Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors

The inference of gene regulatory networks gained within recent years a considerable interest in the biology and biomedical community. The purpose of this paper is to investigate the influence that environmental conditions can exhibit on the inference performance of network inference algorithms. Specifically, we study five network inference methods, Aracne, BC3NET, CLR, C3NET and MRNET, and compare the results for three different conditions: (I) observational gene expression data: normal environmental condition, (II) interventional gene expression data: growth in rich media, (III) interventional gene expression data: normal environmental condition interrupted by a positive spike-in stimulation. Overall, we find that different statistical inference methods lead to comparable, but condition-specific results. Further, our results suggest that non-steady-state data enhance the inferability of regulatory networks.

[1]  Frank Emmert-Streib,et al.  Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma , 2012, BMC Systems Biology.

[2]  Liam Paninski,et al.  Estimation of Entropy and Mutual Information , 2003, Neural Computation.

[3]  Frank Emmert-Streib,et al.  Inferring the conservative causal core of gene regulatory networks , 2010, BMC Systems Biology.

[4]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Kevin Kontos,et al.  Information-Theoretic Inference of Large Transcriptional Regulatory Networks , 2007, EURASIP J. Bioinform. Syst. Biol..

[6]  Martin Vingron,et al.  Normalization and quantification of differential expression in gene expression microarrays , 2006, Briefings Bioinform..

[7]  Paul P. Wang,et al.  Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..

[8]  T. Tuschl,et al.  Mechanisms of gene silencing by double-stranded RNA , 2004, Nature.

[9]  Matthias Dehmer,et al.  Information processing in the transcriptional regulatory network of yeast: Functional robustness , 2009, BMC Systems Biology.

[10]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[11]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[12]  Gianluca Bontempi,et al.  On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information , 2008, EURASIP J. Bioinform. Syst. Biol..

[13]  B. Palsson,et al.  Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. , 2003, Genome research.

[14]  Mark Reimers,et al.  Making Informed Choices about Microarray Data Analysis , 2010, PLoS Comput. Biol..

[15]  Gianluca Bontempi,et al.  On the impact of entropy estimator in transcriptional regulatory network inference , 2008 .

[16]  edited by Frank Emmert-Streib and Matthias Dehmer.  Medical Biostatistics for Complex Diseases , 1994 .

[17]  Rainer Breitling,et al.  What is Systems Biology? , 2010, Front. Physiology.

[18]  H. Kitano.  Towards a theory of biological robustness , 2007, Molecular systems biology.

[19]  Galina V. Glazko,et al.  Statistical Inference and Reverse Engineering of Gene Regulatory Networks from Observational Expression Data , 2012, Front. Gene..

[20]  International Human Genome Sequencing Consortium.  Initial sequencing and analysis of the human genome , 2001, Nature.

[21]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[22]  Edda Klipp,et al.  Systems Biology , 1994 .

[23]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[24]  A. Blais,et al.  Constructing transcriptional regulatory networks. , 2005, Genes & development.

[25]  Klaus Hinkelmann,et al.  Design and Analysis of Experiments: Introduction to Experimental Design , 1994 .

[26]  J. Stelling,et al.  Robustness of Cellular Functions , 2004, Cell.

[27]  Y. Chen,et al.  Ratio-based decisions and the quantitative analysis of cDNA microarray images. , 1997, Journal of biomedical optics.

[28]  Frank Emmert-Streib,et al.  Bagging Statistical Network Inference from Large-Scale Gene Expression Data , 2012, PloS one.

[29]  H Kitano,et al.  The theory of biological robustness and its implication in cancer. , 2007, Ernst Schering Research Foundation workshop.

[30]  Frank Emmert-Streib,et al.  Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks , 2011, PloS one.

[31]  A. Zeng,et al.  An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs. , 2004, Nucleic acids research.

[32]  A. Barabasi,et al.  High-Quality Binary Protein Interaction Map of the Yeast Interactome Network , 2008, Science.

[33]  Gianluca Bontempi,et al.  minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information , 2008, BMC Bioinformatics.

[34]  John F. G. Atack,et al.  RNA Interference , 2010, Methods in Molecular Biology.

[35]  A. Wagner.  Robustness and Evolvability in Living Systems , 2005 .

[36]  V. Anne Smith,et al.  Evaluating functional network inference using simulations of complex biological systems , 2002, ISMB.

[37]  Timothy B. Stockwell,et al.  The Sequence of the Human Genome , 2001, Science.

[38]  Lin Chen,et al.  Combinatorial gene regulation by eukaryotic transcription factors. , 1999, Current opinion in structural biology.

[39]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[40]  A. Rapoport,et al.  Connectivity of random nets , 1951 .

[41]  Yongchao Ge.  Resampling-based Multiple Testing for Microarray Data Analysis , 2003 .

[42]  J. Bonfield,et al.  Finishing the euchromatic sequence of the human genome , 2004, Nature.

[43]  G. Altay,et al.  Structural influence of gene networks on their inference: analysis of C3NET. , 2011 .

[44]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[45]  M. Dehmer,et al.  Analysis of Microarray Data: A Network-Based Approach , 2008 .

[46]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

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

[48]  Jeremiah J. Faith,et al.  Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata , 2007, Nucleic Acids Res..

[49]  Claudio Cobelli,et al.  A Gene Network Simulator to Assess Reverse Engineering Algorithms , 2009, Annals of the New York Academy of Sciences.

[50]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[51]  D. Mccormick.  Sequence the Human Genome , 1986, Bio/Technology.

[52]  A. Wagner.  Robustness, evolvability, and neutrality , 2005, FEBS letters.