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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Book ChapterDOI
Random Boolean Networks
TL;DR: In this chapter, a brief introduction to random Boolean networks is given and the results are abundant, so a detailed discussion is beyond the scope of this book.
Journal ArticleDOI
A Survey on Logical Control Systems
Daizhan Cheng,Ting Liu +1 more
TL;DR: This paper gives a comprehensive introduction to logical control systems (LCSs), including its history, the logical background, the expression, the fundamental mathematical tool, and the analyzing method, which form the foundation of logical control system theory (LCST).
Journal ArticleDOI
Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism.
TL;DR: It is shown that the Boolean networks, which allow avoiding the complexity of general kinetic modelling, preserve the possibility of reproducing the principal switching mechanism of 6-mercaptopurine.
Journal ArticleDOI
Modelling codependence in biological systems.
TL;DR: A novel formalism, codependence modelling, which seeks to combine the needs of the biologist with the mathematical rigour required to support computer simulation of dynamics is proposed here, thus integrating both metabolic and gene regulatory processes within a single conceptual schema.
Proceedings ArticleDOI
Fast Network Component Analysis for Gene Regulation Networks
TL;DR: This paper proposes several fast, non-iterative NCA algorithms based on matrix computation that demonstrate good performance when applied to a hypothetical and a real gene regulation network.
References
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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.