title: Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm.
abstract: An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities.
authors.affiliation: Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune 411008, India; Centre of Excellence in Scientific Computing, (CoESC), CSIR-NCL, Pune, India; Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, India. Electronic address: kd.sarode@ncl.res.in.
authors.forename: Ketan Dinkar
authors.initials: KD
authors.lastname: Sarode
authors.affiliation: Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune 411008, India; Centre of Excellence in Scientific Computing, (CoESC), CSIR-NCL, Pune, India; Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, India. Electronic address: v.ravikumar@ncl.res.in.
authors.forename: V Ravi
authors.initials: VR
authors.lastname: Kumar
authors.affiliation: Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune 411008, India; Centre of Excellence in Scientific Computing, (CoESC), CSIR-NCL, Pune, India; Academy of Scientific and Innovative Research (AcSIR), CSIR-NCL Campus, Pune, India. Electronic address: bd.kulkarni@ncl.res.in.
authors.forename: B D
authors.initials: BD
authors.lastname: Kulkarni
mesh_terms: D000465:Algorithms; D000818:Animals; D002940:Circadian Rhythm; D000242:Cyclic AMP / Q000378:metabolism; D004023:Dictyostelium / Q000378:metabolism; D004330:Drosophila / Q000378:metabolism; D029721:Drosophila Proteins / Q000378:metabolism; D053263:Gene Regulatory Networks; D055641:Mathematical Concepts; D008954:Models, Biological; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D056950:Period Circadian Proteins / Q000378:metabolism; D049490:Systems Biology
chemical_list: D029721:Drosophila Proteins; C539040:PER protein, Drosophila; D056950:Period Circadian Proteins; D000242:Cyclic AMP
keywords: Genetic algorithm; Inverse problem; Multiple shooting; Parameter estimation; S-system; Structure identification