Using Boolean networks to model post-transcriptional regulation in gene regulatory networks

Abstract Gene regulatory networks (GRNs) model some of the mechanisms that regulate gene expression. Among the computational approaches available to model and study GNRs, Boolean network (BN) emerged as very successful to better understand both the structural and dynamical properties of GRNs. Nevertheless, the most widely used models based on BNs do not include any post-transcriptional regulation mechanism. Since miRNAs have been proved to play an important regulatory role, in this paper we show how the post-transcriptional regulation mechanism mediated by miRNAs has been included in an enhanced BN-based model. We resort to the miR-7 in two Drosophila cell fate determination networks to verify the effectiveness of miRNAs modeling in BNs, by implementing it in our tool for the analysis of Boolean networks.

[1]  Roberto Serra,et al.  Dynamical Properties of a Boolean Model of Gene Regulatory Network with Memory , 2011, J. Comput. Biol..

[2]  Attila Csikász-Nagy,et al.  Analysis of a generic model of eukaryotic cell-cycle regulation. , 2006, Biophysical journal.

[3]  Mauro Birattari,et al.  Dynamical regimes and learning properties of evolved Boolean networks , 2013, Neurocomputing.

[4]  Hun-Way Hwang.  Dynamic regulation of microRNAs by post-transcriptional mechanisms , 2009 .

[5]  S. Kauffman,et al.  Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. , 2009, Seminars in cell & developmental biology.

[6]  Alfredo Benso,et al.  Accounting for Post-Transcriptional Regulation in Boolean Networks Based Regulatory Models , 2013, IWBBIO.

[7]  Alfredo Benso,et al.  Using gnome wide data for protein function prediction by exploiting gene ontology relationships , 2012, Proceedings of 2012 IEEE International Conference on Automation, Quality and Testing, Robotics.

[8]  Jaap A. Kaandorp,et al.  Efficient parameter estimation for spatio-temporal models of pattern formation: case study of Drosophila melanogaster , 2007, Bioinform..

[9]  Marcel J. T. Reinders,et al.  Linear Modeling of Genetic Networks from Experimental Data , 2000, ISMB.

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

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

[12]  O. Yli-Harja,et al.  Perturbation avalanches and criticality in gene regulatory networks. , 2006, Journal of theoretical biology.

[13]  David G Hendrickson,et al.  Concordant Regulation of Translation and mRNA Abundance for Hundreds of Targets of a Human microRNA , 2009, PLoS biology.

[14]  Noam Shomron,et al.  Canalization of development by microRNAs , 2006, Nature Genetics.

[15]  S. Mangan,et al.  The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. , 2003, Journal of molecular biology.

[16]  S. Grimmond,et al.  The miR-17-5p microRNA is a key regulator of the G1/S phase cell cycle transition , 2008, Genome Biology.

[17]  R. Albert,et al.  Boolean Modelingof Genetic Regulatory Networks , 2004 .

[18]  Jamie X. Luo,et al.  Evolving Sensitivity Balances Boolean Networks , 2012, PloS one.

[19]  S. Kauffman,et al.  Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. , 2007, Journal of theoretical biology.

[20]  L Glass,et al.  Co-operative components, spatial localization and oscillatory cellular dynamics. , 1972, Journal of theoretical biology.

[21]  Stefan Bornholdt,et al.  Boolean network models of cellular regulation: prospects and limitations , 2008, Journal of The Royal Society Interface.

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

[23]  Alfredo Benso,et al.  A New miRNA Motif Protects Pathways Expression in Gene Regulatory Networks , 2013, IWBBIO.

[24]  Justin J. Cassidy,et al.  A MicroRNA Imparts Robustness against Environmental Fluctuation during Development , 2009, Cell.

[25]  S. Artavanis-Tsakonas,et al.  Notch Signaling : Cell Fate Control and Signal Integration in Development , 1999 .

[26]  Jerrold E. Marsden,et al.  Perspectives and Problems in Nonlinear Science , 2003 .

[27]  D. Thieffry,et al.  A logical analysis of the Drosophila gap-gene system. , 2001, Journal of theoretical biology.

[28]  I. Mian,et al.  Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae. , 2000, Physiological genomics.

[29]  Thomas Werner,et al.  Next generation sequencing in functional genomics , 2010, Briefings Bioinform..

[30]  Sandeep Koranne,et al.  Boost C++ Libraries , 2011 .

[31]  Richard Banks,et al.  Modelling and Analysing Genetic Networks: From Boolean Networks to Petri Nets , 2006, CMSB.

[32]  L. Kadanoff,et al.  Boolean Dynamics with Random Couplings , 2002, nlin/0204062.

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

[34]  Kunihiko Kaneko,et al.  Life: An Introduction to Complex Systems Biology , 2006 .

[35]  A. Lesk.  COMPUTATIONAL MOLECULAR BIOLOGY , 1988, Proceeding of Data For Discovery.

[36]  H. Othmer,et al.  The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. , 2003, Journal of theoretical biology.

[37]  E. Furlong,et al.  Challenges for modeling global gene regulatory networks during development: insights from Drosophila. , 2010, Developmental biology.

[38]  Naama Barkai,et al.  Threshold responses to morphogen gradients by zero-order ultrasensitivity , 2005, Molecular systems biology.