Inferring regulatory networks
Huai Li 1, Jianhua Xuan2, Yue Wang2, Ming Zhan1
1
Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD, USA, 2Department of Electrical, Computer, and Biomedical Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
TABLE OF CONTENTS
- 1. Abstract
- 2. Introduction
- 3. Computational Approaches for Identifying Gene Modules
- 3.1. Advanced Statistical Approaches
- 3.2. Matrix Decomposition Approaches
- 4. Computational Approaches for Inferring Gene Connectivity
- 4.1. ODE-based Models
- 4.2. Bayesian Networks
- 4.3. Coexpression Networks
- 4.4. Probabilistic Boolean Networks
- 4.5. Inference from Multiple Sources of Data
- 5. Network Analysis in Silico
- 5.1. Steady State Analysis by Markov Chain Simulation
- 5.2. Intervention Analysis by Markov Chain Model
- 6. Closing Remarks
- 7. Acknowledgments
- 8. References
1. ABSTRACT
The discovery of regulatory networks is an important aspect in the post genomic research. The process requires integrated efforts of experimental and computational strategies by employing the systems biology approach. This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other data sources, including transcriptional module identification, network topology inference, and network analysis. Popular solutions to each of these problems and their relative merits are discussed.
