Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis

• Published in: Bioinformatics Vol. 27; no. 19; pp. 2686 - 2691
• Main Authors: Li, Zheng, Li, Ping, Krishnan, Arun, Liu, Jingdong
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.10.2011
• Subjects: Algorithms; Bayes Theorem; Biological and medical sciences; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; General aspects; Genes, BRCA1; HeLa Cells; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Oligonucleotide Array Sequence Analysis; Saccharomyces cerevisiae - genetics; Sensitivity and Specificity; Gene; Dynamics; Differential equation; Network analysis; Bayes network; Gene regulatory network; Inference; Large scale; Dynamic model
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btr454

Motivation: Reverse engineering gene regulatory networks, especially large size networks from time series gene expression data, remain a challenge to the systems biology community. In this article, a new hybrid algorithm integrating ordinary differential equation models with dynamic Bayesian network analysis, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), was proposed and implemented for gene regulatory network inference. Results: The performance of DELDBN was benchmarked with an in vivo dataset from yeast. DELDBN significantly improved the accuracy and sensitivity of network inference compared with other approaches. The local causal discovery algorithm implemented in DELDBN also reduced the complexity of the network inference algorithm and improved its scalability to infer larger networks. We have demonstrated the applicability of the approach to a network containing thousands of genes with a dataset from human HeLa cell time series experiments. The local network around BRCA1 was particularly investigated and validated with independent published studies. BRAC1 network was significantly enriched with the known BRCA1-relevant interactions, indicating that DELDBN can effectively infer large size gene regulatory network from time series data. Availability: The R scripts are provided in File 3 in Supplementary Material. Contact: zheng.li@monsanto.com; jingdong.liu@monsanto.com Supplementary information: Supplementary data are available at Bioinformatics online.