Parameter estimation with a novel gradient-based optimization method for biological lattice-gas cellular automaton models

Research output: Contribution to journalResearch articleContributedpeer-review


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.


Original languageEnglish
Pages (from-to)173–200
Number of pages28
JournalJournal of Mathematical Biology
Issue number1
Publication statusPublished - 2011

External IDs

Scopus 79958769055
researchoutputwizard legacy.publication#42457
ORCID /0000-0003-0137-5106/work/142244200
ORCID /0000-0002-9467-780X/work/147674924



  • Lattice-Gas, Parameter