The potential power of dynamics in epistasis analysis

Inferring regulatory relationships between genes, including the direction and the nature of influence between them, is a fundamental challenge in molecular genetics. One classical approach to this problem is epistasis analysis, which infers regulatory relationships in genetic pathways by looking at patterns of change in an observable trait resulting from individual and combinatorial deletion of genes. As useful as this broad approach has been, there are limits to its ability to discriminate alternative pathway structures. 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 dynamic, 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.

[1]  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.

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

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

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

[5]  Michael T. Hallett,et al.  A Trade-Off between Sample Complexity and Computational Complexity in Learning Boolean Networks from Time-Series Data , 2010, IEEE ACM Trans. Comput. Biol. Bioinform..

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

[7]  Sean R. Collins,et al.  Exploration of the Function and Organization of the Yeast Early Secretory Pathway through an Epistatic Miniarray Profile , 2005, Cell.

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

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

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

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

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

[13]  Brandon Barker,et al.  Dynamic Epistasis under Varying Environmental Perturbations , 2014, PloS one.

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

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

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

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

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

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

[20]  Ivan Bratko,et al.  GenePath: a system for automated construction of genetic networks from mutant data , 2003, Bioinform..

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

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

[23]  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.

[24]  G. Church,et al.  Modular epistasis in yeast metabolism , 2005, Nature Genetics.

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

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

[27]  S. Collins,et al.  Comprehensive Characterization of Genes Required for Protein Folding in the Endoplasmic Reticulum , 2009, Science.

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

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

[30]  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.

[31]  Marinka Zitnik,et al.  Gene network inference by probabilistic scoring of relationships from a factorized model of interactions , 2014, Bioinform..

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

[33]  S. Kauffman.  Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

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

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

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

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

[38]  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.

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

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

[41]  Paul F. Lurquin,et al.  Genes and DNA: A Beginner's Guide to Genetics and Its Applications , 2004 .

[42]  Bart Deplancke,et al.  Gene Regulatory Networks , 2012, Methods in Molecular Biology.

[43]  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.

[44]  Gary D Bader,et al.  The Genetic Landscape of a Cell , 2010, Science.