Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
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.read more
Citations
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Increasing the efficiency of fuzzy logic-based gene expression data analysis.
TL;DR: Improvements made over previous fuzzy gene regulatory models in terms of computation time and robustness to noise are introduced.
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Inferring genetic regulatory logic from expression data
Svetlana Bulashevska,Roland Eils +1 more
TL;DR: A model for genetic regulatory interactions is proposed, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data.
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Probabilistic boolean networks
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Challenges for modeling global gene regulatory networks during development: insights from Drosophila.
TL;DR: The current challenges in moving from modeling medium scale well-characterized networks to more poorly characterized global networks are explored and coarse- and find-grain approaches to model gene regulatory networks in cis are suggested.
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Networks in cell biology
TL;DR: This work has shown that Hierarchical modularity in biological networks: the case of metabolic networks Erzsebet Ravasz Regan, a model of protein interaction identification, is compatible with higher-order topological properties.
References
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