Automatic reverse engineering algorithm for drug gene regulating networks

Authors:
Alexandru George Floares
Affiliations:
Oncological Institute Cluj-Napoca, Cluj-Napoca, Transilvania, Romania
Venue:
ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Year:
2007

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Abstract

Automatically inferring gene regulating networks models from microarray time series data is one of the most challenging tasks of bioinformatics. The ordinary differential equations models are the most sensible, but very difficult to build. We introduced the more general concept of drug gene regulating networks, where the regulation is exerted also by drugs. We proposed a reverse engineering algorithm for (drug) gene regulating networks, based on genetic programming - RODES. RODES automatically discovers the structure, estimate the parameter, and identify the molecular mechanisms involved. It starts from experimental or simulated microarray time series data and produces systems of ordinary differential equations. We tested RODES on simulated data, and the accuracy and the CPU time of the results were very good. This is mainly due to the possibility of incorporating a priori knowledge, and to reducing the problem of reversing an ordinary differential equations system to that of reversing individual algebraic equations. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming, applicable to large (drug) gene regulating networks. We suggest that the algorithm can reverse engineer systems of ordinary differential equations in any scientific field with a proper use of domain knowledge.