Reverse Engineering Under Uncertainty

The increased availability of experimental data in systems biology and systems medicine can only lead to better understanding of biological and disease related processes, if we can place them in the context of mechanistic models. Such models can serve as conceptual, but also computational frameworks in which we can reason about, or predict the behaviour of e.g. molecular networks, or cellular processes. Constructing such models, however, remains a formidable challenge: not only are the data noisy and incomplete, but the models that are currently available are hopelessly oversimplified. In this chapter we set out the problems and a list of potential ways of tackling them. The essential premise is always to be aware of the uncertainties inherent in the data and our models.

[1]  Kamil Erguler,et al.  Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models. , 2011, Molecular bioSystems.

[2]  Tina Toni,et al.  Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes , 2011, Nature communications.

[3]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[4]  J. Stoer,et al.  Introduction to Numerical Analysis , 2002 .

[5]  Mikael Sunnåker,et al.  Model Extension and Model Selection , 2016 .

[6]  Bruce Tidor,et al.  Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology , 2013, PLoS Comput. Biol..

[7]  Darren J. Wilkinson.  Stochastic Modelling for Systems Biology , 2006 .

[8]  Michael P H Stumpf,et al.  A general moment expansion method for stochastic kinetic models. , 2013, The Journal of chemical physics.

[9]  Mark Girolami,et al.  Comprar Handbook Of Statistical Systems Biology | Michael P. H. Stumpf | 9780470710869 | Wiley , 2011 .

[10]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[11]  Juergen Hahn,et al.  Integrating parameter selection with experimental design under uncertainty for nonlinear dynamic systems , 2008 .

[12]  Michael P. H. Stumpf,et al.  Inference of temporally varying Bayesian Networks , 2012, Bioinform..

[13]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[14]  Robert J. Flassig,et al.  Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks , 2012, Bioinform..

[15]  P. Swain,et al.  Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Mark Girolami,et al.  Handbook of Statistical Systems Biology: Stumpf/Handbook of Statistical Systems Biology , 2011 .

[17]  Michael P H Stumpf,et al.  Decomposing noise in biochemical signaling systems highlights the role of protein degradation. , 2011, Biophysical journal.

[18]  Thomas Thorne,et al.  Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation. , 2012, Integrative biology : quantitative biosciences from nano to macro.

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

[20]  Rob Johnson,et al.  SYSBIONS: nested sampling for systems biology , 2015, Bioinform..

[21]  R. Wilkinson.  Approximate Bayesian computation (ABC) gives exact results under the assumption of model error , 2008, Statistical applications in genetics and molecular biology.

[22]  A. Hartemink.  Reverse engineering gene regulatory networks , 2005, Nature Biotechnology.

[23]  D. Gillespie.  A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[24]  Jeremy E. Oakley,et al.  Managing structural uncertainty in health economic decision models: a discrepancy approach , 2012 .

[25]  Peter A. J. Hilbers,et al.  A Bayesian approach to targeted experiment design , 2012, Bioinform..

[26]  Reinhard Laubenbacher,et al.  Comparison of Reverse‐Engineering Methods Using an in Silico Network , 2007, Annals of the New York Academy of Sciences.

[27]  Christopher R. Myers,et al.  Universally Sloppy Parameter Sensitivities in Systems Biology Models , 2007, PLoS Comput. Biol..

[28]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[29]  Andrew M. Stuart,et al.  Inverse problems: A Bayesian perspective , 2010, Acta Numerica.

[30]  J. Doyle,et al.  Reverse Engineering of Biological Complexity , 2002, Science.

[31]  Xiaohui Xie,et al.  Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent , 2010, BMC Systems Biology.

[32]  Bernard Haasdonk,et al.  Efficient parametric analysis of the chemical master equation through model order reduction , 2012, BMC Systems Biology.

[33]  R. May.  Uses and Abuses of Mathematics in Biology , 2004, Science.

[34]  Michael P H Stumpf,et al.  Sensitivity, robustness, and identifiability in stochastic chemical kinetics models , 2011, Proceedings of the National Academy of Sciences.

[35]  Daniel T Gillespie,et al.  Stochastic simulation of chemical kinetics. , 2007, Annual review of physical chemistry.

[36]  R. Milo,et al.  Oscillations and variability in the p53 system , 2006, Molecular systems biology.

[37]  Michael P H Stumpf,et al.  Bayesian design strategies for synthetic biology , 2011, Interface Focus.

[38]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[39]  Carsten Wiuf,et al.  The effects of incomplete protein interaction data on structural and evolutionary inferences , 2006, BMC Biology.

[40]  Marco Grzegorczyk,et al.  Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks , 2006, Bioinform..

[41]  Jesper Tegnér,et al.  Optimization in Biology Parameter Estimation and the Associated Optimization Problem , 2016 .

[42]  P. Donnelly,et al.  Approximate likelihood methods for estimating local recombination rates , 2002 .

[43]  Thomas Thorne,et al.  Model selection in systems and synthetic biology. , 2013, Current opinion in biotechnology.

[44]  D. Wilkinson.  Stochastic modelling for quantitative description of heterogeneous biological systems , 2009, Nature Reviews Genetics.

[45]  Bruce Tidor,et al.  Sloppy models, parameter uncertainty, and the role of experimental design. , 2010, Molecular bioSystems.

[46]  Clive G. Bowsher,et al.  Identifying sources of variation and the flow of information in biochemical networks , 2012, Proceedings of the National Academy of Sciences.

[47]  Thierry Bastogne,et al.  The Experimental Side of Parameter Estimation , 2016 .

[48]  Xun Huan,et al.  Simulation-based optimal Bayesian experimental design for nonlinear systems , 2011, J. Comput. Phys..

[49]  D. Lindley.  On a Measure of the Information Provided by an Experiment , 1956 .

[50]  David Welch,et al.  Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems , 2009, Journal of The Royal Society Interface.

[51]  J. Skilling.  Nested sampling for general Bayesian computation , 2006 .

[52]  S. Brenner.  Sequences and consequences , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[53]  Kwang-Hyun Cho,et al.  Optimal sampling time selection for parameter estimation in dynamic pathway modeling. , 2004, Bio Systems.

[54]  Johan Paulsson,et al.  Separating intrinsic from extrinsic fluctuations in dynamic biological systems , 2011, Proceedings of the National Academy of Sciences.

[55]  Korbinian Strimmer,et al.  An empirical Bayes approach to inferring large-scale gene association networks , 2005, Bioinform..

[56]  Emerson M. Pugh,et al.  The analysis of physical measurements , 1966 .

[57]  Ramon Grima,et al.  A study of the accuracy of moment-closure approximations for stochastic chemical kinetics. , 2012, The Journal of chemical physics.

[58]  Michael P. H. Stumpf,et al.  Maximizing the Information Content of Experiments in Systems Biology , 2013, PLoS Comput. Biol..