Model order reduction of deterministic and stochastic gene regulatory networks.

Altwasser R, Guthke R, Vlaic S, Emmett MR, Conrad CA, Meyer-Baese A (2013) Model order reduction of deterministic and stochastic gene regulatory networks. In: Hamid R. Arabnia, Quoc-Nam Tran (eds.) Proceedings of the International Conference on Bioinformatics and Computational Biology BIOCOMP'13 13th Int. Conf. Bioinformatics and Computational Biology, Las Vegas/USA, 09/16/2012-09/19/2012, pp. 487-496.CSREA Press, USA. ISBN: 1-60132-234-8.

Abstract

The complexity of gene regulatory networks in terms of both large-scale description
as well as nonlinear models is often an obstacle for analysis purposes. Therefore,
the development of e ective model reduction techniques is of paramount importance
in the eld of systems biology. In this paper, we apply Carleman biliniarization for
model reduction for gene regulatory networks based only on gramians computations.
The method is based on the bilinear representation of weakly nonlinear systems
and Taylor's series expansion. Thus, we obtain a computational simple solution and
identify parameters that are relevant to the behavior of the system. The theoretical
results are elucidated in an illustrative example and thus shown how they can be
applied to reverse engineering design.

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