Fuzzy Model for Gene Regulatory Network

Gene regulatory networks influence development and evolution in living organism. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Recently, a fuzzy logic model has been proposed to search microarray datasets for activator/repressor regulatory relationship. We improve this model for searching regulatory triplets by means of predicting changes in expression level of the target over interval time points based on input expression level, and comparing them with actual changes. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. We also introduce a novel pre-processing technique using fuzzy logic that can group genes having similar changes in expression profile over all available intervals in the microarray data. This technique eliminates redundant computation performed by the proposed model. Saccharomyces cerevisiae data was applied to the model and 548 activator/repressor regulatory triplets were inferred from the data. These improvements will increase feasibility of using fuzzy logic for understanding the relationship between genes using microarray technology.

[1]  Robert Reynolds,et al.  Fuzzy logic-based gene regulatory network , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[2]  Jian Gong,et al.  Modeling gene expression networks using fuzzy logic , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[4]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[5]  Reinhard Guthke,et al.  Gene Expression Data Mining for Functional Genomics , 2001 .

[6]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Aurélien Mazurie,et al.  Gene networks inference using dynamic Bayesian networks , 2003, ECCB.

[8]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[9]  Ed Keedwell,et al.  Single-layer artificial neural networks for gene expression analysis , 2004, Neurocomputing.

[10]  P. Woolf,et al.  A fuzzy logic approach to analyzing gene expression data. , 2000, Physiological genomics.

[11]  Sorin Istrail,et al.  Logic Functions of the Genomic Cis-regulatory Code , 2005, UC.

[12]  Marcel J. T. Reinders,et al.  A Comparison of Genetic Network Models , 2000, Pacific Symposium on Biocomputing.

[13]  Edward Keedwell,et al.  Discovering Gene Networks with a Neural-Genetic Hybrid , 2005, TCBB.

[14]  Richard Scheines,et al.  Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data , 2000 .