Linear fuzzy gene network models obtained from microarray data by exhaustive search

Background: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semiquantitative, does not require the precise parameter measurement needed for chemical kineticsbased modeling. Results: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, uni...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Bahrad A. Sokhansanj, J. Patrick Fitch, Judy N. Quong, Andrew A. Quong
• Keywords: BMCBI 2004, Fuzzy Gene, Gene, Gene Network Models