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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Refining current knowledge on the yeast FLR1 regulatory network by combined experimental and computational approaches
Miguel C. Teixeira,Paulo Dias,Pedro T. Monteiro,A. Sala,Arlindo L. Oliveira,Ana T. Freitas,Isabel Sá-Correia +6 more
TL;DR: The resulting model allowed the formulation of new hypotheses, in a quick and cost effective manner, on the qualitative behavior of the system following mancozeb challenge, some of which were validated experimentally.
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A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks
Pedro T. Monteiro,Pedro T. Monteiro,Estelle Dumas,Bruno Besson,Radu Mateescu,Michel Page,Michel Page,Ana T. Freitas,Hidde de Jong +8 more
TL;DR: A generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks, which guarantees a transparent access to formal verification technology for modelers of genetic Regulatory networks.
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Efficient selection of feature sets possessing high coefficients of determination based on incremental determinations
Ronaldo Fumio Hashimoto,Edward R. Dougherty,Marcel Brun,Zheng-Zheng Zhou,Michael L. Bittner,Jeffrey M. Trent +5 more
TL;DR: The paper considers various loss measures pertaining to omitting feature sets based on the criteria, and applies the theory in the context of microarray-based genomic CoD analysis, which provides optimal computational algorithms.
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Inference of gene regulatory networks based on nonlinear ordinary differential equations.
TL;DR: A method for inferring GRNs from time-series data and steady-state data jointly and makes use of a nonlinear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently is proposed.
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Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET
TL;DR: PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions, and the general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes.
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.