title: Parameter estimation using meta-heuristics in systems biology: a comprehensive review.
abstract: This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.
authors.affiliation: The University of Nottingham, Sutton Bonington.
authors.forename: Jianyong
authors.initials: J
authors.lastname: Sun
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Garibaldi
authors.forename: Charlie
authors.initials: C
authors.lastname: Hodgman
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D053858:Metabolic Networks and Pathways; D008957:Models, Genetic; D015398:Signal Transduction; D049490:Systems Biology