Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks

We develop a new regression algorithm, cMIKANA, for inference of gene regulatory networks from combinations of steady-state and time-series gene expression data. Using simulated gene expression datasets to assess the accuracy of reconstructing gene regulatory networks, we show that steady-state and time-series data sets can successfully be combined to identify gene regulatory interactions using the new algorithm. Inferring gene networks from combined data sets was found to be advantageous when using noisy measurements collected with either lower sampling rates or a limited number of experimental replicates. We illustrate our method by applying it to a microarray gene expression dataset from human umbilical vein endothelial cells (HUVECs) which combines time series data from treatment with growth factor TNF and steady state data from siRNA knockdown treatments. Our results suggest that the combination of steady-state and time-series datasets may provide better prediction of RNA-to-RNA interactions, and may also reveal biological features that cannot be identified from dynamic or steady state information alone. Finally, we consider the experimental design of genomics experiments for gene regulatory network inference and show that network inference can be improved by incorporating steady-state measurements with time-series data.

[1]  Christopher A. Penfold,et al.  How to infer gene networks from expression profiles, revisited , 2011, Interface Focus.

[2]  Edmund J. Crampin,et al.  Enzyme catalyzed reactions: From experiment to computational mechanism reconstruction , 2010, Comput. Biol. Chem..

[3]  Ming-Hui Chen,et al.  A Bayesian Approach to Pathway Analysis by Integrating Gene–Gene Functional Directions and Microarray Data , 2012, Statistics in biosciences.

[4]  Marlien Herselman,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2015 .

[5]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[6]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[7]  Jeffrey T. Chang,et al.  GATHER: a systems approach to interpreting genomic signatures , 2006, Bioinform..

[8]  J. Collins,et al.  Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks , 2005, Nature Biotechnology.

[9]  E. Crampin,et al.  Reconstructing gene regulatory networks: from random to scale-free connectivity. , 2006, Systems biology.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  P. McSharry,et al.  Mathematical and computational techniques to deduce complex biochemical reaction mechanisms. , 2004, Progress in biophysics and molecular biology.

[12]  Richard Bonneau,et al.  The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo , 2006, Genome Biology.

[13]  Benjamin E Dunmore,et al.  Gene network inference and visualization tools for biologists: application to new human transcriptome datasets , 2011, Nucleic acids research.

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

[15]  Eike Kiltz,et al.  Tightly-Secure Signatures from Chameleon Hash Functions , 2015, Public Key Cryptography.

[16]  A. Mees,et al.  On selecting models for nonlinear time series , 1995 .

[17]  Michele Ceccarelli,et al.  articleTimeDelay-ARACNE : Reverse engineering of gene networks from time-course data by an information theoretic approach , 2010 .

[18]  Patrik D'haeseleer,et al.  Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.

[19]  J. Hasty,et al.  Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Marcel J. T. Reinders,et al.  Least absolute regression network analysis of the murine osteoblast differentiation network , 2006, Bioinform..

[21]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

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

[23]  P. McSharry,et al.  Reconstructing biochemical pathways from time course data , 2007, Proteomics.

[24]  Richard Bonneau.  Learning biological networks: from modules to dynamics. , 2008, Nature chemical biology.

[25]  Subodh B. Rawool,et al.  Steady state approach to model gene regulatory networks - Simulation of microarray experiments , 2007, Biosyst..

[26]  Edmund J. Crampin,et al.  A Graphical User Interface for a Method to Infer Kinetics and Network Architecture (MIKANA) , 2011, PloS one.

[27]  Min Zou,et al.  A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..

[28]  Nathan Intrator,et al.  Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data , 2007, MCS.

[29]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[30]  Qing Nie,et al.  Incorporating Existing Network Information into Gene Network Inference , 2009, PloS one.

[31]  Tong Zhou,et al.  Gene Regulatory Network Inference from Multifactorial Perturbation Data Using both Regression and Correlation Analyses , 2012, Proceedings of the 31st Chinese Control Conference.

[32]  Edmund J. Crampin,et al.  Extracting Biochemical Reaction Kinetics from Time Series Data , 2004, KES.

[33]  Bartek Wilczynski,et al.  Applying dynamic Bayesian networks to perturbed gene expression data , 2006, BMC Bioinformatics.