Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
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TLDR
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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Inferring Gene Regulatory Networks From Expression Data by Discovering Fuzzy Dependency Relationships
P.C.H. Ma,Keith C. C. Chan +1 more
TL;DR: Results show that the use of fuzzy-logic based technique in gene expression data analysis can be quite effective and predict how a gene in an unseen sample would be affected by other genes in it and this makes statistical verification of the reliability of the discovered gene interactions easier.
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A CoD-based reduction algorithm for designing stationary control policies on Boolean networks
TL;DR: An algorithm is proposed that reduces a BN with perturbation, designs a control policy on the reduced network and then induces that policy to the original network, and the efficacy of this algorithm is demonstrated on networks of 10 genes or less.
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Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks
Ye Tian,Bai Zhang,Eric P. Hoffman,Robert Clarke,Zhen Zhang,Ie Ming Shih,Jianhua Xuan,David M. Herrington,Yue Wang +8 more
TL;DR: This work formulated the inference of differential dependency networks that incorporate both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions.
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Vaccination behavior by coupling the epidemic spreading with the human decision under the game theory
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Noise in random Boolean networks
Tiago P. Peixoto,Barbara Drossel +1 more
TL;DR: Two order parameters are defined, the long-time average of the Hamming distance between a network with and without noise, and the average frozenness, which is a measure of the extent to which a node prefers one of the two Boolean states.
References
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