title: Modeling stochasticity and robustness in gene regulatory networks.
abstract: BACKGROUND
  Understanding gene regulation in biological processes and modeling the robustness of underlying regulatory networks is an important problem that is currently being addressed by computational systems biologists. Lately, there has been a renewed interest in Boolean modeling techniques for gene regulatory networks (GRNs). However, due to their deterministic nature, it is often difficult to identify whether these modeling approaches are robust to the addition of stochastic noise that is widespread in gene regulatory processes. Stochasticity in Boolean models of GRNs has been addressed relatively sparingly in the past, mainly by flipping the expression of genes between different expression levels with a predefined probability. This stochasticity in nodes (SIN) model leads to over representation of noise in GRNs and hence non-correspondence with biological observations.
  
  
  RESULTS
  In this article, we introduce the stochasticity in functions (SIF) model for simulating stochasticity in Boolean models of GRNs. By providing biological motivation behind the use of the SIF model and applying it to the T-helper and T-cell activation networks, we show that the SIF model provides more biologically robust results than the existing SIN model of stochasticity in GRNs.
  
  
  BACKGROUND
  Algorithms are made available under our Boolean modeling toolbox, GenYsis. The software binaries can be downloaded from http://si2.epfl.ch/ approximately garg/genysis.html.
authors.affiliation: Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. abhishek.garg@epfl.ch
authors.forename: Abhishek
authors.initials: A
authors.lastname: Garg
authors.forename: Kartik
authors.initials: K
authors.lastname: Mohanram
authors.forename: Alessandro
authors.initials: A
authors.lastname: Di Cara
authors.forename: Giovanni
authors.initials: G
authors.lastname: De Micheli
authors.forename: Ioannis
authors.initials: I
authors.lastname: Xenarios
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks