Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks
Reads0
Chats0
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
More filters
Journal ArticleDOI
On detectability of Boolean control networks
TL;DR: In this paper, the authors introduce the detectability notions and investigate the properties of Boolean control networks (BCNs) and Boolean networks (BNs) using the semi-tensor product of matrices.
Journal ArticleDOI
Minimum-Time State Feedback Stabilization of Constrained Boolean Control Networks
Haitao Li,Yuzhen Wang +1 more
TL;DR: The winner of the China.Ministry in 2005, and the National Prize of Natural Science of China in 2008.
Journal ArticleDOI
An extended gene protein/products boolean network model including post-transcriptional regulation
TL;DR: The extended model proposed in this paper opens new opportunities in the study of gene regulation by showing how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN.
Journal ArticleDOI
Optimal Intervention Strategies for Therapeutic Methods With Fixed-Length Duration of Drug Effectiveness
TL;DR: A unified approach to both cyclic and acyclic control with duration of effectiveness is taken by placing the problem in the general framework of multiperiod decision epochs with infinite horizon discounting cost.
Journal ArticleDOI
A survey of gene regulatory networks modelling methods: from differential equations, to Boolean and qualitative bioinspired models
TL;DR: The aim is to introduce the reader to the problem of GRN modelling and to guide her/him along the path that goes from classical quantitative methods, through qualitative methods based on Boolean network, up to some of the most relevant qualitative computational methods to understand the advantages and limitations of the different approaches.
References
More filters
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.