Optimization Algorithms for Computational Systems Biology

Frontiers in Applied Mathematics and Statistics

Priami, Corrado¹  Reali, Federico²  Marchetti, Luca² 

[1] Department of Mathematics, University of Trento, Trento, Italy;The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy

关键词: optimization;    least squares algorithms;    Markov chain Monte Carlo;    genetic algorithms;    Computational systems biology;    parameter estimation;    global optimization;    mathematical modeling;   

DOI  :  10.3389/fams.2017.00006

学科分类：数学（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY