Experimental design for system identification of Boolean Control Networks in biology

• Published in: 53rd IEEE Conference on Decision and Control pp. 5704 - 5709
• Main Authors: Busetto, Alberto Giovanni, Lygeros, John
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Approximation methods; Biological system modeling; Data models; Mutual information; Random variables; Time measurement
• ISBN: 9781479977468; 1479977462
• ISSN: 0191-2216
• DOI: 10.1109/CDC.2014.7040282

This study is primarily motivated by biological applications and focuses on the identification of Boolean networks 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.