Comparison of global optimization methods for parameter estimation in biochemical networks.
Authorship
A.P.R.
Master in Industrial Mathematics
A.P.R.
Master in Industrial Mathematics
Defense date
01.24.2025 10:00
01.24.2025 10:00
Summary
This study evaluates the performance of various global optimization methods for parameter estimation in biochemical networks, a critical task in Computational Systems Biology. Deterministic and stochastic algorithms are compared using a set of standard optimization problems and four benchmark challenges specific to Systems Biology (the BioPreDyn benchmark set). The goal is to identify the most effective and reliable methods for addressing the non-convex optimization problems that frequently arise in this field. The study’s most significant finding is that the “enhanced Scatter Search” (eSS) method demonstrated the highest reliability in solving Systems Biology optimization problems among the tested metaheuristics. While no single algorithm excelled in all cases, eSS consistently achieved the greatest reduction in the objective function value and demonstrated superior robustness overall. Deterministic methods proved unsuitable for large-scale problems, highlighting their limitations in such contexts. This study highlights the importance of selecting appropriate optimization algorithms for parameter estimation in modeling biochemical networks. It further emphasizes the efficacy of certain metaheuristics in addressing the complex optimization problems that arise in Systems Biology.
This study evaluates the performance of various global optimization methods for parameter estimation in biochemical networks, a critical task in Computational Systems Biology. Deterministic and stochastic algorithms are compared using a set of standard optimization problems and four benchmark challenges specific to Systems Biology (the BioPreDyn benchmark set). The goal is to identify the most effective and reliable methods for addressing the non-convex optimization problems that frequently arise in this field. The study’s most significant finding is that the “enhanced Scatter Search” (eSS) method demonstrated the highest reliability in solving Systems Biology optimization problems among the tested metaheuristics. While no single algorithm excelled in all cases, eSS consistently achieved the greatest reduction in the objective function value and demonstrated superior robustness overall. Deterministic methods proved unsuitable for large-scale problems, highlighting their limitations in such contexts. This study highlights the importance of selecting appropriate optimization algorithms for parameter estimation in modeling biochemical networks. It further emphasizes the efficacy of certain metaheuristics in addressing the complex optimization problems that arise in Systems Biology.
Direction
López Pouso, Óscar (Tutorships)
López Pouso, Óscar (Tutorships)
Court
VAZQUEZ CENDON, MARIA ELENA (Coordinator)
VAZQUEZ CENDON, MARIA ELENA (Chairman)
Carretero Cerrajero, Manuel (Secretary)
ARREGUI ALVAREZ, IÑIGO (Member)
VAZQUEZ CENDON, MARIA ELENA (Coordinator)
VAZQUEZ CENDON, MARIA ELENA (Chairman)
Carretero Cerrajero, Manuel (Secretary)
ARREGUI ALVAREZ, IÑIGO (Member)