AUTHOR=Reali Federico , Priami Corrado , Marchetti Luca TITLE=Optimization Algorithms for Computational Systems Biology JOURNAL=Frontiers in Applied Mathematics and Statistics VOLUME=3 YEAR=2017 URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2017.00006 DOI=10.3389/fams.2017.00006 ISSN=2297-4687 ABSTRACT=

Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology.