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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Journal ArticleDOI
Adaptive intervention in probabilistic boolean networks
TL;DR: In this paper, the feasibility of applying online adaptive control to improve intervention performance in genetic regulatory networks modeled by probabilistic Boolean networks (PBNs) has been demonstrated via simulations that when the network is modeled by a known family of PBNs, an adaptive design can yield improved performance in terms of the average cost.
Proceedings ArticleDOI
Robust Intervention in Probabilistic Boolean Networks
TL;DR: A robust intervention strategy is developed by minimizing the worst-case cost over the uncertainties in the entries of the transition probability matrix of the Probabilistic Boolean networks.
Journal ArticleDOI
Generating probabilistic Boolean networks from a prescribed stationary distribution
TL;DR: This paper first formulate the inverse problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs) as a constrained least squares problem, and proposes a heuristic method based on Conjugate Gradient algorithm, an iterative method, to solve the resulting most squares problem.
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Intracellular Delivery II
TL;DR: An overview of scalable methods for industrial production of nanofibers is given in this paper, where the theoretical principles of both nozzle and nozzleless electrospinning processes are discussed, together with their predominant potential application areas.
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Inference of Gene Regulatory Networks from Time Course Gene Expression Data Using Neural Networks and Swarm Intelligence
TL;DR: A novel algorithm that combines a recurrent neural network (RNN) and two swarm intelligence (SI) methods to infer a gene regulatory network (GRN) from time course gene expression data is presented.
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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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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.