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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Optimal Intervention in Markovian Gene Regulatory Networks With Random-Length Therapeutic Response to Antitumor Drug
TL;DR: This paper first probabilistically characterize the variability of the length of drug action, then presents a methodology to devise optimal intervention strategies for any Markovian genetic regulatory network governing the tumor when the antitumor drug has a random-length duration of action.
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A New Approach to Pinning Control of Boolean Networks
TL;DR: In this paper , the authors proposed a new approach on pinning control design for global stabilization of BNs based on BNs’ network structure, named as network-structure-based distributed Pinning control.
Proceedings ArticleDOI
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles
Akther Shermin,Mehmet A. Orgun +1 more
TL;DR: This work has exploited some biological features of yeast cell cycle to exploit the peak time of individual genes which falls into one/more phases of the cell cycle, and applied the Dynamic Bayesian Network algorithm within distinct phases of genes.
Journal ArticleDOI
Majority rules with random tie-breaking in Boolean gene regulatory networks.
TL;DR: It is demonstrated that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.
Journal ArticleDOI
Automatic Screening for Perturbations in Boolean Networks.
Julian D. Schwab,Hans A. Kestler +1 more
TL;DR: A method to automatically screen for perturbations that lead to a user-specified change in the network's functioning is developed and implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.
References
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TL;DR: The structure of rugged fitness landscapes and the structure of adaptive landscapes underlying protein evolution, and the architecture of genetic regulatory circuits and its evolution.
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