Fuzzy system methods in modeling gene expression and analyzing protein networks

Abstract

Recent technological advances in high-throughput data collection allow for computational study of complex biological systems on the scale of the whole cellular genome and proteome. Gene regulatory network is expected to be one of suitable tools for interpreting the resulting large amount of genomic and proteomic data sets. A huge number of methods have been developed for extracting gene networks from such data. Fuzzy logic which plays an important role in multiple disciplines is a framework bringing together physics-based models with more logical methods to build a foundation for multi-scale bio-molecular network models. Biological relationships in the best-fitting fuzzy gene network models can successfully recover direct and indirect interactions from previous knowledge to result in more biological insights about regulatory and transcriptional mechanism. In this chapter, we survey a class of models based on fuzzy logic with particular applications in reconstructing gene regulatory networks. We also extend our survey of the application of fuzzy logic methods to highly related topics such as protein interaction network analysis and microarray data analysis. We believe that fuzzy logic-based models would take a key step towards providing a framework for integrating, analyzing and modeling complex biological systems. © 2009 Springer-Verlag Berlin Heidelberg.