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Experimental design for system identi cation of boolean control networks in biology


A.G. Busetto, J. Lygeros

IEEE Conference on Decision and Control, Los Angeles, CA, USA, Dec 15-17, 2014

This study is primarily motivated by biological applications and focuses on the identification of Boolean net- works from scarce and noisy data. We consider two Bayesian experimental design scenarios: selection of the observations under a budget, and input design. The goal is to maximize the mutual information between models and data, that is the ultimate statistical upper bound on the identifiability of a system from empirical data. First, we introduce a method to select which components of the state variable to measure under a budget constraint, and at which time points. Our greedy approach takes advantage of the submodularity of the mutual information, and hence requires only a polynomial number of evaluations of the objective to achieve near-optimal designs. Second, we consider the computationally harder task of designing sequences of input interventions, and propose a likelihood-free approximation method. Exact and approximate design solutions are verified with predictive models of genetic regulatory interaction networks in embryonic development


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% Autogenerated BibTeX entry
@InProceedings { BusLyg:2014:IFA_5095,
    author={A.G. Busetto and J. Lygeros},
    title={{Experimental design for system identi cation of boolean
	  control networks in biology}},
    booktitle={IEEE Conference on Decision and Control},
    address={Los Angeles, CA, USA, Dec 15-17, 2014},
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