Implicit methods for probabilistic modeling of Gene Regulatory Networks.
In silico modeling of Gene Regulatory Networks (GRN) has recently aroused a lot of interest in the biological community for modeling and understanding complex pathways. Boolean Networks (BN) are a common modeling tool for in silico dynamic analysis of such pathways. Although they are known to have effectively modeled many real and complex regulatory networks, they are deterministic in nature and have shortcomings in modeling non-determinism that is inherent in biological systems. Probabilistic Boolean Networks (PBN) have been proposed to counter these shortcomings. The capabilities of PBNs have been so far under-utilised because of the lack of an efficient PBN toolbox. This work addresses some issues associated with traditional methods of PBN representation and proposes efficient algorithms to model gene regulatory networks using PBNs.
Garg
Abhishek
A
Laboratory of System Integrated, Faculty of Information and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Station 14, 1015, Switzerland. abhishek.garg@epfl.ch
Banerjee
Debasree
D
De Micheli
Giovanni
G
D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D005786:Gene Expression Regulation; D005823:Genetics; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D012984:Software