Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network

Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as...

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Bibliographic Details
Published in:BMC bioinformatics 2012-06, Vol.13 (1), p.131-131, Article 131
Main Authors: Xuan, Nguyen Vinh, Chetty, Madhu, Coppel, Ross, Wangikar, Pramod P
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Bayes Theorem
Bayesian analysis
Bioinformatics
Computational Biology - methods
Computer programs
Cyanobacteria - genetics
Data processing
Gene expression
Gene Expression Profiling - methods
Gene Regulatory Networks
Genes
Genetic algorithms
Information theory
Learning
Mathematical models
Monte Carlo simulation
Optimization
Physiological aspects
Reverse engineering
Stochasticity
Studies
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Summary:Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-13-131