Parameter estimation with a novel gradient-based optimization method for biological lattice-gas cellular automaton models.
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.
Mente
Carsten
C
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
Prade
Ina
I
Brusch
Lutz
L
Breier
Georg
G
Deutsch
Andreas
A
D000465:Algorithms; D002477:Cells; D003198:Computer Simulation; D008954:Models, Biological; D018919:Neovascularization, Physiologic