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A tutorial on analysis and simulation of boolean gene regulatory network models
 Curr Genomics
"... Abstract: Driven by the desire to understand genomic functions through the interactions among genes and gene products, the research in gene regulatory networks has become a heated area in genomic signal processing. Among the most studied mathematical models are Boolean networks and probabilistic Boo ..."
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Abstract: Driven by the desire to understand genomic functions through the interactions among genes and gene products, the research in gene regulatory networks has become a heated area in genomic signal processing. Among the most studied mathematical models are Boolean networks and probabilistic Boolean networks, which are rulebased dynamic systems. This tutorial provides an introduction to the essential concepts of these two Boolean models, and presents the uptodate analysis and simulation methods developed for them. In the Analysis section, we will show that Boolean models are Markov chains, based on which we present a Markovian steadystate analysis on attractors, and also reveal the relationship between probabilistic Boolean networks and dynamic Bayesian networks (another popular genetic network model), again via Markov analysis; we dedicate the last subsection to structural analysis, which opens a door to other topics such as network control. The Simulation section will start from the basic tasks of creating state transition diagrams and finding attractors, proceed to the simulation of network dynamics and obtaining the steadystate distributions, and finally come to an algorithm of generating artificial Boolean networks with prescribed attractors. The contents are arranged in a roughly logical order, such that the Markov chain analysis lays the basis for the most part of Analysis section, and also prepares the readers to the topics in Simulation section.
Optimizing ConsistencyBased Design of ContextSensitive Gene Regulatory Networks
"... Abstract—When designing a gene regulatory network, except in rare circumstances there will be inconsistencies in the data. Modeling data inconsistencies fits naturally into the framework of probabilistic Boolean networks (PBNs). This model consists of a family of deterministic models and the overall ..."
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Abstract—When designing a gene regulatory network, except in rare circumstances there will be inconsistencies in the data. Modeling data inconsistencies fits naturally into the framework of probabilistic Boolean networks (PBNs). This model consists of a family of deterministic models and the overall model is based on random switching between constituent networks, each of which determines a context. A previous paper has proposed an inference procedure for PBNs to achieve data consistency within constituent networks. This paper proposes optimization methods targeted at two dataconsistent design issues having to do with network structure: (1) generalization (namely, model selection) arising from the onetomany mapping between the data set and PBN model; (2) model reduction under constraint on network connectivity, which is typically made for computational, statistical, or biological reasons. Regarding generalization, we combine connectivity and minimal logical realization to formulate the optimality criterion and propose two algorithms to solve it, the second algorithm guaranteeing a minimally connected PBN. Regarding constrained connectivity, we rephrase it as a lossy coding problem and develop an algorithm to find a best subset of predictors from the full set of predictors with the objective of minimizing probability of prediction error. Index Terms—Gene regulatory network, logic reduction, network inference, optimization, probabilistic Boolean network (PBN). I.
Binary Based Biological Network Model: A Review
"... This article reviews some binary based models, techniques and methods used for clustering biological datasets. Models presented include the deterministic Boolean Network and its stochastic extension; Probabilistic Boolean Network. Some of the problems associated with these models as well as attempts ..."
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This article reviews some binary based models, techniques and methods used for clustering biological datasets. Models presented include the deterministic Boolean Network and its stochastic extension; Probabilistic Boolean Network. Some of the problems associated with these models as well as attempts at resolving them were presented. Their application to the modeling and study of the gene regulatory network and cell differentiation, and evolutionary changes were reviewed.
Systems biology Adaptive intervention in probabilistic boolean networks
"... Motivation: A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theo ..."
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Motivation: A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theory via the design of optimal Markov chain decision processes, mainly in the framework of probabilistic Boolean networks (PBNs). Full optimization assumes that the network is accurately modeled and, to the extent that model inference is inaccurate, which can be expected for gene regulatory networks owing to the combination of model complexity and a paucity of timecourse data, the designed intervention strategy may perform poorly. We desire intervention strategies that do not assume accurate fullmodel inference. Results: This article demonstrates the feasibility of applying online adaptive control to improve intervention performance in genetic regulatory networks modeled by PBNs. It shows via simulations that when the network is modeled by a member of a known family of PBNs, an adaptive design can yield improved performance in terms of the average cost. Two algorithms are presented, one better suited for instantaneously random PBNs and the other better suited for contextsensitive PBNs with low switching probability between the constituent BNs. Contact: