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Open AccessJournal ArticleDOI

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.

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Book ChapterDOI

Modeling Asymmetric Cell Division in Caulobacter crescentus Using a Boolean Logic Approach.

TL;DR: This chapter describes a modeling approach based on the Boolean logic framework that provides a means for the integration of knowledge and study of the emergence of asymmetric division and illustrates how the simulation of simple logic models gives valuable insight into the dynamic behavior of the regulatory and signaling networks driving the emergence.
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Information-thermodynamic characterization of stochastic Boolean networks

TL;DR: This work systematically characterize gene regulatory networks on the basis of information thermodynamics, and model three-node gene regulatory patterns by a stochastic Boolean model, which receive one or two input signals that carry external information.
Journal ArticleDOI

Functional analyses of NSF1 in wine yeast using interconnected correlation clustering and molecular analyses.

TL;DR: The Interdependent Correlation Clustering (ICC) method is developed to analyze relationships that exist among genes conditioned on the expression of a specific target gene in microarray data to mining for novel gene functions using complex microarray datasets with a limited number of samples.
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Joint Latent Space Model for Social Networks with Multivariate Attributes

TL;DR: A Variational Bayesian Expectation-Maximization estimation algorithm is developed to estimate the posterior distribution of the attribute and person locations in the joint latent space of the Attribute and Person Latent Space Model.
Journal ArticleDOI

Finite‐time observability of probabilistic Boolean control networks

TL;DR: In this paper , the finite-time observability of probabilistic Boolean control networks (PBCNs) based on set reachability and parallel extension is investigated, where some efficient criteria are proposed.
References
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Book

The Origins of Order: Self-Organization and Selection in Evolution

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.
Journal ArticleDOI

Metabolic stability and epigenesis in randomly constructed genetic nets

TL;DR: The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”.
Journal ArticleDOI

Using Bayesian networks to analyze expression data

TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
Book

An introduction to Bayesian networks

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.
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