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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Journal ArticleDOI
On state feedback asymptotical stabilization of probabilistic Boolean control networks
Xinrong Yang,Haitao Li +1 more
TL;DR: In this article , a class of complete family of reachable sets (CFRSs) is constructed to stabilize PBCNs to a given control-fixed point in distribution, and all possible state feedback controllers are designed to stabilize the PBCN.
DissertationDOI
Computational Systems Biology Methods for Functional Classification of Membrane Proteins and Modeling of Quorum Sensing in Pseudomonas aeruginosa
TL;DR: It was shown that including further information in the amino acid composition and filtering into different sequence regions improved the classification quality, and a ranking method was developed to discriminate between functional classes.
Book ChapterDOI
A Survey of Computational Approaches to Reconstruct and Partition Biological Networks
TL;DR: Two problems that have received a considerable amount of attention among researchers are reverse engineering of biological networks from genome-wide measurements and inference of functional units in large biological networks.
Book ChapterDOI
Designing Microarray Experiments
TL;DR: This chapter describes some of the major issues related to the design of either randomized control trials or observational studies and discusses the choice of powerful sample sizes, the selection of informative experimental conditions, and experimental strategies that can minimize confounding.
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.