Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
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Probabilistic Boolean Networks (PBN) are introduced that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.Abstract:
Motivation: Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. Results: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks—a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.read more
Citations
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Control of Boolean networks: hardness results and algorithms for tree structured networks.
TL;DR: It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general and this hardness result is extended for BNs with considerably restricted network structures.
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Inference of gene regulatory networks and compound mode of action from time course gene expression profiles
TL;DR: It is shown that it is possible to recover the gene regulatory network from a time series data of gene expression following a perturbation to the cell and on a nine gene subnetwork part of the DNA-damage response pathway (SOS pathway) in the bacteria E. coli.
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Boolean modeling in systems biology: An overview of methodology and applications
TL;DR: This paper builds on research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models.
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Comparison of co-expression measures: mutual information, correlation, and model based indices
TL;DR: The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships and can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.
Journal ArticleDOI
Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction
Julio Saez-Rodriguez,Julio Saez-Rodriguez,Leonidas G. Alexopoulos,Leonidas G. Alexopoulos,Jonathan P. Epperlein,Regina Samaga,Douglas A. Lauffenburger,Steffen Klamt,Peter K. Sorger,Peter K. Sorger +9 more
TL;DR: A computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data, which represents a means to generate predictive, cell‐type‐specific models of mammalian signalling from generic protein signalling networks.
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