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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A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks
TL;DR: The Bayesian method has been used to construct a PGRN that contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state.
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Controllability Analysis and Control Design for Switched Boolean Networks with State and Input Constraints
Haitao Li,Yuzhen Wang +1 more
TL;DR: This paper investigates the controllability analysis and the control design for switched Boolean networks (SBNs) with state and input constraints by using the semi-tensor product method and presents a number of new results on their controllable, optimal control, and stabilization.
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Modeling of gene regulatory networks: A review
TL;DR: This paper tried to review the different methods for reconstructing gene regulatory networks and proposed methods have proved to be valuable tools in bioinformatics applications.
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Uncovering operational interactions in genetic networks using asynchronous boolean dynamics
Laurent Tournier,Madalena Chaves +1 more
TL;DR: A model reduction method will be developed for identifying the active or operational interactions responsible for a given dynamic behaviour and its usefulness is illustrated by an application to a model of programmed cell death.
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From biological pathways to regulatory networks
TL;DR: A general theoretical framework for generating Boolean networks whose state transitions realize a set of given biological pathways or minor variations thereof is presented, and it is shown that the incorporation of prior knowledge can bring about a dramatic reduction in the cardinality of the network search space.
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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Metabolic stability and epigenesis in randomly constructed genetic nets
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TL;DR: The principal ideas of probabilistic reasoning - known as Bayesian networks - are outlined and their practical implications illustrated and are intended for MSc students in knowledge-based systems, artificial intelligence and statistics, and for professionals in decision support systems applications and research.