,et al.  Kinetic models in industrial biotechnology - Improving cell factory performance. , 2014, Metabolic engineering.

[17]  S. Klamt,et al.  Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks , 2013, Cell Communication and Signaling.

[18]  P. Mendes,et al.  Large-Scale Metabolic Models: From Reconstruction to Differential Equations , 2013 .

[19]  Mary C. Freeman,et al.  Integrating modeling, monitoring, and management to reduce critical uncertainties in water resource decision making. , 2013, Journal of environmental management.

[20]  Meiyappan Lakshmanan,et al.  Genome-scale in silico modeling and analysis for designing synthetic terpenoid-producing microbial cell factories , 2013 .

[21]  Stephen S. Fong,et al.  Computational approaches to metabolic engineering utilizing systems biology and synthetic biology , 2014, Computational and structural biotechnology journal.

[22]  R. Breitling,et al.  Explicit consideration of topological and parameter uncertainty gives new insights into a well‐established model of glycolysis , 2013, The FEBS journal.

[23]  G. Box Robustness in the Strategy of Scientific Model Building. , 1979 .

[24]  Neil D. Lawrence,et al.  Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data , 2013, PLoS Comput. Biol..

[25]  Nicolas André,et al.  Computational oncology — mathematical modelling of drug regimens for precision medicine , 2016, Nature Reviews Clinical Oncology.

[26]  Jeffrey D Varner,et al.  Generating effective models and parameters for RNA genetic circuits , 2015, bioRxiv.

[27]  Yonatan Sanz Perl,et al.  Elemental gesture dynamics are encoded by song premotor cortical neurons , 2013, Nature.

[28]  Yaron E. Antebi,et al.  Dynamics of epigenetic regulation at the single-cell level , 2016, Science.

[29]  Y. Jang,et al.  Production of succinic acid by metabolically engineered microorganisms. , 2016, Current opinion in biotechnology.

[30]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

[31]  David R. Gilbert,et al.  Handling Uncertainty in Dynamic Models: The Pentose Phosphate Pathway in Trypanosoma brucei , 2013, PLoS Comput. Biol..

[32]  Nicolas Le Novère,et al.  BioModels.net Web Services, a free and integrated toolkit for computational modelling software , 2010, Briefings Bioinform..

[33]  M. Stumpf,et al.  Systems biology (un)certainties , 2015, Science.

[34]  J. Liao,et al.  Ensemble modeling of metabolic networks. , 2008, Biophysical journal.

[35]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[36]  M. Girolami,et al.  Inferring Signaling Pathway Topologies from Multiple Perturbation Measurements of Specific Biochemical Species , 2010, Science Signaling.

[37]  Jeremy Gunawardena,et al.  Models in biology: ‘accurate descriptions of our pathetic thinking’ , 2014, BMC Biology.

[38]  Carel van Gend,et al.  Data and Model Integration Using JWS Online , 2007, Silico Biol..

[39]  L. Antilla Climate of scepticism: US newspaper coverage of the science of climate change , 2005 .

[40]  Chris J. Myers,et al.  The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core , 2018, J. Integr. Bioinform..

[41]  Keng C. Soh,et al.  Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. , 2013, Biotechnology journal.

[42]  Rainer Breitling,et al.  Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism , 2012, PLoS Comput. Biol..

[43]  Rainer Breitling,et al.  Synthetic biology advances for pharmaceutical production , 2015, Current opinion in biotechnology.

[44]  E. Klipp,et al.  Biochemical networks with uncertain parameters. , 2005, Systems biology.

[45]  Jeong Wook Lee,et al.  Systems metabolic engineering of microorganisms for natural and non-natural chemicals. , 2012, Nature chemical biology.

[46]  Neil D. Lawrence,et al.  Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays , 2015, Proceedings of the National Academy of Sciences.

[47]  O. Wolkenhauer Why model? , 2013, Front. Physiol..

[48]  Lingchong You,et al.  Optimal tuning of bacterial sensing potential , 2009, Molecular systems biology.

[49]  P. K. Ajikumar,et al.  The future of metabolic engineering and synthetic biology: towards a systematic practice. , 2012, Metabolic engineering.

[50]  Rainer Breitling,et al.  Computational tools for the synthetic design of biochemical pathways , 2012, Nature Reviews Microbiology.

[51]  Michael Hucka,et al.  The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core , 2010, J. Integr. Bioinform..

[52]  Jordi Vallverdú,et al.  The Best Model of a Cat Is Several Cats. , 2016, Trends in biotechnology.

[53]  Neil Swainston,et al.  Recon 2.2: from reconstruction to model of human metabolism , 2016, Metabolomics.

[54]  Nicolas Le Novère,et al.  Integration of Biochemical and Electrical Signaling-Multiscale Model of the Medium Spiny Neuron of the Striatum , 2013, PloS one.

[55]  U. Sauer,et al.  Advancing metabolic models with kinetic information. , 2014, Current opinion in biotechnology.

[56]  R. Breitling,et al.  Modeling challenges in the synthetic biology of secondary metabolism. , 2013, ACS synthetic biology.

[57]  N A W van Riel,et al.  Parameter uncertainty in biochemical models described by ordinary differential equations. , 2013, Mathematical biosciences.

[58]  A. O'Hagan,et al.  Probabilistic sensitivity analysis of complex models: a Bayesian approach , 2004 .

[59]  Mark Girolami,et al.  Bayesian approaches for mechanistic ion channel modeling. , 2013, Methods in molecular biology.

[60]  L. Meyers,et al.  Respiratory virus transmission dynamics determine timing of asthma exacerbation peaks: Evidence from a population-level model , 2016, Proceedings of the National Academy of Sciences.

[61]  Edda Klipp,et al.  SBtab: a flexible table format for data exchange in systems biology , 2016, Bioinform..

[62]  Nicole Radde,et al.  mcmc_clib-an advanced MCMC sampling package for ode models , 2014, Bioinform..

[63]  B. Corfe,et al.  A mathematical model of the colon crypt capturing compositional dynamic interactions between cell types , 2014, International journal of experimental pathology.

[64]  E. Motamedian,et al.  Reconstruction of a charge balanced genome-scale metabolic model to study the energy-uncoupled growth of Zymomonas mobilis ZM1. , 2016, Molecular bioSystems.

[65]  V. Singh,et al.  Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets , 2006, Theoretical Biology and Medical Modelling.

[66]  Arto Annila,et al.  Natural distribution. , 2007, Mathematical biosciences.

[67]  Neil Swainston,et al.  RobOKoD: microbial strain design for (over)production of target compounds , 2015, Front. Cell Dev. Biol..

[68]  Sriganesh Srihari,et al.  Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks , 2016, npj Systems Biology and Applications.

[69]  Ryan S. Senger,et al.  A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories , 2014, Computational and structural biotechnology journal.

[70]  Philip Miller,et al.  BiGG Models: A platform for integrating, standardizing and sharing genome-scale models , 2015, Nucleic Acids Res..

[71]  Rudiyanto Gunawan,et al.  Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles , 2012, Metabolites.

[72]  Peter J. Hunter,et al.  The CellML 1.1 Specification , 2015, J. Integr. Bioinform..

[73]  D. Kirschner,et al.  A methodology for performing global uncertainty and sensitivity analysis in systems biology. , 2008, Journal of theoretical biology.

[74]  G. Buzsáki,et al.  The log-dynamic brain: how skewed distributions affect network operations , 2014, Nature Reviews Neuroscience.