Multi-criterion optimization for genetic network modeling

A major problem associated with the reverse engineering of genetic networks from micro-array data is how to reliably find genetic interactions when faced with a relatively small number of arrays compared to the number of genes. To cope with this dimensionality problem, it is imperative to employ additional (biological) knowledge about real genetic networks, such as limited connectivity, redundancy, stability and robustness, to sensibly constrain the modeling process. In previous work (Proceedings of the 2001 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, Baltimore, MA, June 2001; Proceedings of the Second International Conference on Systems Biology, Pasadena, CA, November 2, pp. 222-230), we have shown that by applying single constraints, the inference of genetic interactions under realistic conditions can be significantly improved. Recently (Proceedings of the SPIE, San Jose, CA, January 2002), we have made a preliminary study on how these approaches based on single constraints solve the underlying bi-criterion optimization problem. In this paper, we study the problem of how multiple constraints can be combined by formulating genetic network modeling as a multi-criterion optimization problem. Results are shown on artificial as well as on a real data example.

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