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 novel method of gene regulatory network structure inference from gene knock-out expression data
TL;DR: A novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information (PCA -CMI).
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On pinning reachability of probabilistic Boolean control networks
TL;DR: Dear editor, Boolean networks (BNs) were introduced by Kauffman to model complex and nonlinear biological systems and have become a powerful tool for describing, analyzing, and simulating gene regulatory networks.
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Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data
Narsis A. Kiani,Lars Kaderali +1 more
TL;DR: Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data and outperforms current state of the art methods on simulated data and on the ERBB data.
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State Space Model with hidden variables for reconstruction of gene regulatory networks.
TL;DR: This study used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion and Principle Component Analysis approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN.
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Intervention in Gene Regulatory Networks via Phenotypically Constrained Control Policies Based on Long-Run Behavior
TL;DR: Two new phenotypically constrained stationary control policies are derived to reduce the risk of visiting undesirable states in conjunction with constraints on the shift of undesirable steady-state mass so that only limited collateral damage can be introduced.
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.