Computational Design of Informative Experiments in Systems Biology

Accurate predictions of the behavior of biological systems can be achieved through multiple iterations of modeling and experimentation. In this chapter, we present the central ideas for the design of informative experiments in systems biology. We start by formalizing the task, and proceed by introducing the required tools to process data subject to uncertainty. We analyze design approaches which are Bayesian and information-theoretic in nature. A particular emphasis is placed on implicit and explicit assumptions of the available techniques. Two main design goals are here compared: reducing uncertainty and challenging existing belief. Finally, we discuss the limitations of the presented approaches to provide general guidelines for predictive modeling.

[1]  M C Mackey,et al.  Dynamic regulation of the tryptophan operon: a modeling study and comparison with experimental data. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[3]  Mordecai Avriel,et al.  Nonlinear programming , 1976 .

[4]  Vipul Periwal,et al.  Bayesian Inference of Biological Systems: The Logic of Biology , 2006 .

[5]  John Jeremy Rice,et al.  A plausible model for the digital response of p53 to DNA damage. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[6]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[7]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[8]  Joachim M. Buhmann,et al.  Optimized expected information gain for nonlinear dynamical systems , 2009, ICML '09.

[9]  R. T. Cox.  The Algebra of Probable Inference , 1962 .

[10]  A. Zeng,et al.  Model analysis concerning the effects of growth rate and intracellular tryptophan level on the stability and dynamics of tryptophan biosynthesis in bacteria , 1997 .

[11]  A. Doucet,et al.  Parameter estimation in general state-space models using particle methods , 2003 .

[12]  Joachim M. Buhmann,et al.  Stable Bayesian Parameter Estimation for Biological Dynamical Systems , 2009, 2009 International Conference on Computational Science and Engineering.

[13]  Alberto Giovanni Busetto.  Information theoretic modeling of dynamical systems , 2012 .

[14]  Jukka Corander,et al.  Approximate Bayesian Computation , 2013, PLoS Comput. Biol..

[15]  Ron Weiss,et al.  Synthetic Gene Regulatory Systems , 2006 .

[16]  Oliver Nelles.  Dynamic Neural and Fuzzy Models , 2001 .

[17]  Joachim M Buhmann,et al.  Unsupervised modeling of cell morphology dynamics for time-lapse microscopy , 2012, Nature Methods.

[18]  Pierre Baldi,et al.  Of bits and wows: A Bayesian theory of surprise with applications to attention , 2010, Neural Networks.

[19]  T. Kuhn.  The structure of scientific revolutions, 3rd ed. , 1996 .

[20]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[21]  J. Keynes.  A Treatise on Probability. , 1923 .

[22]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[23]  Anthony C. Atkinson,et al.  Optimum Experimental Designs , 1992 .

[24]  W. Näther.  Optimum experimental designs , 1994 .

[25]  H. Risken.  The Fokker-Planck equation : methods of solution and applications , 1985 .

[26]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[27]  Kerstin Preuschoff,et al.  Optimizing Experimental Design for Comparing Models of Brain Function , 2011, PLoS Comput. Biol..

[28]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[29]  H. Risken.  Fokker-Planck Equation , 1996 .

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

[31]  E. Jaynes.  Probability theory : the logic of science , 2003 .

[32]  Aleksandr Yakovlevich Khinchin,et al.  Mathematical foundations of information theory , 1959 .

[33]  M. J. Hatcher,et al.  Modeling Biological Systems: Principles and Applications , 1997 .

[34]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[35]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[36]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[37]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[38]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[39]  Florian Steinke,et al.  Bayesian Inference and Optimal Design in the Sparse Linear Model , 2007, AISTATS.

[40]  Joachim M. Buhmann,et al.  Structure Identification by Optimized Interventions , 2009, AISTATS.

[41]  Joachim M. Buhmann,et al.  Information Theoretic Model Validation for Spectral Clustering , 2012, AISTATS.

[42]  H. Kitano,et al.  Computational systems biology , 2002, Nature.

[43]  Thomas M. Cover,et al.  Elements of Information Theory: Cover/Elements of Information Theory, Second Edition , 2005 .

[44]  R. T. Cox.  Probability, frequency and reasonable expectation , 1990 .

[45]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[46]  Marcus Hutter,et al.  A Philosophical Treatise of Universal Induction , 2011, Entropy.

[47]  Joachim M. Buhmann.  Information theoretic model validation for clustering , 2010, 2010 IEEE International Symposium on Information Theory.

[48]  J. Banga,et al.  Computational procedures for optimal experimental design in biological systems. , 2008, IET systems biology.

[49]  L. Serrano,et al.  Engineering stability in gene networks by autoregulation , 2000, Nature.

[50]  Andreas Krause,et al.  Near-optimal Nonmyopic Value of Information in Graphical Models , 2005, UAI.

[51]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[52]  Roland Eils,et al.  Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model , 2009, PLoS Comput. Biol..

[53]  John J. Tyson,et al.  Modeling Molecular Interaction Networks with Nonlinear Ordinary Differential Equations , 2006 .