Dynamics in Epistasis Analysis

Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations. Here, we explore the possibility of dynamic epistasis analysis, in which, in addition to performing genetic perturbations of a pathway, we drive the pathway by a time-varying upstream signal. We explore the theoretical power of dynamical epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We find that even relatively simple input dynamics greatly increases the power of epistasis analysis to discriminate alternative network structures. Further, we explore the question of experiment design, and show that a subset of short time-varying signals, which we call dynamic primitives, allow maximum discriminative power with a reduced number of experiments.

[1]  Carsten Peterson,et al.  Random Boolean network models and the yeast transcriptional network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  G. Whitesides.  The origins and the future of microfluidics , 2006, Nature.

[3]  E. Davidson,et al.  Gene regulatory networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[4]  T. Perkins,et al.  A Trade-Off between Sample Complexity and Computational Complexity in Learning Boolean Networks from Time-Series Data , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  Jerome T. Mettetal,et al.  The Frequency Dependence of Osmo-Adaptation in Saccharomyces cerevisiae , 2008, Science.

[6]  Kevin Murphy,et al.  Modelling Gene Expression Data using Dynamic Bayesian Networks , 2006 .

[7]  Jesper Tegnér,et al.  Reverse engineering gene networks using singular value decomposition and robust regression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[8]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[9]  S A Kauffman,et al.  Scaling in ordered and critical random boolean networks. , 2002, Physical review letters.

[10]  Roded Sharan,et al.  Experimental design schemes for learning Boolean network models , 2014, Bioinform..

[11]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[12]  Robert P. St.Onge,et al.  Defining genetic interaction , 2008, Proceedings of the National Academy of Sciences.

[13]  L. Glass,et al.  Inferring models of gene expression dynamics. , 2004, Journal of theoretical biology.

[14]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[15]  Joshua L. Payne,et al.  Function does not follow form in gene regulatory circuits , 2015, Scientific Reports.

[16]  Ezgi O. Booth,et al.  Epistasis analysis with global transcriptional phenotypes , 2005, Nature Genetics.

[17]  Gary D Bader,et al.  Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.

[18]  Liu Yang,et al.  Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data , 2011, PLoS Comput. Biol..

[19]  L. Raeymaekers,et al.  Dynamics of Boolean networks controlled by biologically meaningful functions. , 2002, Journal of theoretical biology.

[20]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[21]  L. Glass,et al.  The logical analysis of continuous, non-linear biochemical control networks. , 1973, Journal of theoretical biology.

[22]  D. Floreano,et al.  Revealing strengths and weaknesses of methods for gene network inference , 2010, Proceedings of the National Academy of Sciences.

[23]  I. Albert,et al.  Attractor analysis of asynchronous Boolean models of signal transduction networks. , 2010, Journal of theoretical biology.

[24]  L. Tournier,et al.  Uncovering operational interactions in genetic networks using asynchronous Boolean dynamics. , 2009, Journal of theoretical biology.

[25]  D. Koller,et al.  Automated identification of pathways from quantitative genetic interaction data , 2010, Molecular systems biology.

[26]  Aurélien Naldi,et al.  Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle , 2006, ISMB.

[27]  Elena R. Alvarez-Buylla,et al.  Dynamic Network-Based Epistasis Analysis: Boolean Examples , 2011, Front. Plant Sci..

[28]  Fabian J. Theis,et al.  Odefy -- From discrete to continuous models , 2010, BMC Bioinformatics.

[29]  P. Phillips.  Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems , 2008, Nature Reviews Genetics.

[30]  Ronald W. Davis,et al.  Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions , 2007, Nature Genetics.

[31]  Z. Szallasi,et al.  Modeling the normal and neoplastic cell cycle with "realistic Boolean genetic networks": their application for understanding carcinogenesis and assessing therapeutic strategies. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[32]  Brandon Barker,et al.  Dynamic epistasis for different alleles of the same gene , 2012, Proceedings of the National Academy of Sciences.

[33]  Satoru Miyano,et al.  Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions , 1998, SODA '98.

[34]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[35]  M. Jonikas,et al.  for Protein Folding in the Endoplasmic Reticulum Comprehensive Characterization of Genes Required , 2009 .

[36]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[37]  Mads Kærn,et al.  Identifiability and inference of pathway motifs by epistasis analysis. , 2013, Chaos.

[38]  N. Perrimon,et al.  Comparing CRISPR and RNAi-based screening technologies , 2016, Nature Biotechnology.

[39]  R. Bernards,et al.  CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes , 2016, Nature Biotechnology.

[40]  Theodore J. Perkins,et al.  The potential power of dynamics in epistasis analysis , 2015, BCB.

[41]  S. Kauffman,et al.  Activities and sensitivities in boolean network models. , 2004, Physical review letters.

[42]  D. Beebe,et al.  The present and future role of microfluidics in biomedical research , 2014, Nature.

[43]  Henry Horng-Shing Lu,et al.  Inference of Biological Pathway from Gene Expression Profiles by Time Delay Boolean Networks , 2012, PloS one.

[44]  Michael T. McManus,et al.  Gene silencing in mammals by small interfering RNAs , 2002, Nature Reviews Genetics.

[45]  Darren J. Wilkinson,et al.  Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models , 2006, J. Comput. Biol..

[46]  M. Vingron,et al.  Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments , 2009, Molecular systems biology.

[47]  Jeffry D. Sander,et al.  CRISPR-Cas systems for editing, regulating and targeting genomes , 2014, Nature Biotechnology.

[48]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[49]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[50]  L. Avery,et al.  Ordering gene function: the interpretation of epistasis in regulatory hierarchies. , 1992, Trends in genetics : TIG.

[51]  Luis Mendoza,et al.  Building Qualitative Models of Plant Regulatory Networks with SQUAD , 2012, Front. Plant Sci..

[52]  Yigal D. Nochomovitz,et al.  Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[53]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[54]  Bruce T. Lahn,et al.  Systematic Comparison of Constitutive Promoters and the Doxycycline-Inducible Promoter , 2010, PloS one.

[55]  Gary D Bader,et al.  Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants , 2001, Science.

[56]  H. Cordell.  Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. , 2002, Human molecular genetics.

[57]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..