Structural Identification of Piecewise-Linear Models of Genetic Regulatory Networks

We present a method for the structural identification of genetic regulatory networks (GRNs), based on the use of a class of Piecewise-Linear (PL) models. These models consist of a set of decoupled linear models describing the different modes of operation of the GRN and discrete switches between the modes accounting for the nonlinear character of gene regulation. They thus form a compromise between the mathematical simplicity of linear models and the biological expressiveness of nonlinear models. The input of the PL identification method consists of time-series measurements of concentrations of gene products. As output it produces estimates of the modes of operation of the GRN as well as all possible minimal combinations of threshold concentrations of the gene products accounting for switches between the modes of operation. The applicability of the PL identification method has been evaluated using simulated data obtained from a model of the carbon starvation response in the bacterium E.coli. This has allowed us to systematically test the performance of the method under different data characteristics, notably variations in the noise level and the sampling density.

Published in:
Journal of Computational Biology, 15, 10, 1365-1380
Mary Ann Liebert, Inc. 2 Madison Avenue Larchmont, NY 10538 USA

 Record created 2017-01-10, last modified 2018-09-13

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