title: Parameter estimation with a novel gradient-based optimization method for biological lattice-gas cellular automaton models.
abstract: Lattice-gas cellular automata (LGCAs) can serve as stochastic mathematical models for collective behavior (e.g. pattern formation) emerging in populations of interacting cells. In this paper, a two-phase optimization algorithm for global parameter estimation in LGCA models is presented. In the first phase, local minima are identified through gradient-based optimization. Algorithmic differentiation is adopted to calculate the necessary gradient information. In the second phase, for global optimization of the parameter set, a multi-level single-linkage method is used. As an example, the parameter estimation algorithm is applied to a LGCA model for early in vitro angiogenic pattern formation.
authors.affiliation: Department for Innovative Methods of Computing, Center for Information Services and High Performance Computing, Technische Universität Dresden, Nöthnitzer Strasse 46, 01062 Dresden, Germany. carsten.mente@tu-dresden.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Mente
authors.forename: Ina
authors.initials: I
authors.lastname: Prade
authors.forename: Lutz
authors.initials: L
authors.lastname: Brusch
authors.forename: Georg
authors.initials: G
authors.lastname: Breier
authors.forename: Andreas
authors.initials: A
authors.lastname: Deutsch
mesh_terms: D000465:Algorithms; D002477:Cells; D003198:Computer Simulation; D008954:Models, Biological; D018919:Neovascularization, Physiologic