A model-based optimization framework for the inference on gene regulatory networks from DNA array data

Identification of the regulatory structures in genetic networks and the formulation of mechanistic models in the form of wiring diagrams is one of the significant objectives of expression profiling using DNA microarray technologies and it requires the development and application of identification frameworks. Authors have developed a novel optimization framework for identifying regulation in a genetic network using the S-system modeling formalism. It has been shown that balance equations on both mRNA and protein species led to a formulation suitable for analyzing DNA-microarray data whereby protein concns. have been eliminated and only mRNA relative concns. are retained. Using this formulation, authors examd. if it is possible to infer a set of possible genetic regulatory networks consistent with obsd. mRNA expression patterns. Two origins of changes in mRNA expression patterns were considered. One derives from changes in the biophys. properties of the system that alter the mol.-interaction kinetics and/or message stability. The second is due to gene knock-outs. Authors reduced the identification problem to an optimization problem (of the so-called mixed-integer non-linear programming class) and developed an algorithmic procedure for solving this optimization problem. Using simulated data generated by our math. model, it has been shown that the method can actually find the regulatory network from which the data were generated. Authors also show that the no. of possible alternate genetic regulatory networks depends on the size of the dataset (i.e. no. of expts.), but this dependence is different for each of the two types of problems considered, and that a unique soln. requires fewer datasets than previously estd. in the literature. This is the first method that also allows the identification of every possible regulatory network that could explain the data, when the no. of expts. does not allow identification of unique regulatory structure. [on SciFinder (R)]

Published in:
Bioinformatics, 20, 17, 3221-3235
3-1 FIELD Section Title:Biochemical Genetics
Department of Industrial Engineering and Management Science,Northwestern University,Evanston,IL,USA. FIELD URL:
written in English.

Note: The status of this file is: EPFL only

 Record created 2007-01-11, last modified 2019-03-16

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