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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BoolNet—an R package for generation, reconstruction and analysis of Boolean networks
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Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis.
TL;DR: This study constructed an integrated regulatory-metabolic network for the model organism, Escherichia coli, and demonstrated that the method based on automated inference is more accurate and comprehensive than the current state of the art, which is based on manual curation of literature.
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Quantitative and logic modelling of molecular and gene networks
TL;DR: This work has shown that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.
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Current approaches to gene regulatory network modelling
Thomas Schlitt,Alvis Brazma +1 more
TL;DR: A hybrid model called Finite State Linear Model is described and some simple network dynamics can be simulated in this model, and the topology of gene regulatory networks in yeast is studied in more detail.
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Brief paper: Controllability of Boolean control networks via the Perron-Frobenius theory
TL;DR: Two definitions for controllability of a BCN are introduced, and it is shown that a necessary and sufficient condition for each form of controllable is that a certain nonnegative matrix is irreducible or primitive, respectively.
References
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