title: Multi-objective mixed integer strategy for the optimisation of biological networks.
abstract: In this contribution, the authors consider multi-criteria optimisation problems arising from the field of systems biology when both continuous and integer decision variables are involved. Mathematically, they are formulated as mixed-integer non-linear programming problems. The authors present a novel solution strategy based on a global optimisation approach for dealing with this class of problems. Its usefulness and capabilities are illustrated with two metabolic engineering case studies. For these problems, the authors show how the set of optimal solutions (the so-called Pareto front) is successfully and efficiently obtained, providing further insight into the systems under consideration regarding their optimal manipulation.
authors.affiliation: Process Engineering Group, Vigo, SpainUniversity of Bayreuth, Department of Computer Science, Bayreuth, Germany. julio@iim.csic.es
authors.forename: J O H
authors.initials: JO
authors.lastname: Sendín
authors.forename: O
authors.initials: O
authors.lastname: Exler
authors.forename: J R
authors.initials: JR
authors.lastname: Banga
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome