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
Structural stability analysis of gene regulatory networks modeled by Boolean networks
Journal ArticleDOI
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells.
Julian D. Schwab,Nensi Ikonomi,Silke D. Werle,Felix M. Weidner,Hartmut Geiger,Hans A. Kestler +5 more
TL;DR: In this article, a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data is presented, which allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements.
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Robust control of uncertain context-sensitive probabilistic Boolean networks
TL;DR: The authors derive an optimal risk-sensitive controller when a GRN is modelled by a context-sensitive probabilistic Boolean network (CSPBN) by using a relation between the relative entropy and free-energy to analyse the influence of a particular attractor on the robustness of the controller.
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Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks
TL;DR: This study presents a novel algorithm for reverse engineering with linear systems that is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison and can be used to elucidate gene regulatory networks using limited number of experimental data points.
Book ChapterDOI
Soft Computing Approach for Modeling Genetic Regulatory Networks
Khalid Raza,Rafat Parveen +1 more
TL;DR: The role of soft computing techniques, such as fuzzy logic, artificial neural networks, evolutionary algorithms and their hybridization, for modeling GRNs are reviewed and various challenges and opportunities for future research are discussed.
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.