Exhaustive Search for Fuzzy Gene Networks from Microarray Data
Recent technological advances in high-throughput data collection allow for the study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are required to interpret large and complex data sets. Rationally designed system perturbations (e.g. gene knock-outs, metabolite removal, etc) can be used to iteratively refine hypothetical models, leading to a modeling-experiment cycle for high-throughput biological system analysis. We use fuzzy logic gene network models because they have greater resolution than Boolean logic models and do not require the precise parameter measurement needed for chemical kinetics-based modeling. The fuzzy gene network approach is tested by exhaustive search for network models describing cyclin gene interactions in yeast cell cycle microarray data, with preliminary success in recovering interactions predicted by previous biological knowledge and other analysis techniques. Our goal is to further develop this method in combination with experiments we are performing on bacterial regulatory networks.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 15007882
- Report Number(s):
- UCRL-JC-154129; TRN: US200423%%85
- Resource Relation:
- Conference: 25th Annual International Conference of the Institute for Electrical and Electronics Engineers Engineering in Medicine and Biology Society, Cancun (MX), 09/17/2003--09/21/2003; Other Information: PBD: 7 Jul 2003
- Country of Publication:
- United States
- Language:
- English
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