Paper Info
Title
Computational Intelligence for Parameter Estimation of Biochemical Systems
Abstract
In the field of Systems Biology, simulating the dynamics of biochemical models represents one of the most effective methodologies to understand the functioning of cellular processes in normal or altered conditions. However, the lack of kinetic rates, necessary to perform accurate simulations, strongly limits the scope of these analyses. Parameter Estimation (PE), which consists in identifying a proper model parameterization, is a non-linear, non-convex and multi-modal optimization problem, typically tackled by means of Computational Intelligence techniques, such as Evolutionary Computation and Swarm Intelligence. In this work, we perform a thorough investigation of the most widespread methods for PE-namely, Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution (DE), Estimation of Distribution Algorithm (EDA), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Fuzzy Self-Tuning PSO (FST-PSO)-comparing their performances on a set of synthetic (yet realistic) biochemical models of increasing size and complexity. Our results show that a variant of the settings-free FST-PSO algorithm can consistently outperform all other methods; ABC and GAs represent the most performing alternatives, while methods based on multivariate normal distributions (e.g., CMA-ES, EDA) struggle to keep pace with the other approaches. Index Terms-Computational Intelligence, Systems Biology, Parameter Estimation, Evolutionary Computation, Swarm Intelligence.
Year
DOI
Venue
2018
10.1109/CEC.2018.8477873
2018 IEEE Congress on Evolutionary Computation (CEC)
Keywords
Field
DocType
Parameter Estimation,biochemical Systems,Systems Biology,biochemical models,cellular processes,multimodal optimization problem,Computational Intelligence techniques,Artificial Bee Colony,ABC,Covariance Matrix Adaptation Evolution Strategy,CMA-ES,Differential Evolution,Genetic Algorithms,multivariate normal distributions,Index Terms-Computational Intelligence,nonlinear optimization problem,nonconvex optimization problem,estimation of distribution algorithm,particle swarm optimization,PSO,fuzzy self-tuning PSO,FST-PSO
Particle swarm optimization,Mathematical optimization,Computational intelligence,Estimation of distribution algorithm,Computer science,Swarm intelligence,Evolutionary computation,Evolution strategy,Artificial intelligence,CMA-ES,Machine learning,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-5090-6018-4
2
0.40
References 
Authors
16
7
Name
Order
Citations
PageRank
Marco S. Nobile114323.69
Andrea Tangherloni2407.88
Leonardo Rundo3203.55
Simone Spolaor494.97
Daniela Besozzi539139.10
Giancarlo Mauri62106297.38
Paolo Cazzaniga723527.16