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Experiment design for optimal excitation of gene regulatory networks
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
2006 (English)Conference paper, Abstract (Refereed)
Abstract [en]

Identification of gene regulatory networks from quantitative data has attracted significant interest in recent years. The focus has mainly been on determining model structures and algorithms for fitting experimental data, while the problem of obtaining suitable experimental data largely has been neglected. In this work we focus on the problem of systematically designing in vivo/in vitro experiments that will yield the information needed to determine both the structure and dynamics of biochemical networks. As a first approximation we consider linear dynamic models valid in a particular physiological state. We propose an iterative design strategy, where selection of the perturbation, sampling time and number of samples in each experiment is based on available partial information about the system, i.e. an ill-conditioned or rank deficient measurement matrix. Three different sources of such deficiency exist: (i) unidirectionality intrinsic to the system, due to moiety conservation or strongly correlated variables, (ii) fast dynamic modes and (iii) incomplete excitation of the system. The former two can be identified and ᅵlifted outᅵ of the measurement matrix, while the latter require additional experimental data. Our experiment design strategy endeavours in each step to provide information perpendicular to the existing one. When all directions of the state space, spanned by the gene network, are present in the measurements matrix, the design emphasizes those directions where the least information has been obtained. Existing optimum design strategies are based on maximization of some measure of the Fisher information matrix (FIM). An a priori model of the system is needed to determine the FIM and hence good prior knowledge of the system is essential. Otherwise the design will give slow convergence, corresponding to an excessive number of experiments. Our approach requires no prior information and its effectiveness is here demonstrated through identification of in silico networks previously proposed in the literature.

Place, publisher, year, edition, pages
Keyword [en]
Experiment design, System identification, Systems Biology
National Category
Control Engineering Bioinformatics and Systems Biology
URN: urn:nbn:se:kth:diva-80741OAI: diva2:496840
7th International Conference on Systems Biology (ICSB-2006). Yokohama, Japan. October 9-13 2006
QC 20120525Available from: 2012-02-10 Created: 2012-02-10 Last updated: 2012-05-25Bibliographically approved

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Nordling, Torbjörn E. M.Jacobsen, Elling W.
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