A software for parameter estimation in dynamic models

Abstract

-A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

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“…Table 1A provides a sense of the compatibility of some key analytical tools for a broad variety of model classes. To construct this table, we relied on an extensive literature survey of each model class and tool (White, 1977; Dytham, 1995; Bengtsson et al, 1996; Henzinger et al, 1997; Cozman, 1997; Ghosh and Tomlin, 2001; Alur et al, 2001; Bandini et al, 2001; Barton and Lee, 2002; Sutner, 2002, 2009; Wang et al, 2002; Antoniotti et al, 2003a,b; Shmulevich et al, 2003; Ghosh et al, 2003; Friedman and Koller, 2003; Lincoln and Tiwari, 2004; Janes et al, 2004; Friedman, 2004; Li and Chan, 2004; Kwiatkowska et al, 2004; Ihekwaba et al, 2004; Hagiya et al, 2004; Das et al, 2004; Pe’er, 2005; Fauré et al, 2006; Reeves et al, 2006; Langmead et al, 2006; Kim et al, 2006; Chaouiya, 2007; Saez-Rodriguez et al, 2007; Fränzle and Herde, 2007; Narasimhan and Biswas, 2007; Wilkinson, 2007; Sandmann, 2007; Mukherjee and Speed, 2008; Clarke et al, 2008; Sandmann and Wolf, 2008; Ryu et al, 2008; Donaldson and Gilbert, 2008; Figueirêdo et al, 2008; Yuceer et al, 2008; Qian and Dougherty, 2009; Tatyana et al, 2009; Wartlick et al, 2009; Sobie, 2009; Langmead, 2009; Didier et al, 2009; Müssel et al, 2010; Sarkar and Sobie, 2010; Campagna and Piazza, 2010; Bortolussi and Policriti, 2010; Garmaroudi et al, 2010; Yang and Lin, 2010; Donzé et al, 2010; Gunawardena, 2010; Vikram et al, 2010; Kobayashi and Hiraishi, 2010, 2011; Gong et al, 2010, 2011a,b,c; Dimitrova et al, 2011; Grosu et al, 2011; Alfieri et al, 2011; Fischer and Kaiser, 20...…”

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