Parameter estimation with a novel gradient-based optimization method for biological lattice-gas cellular automaton models |
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| Authors: | Carsten Mente Ina Prade Lutz Brusch Georg Breier Andreas Deutsch |
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| Institution: | 1.Department for Innovative Methods of Computing, Center for Information Services and High Performance Computing,Technische Universit?t Dresden,Dresden,Germany;2.Institute of Pathology, Universit?tsklinikum Carl Gustav Carus,Technische Universit?t Dresden,Dresden,Germany |
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| 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. |
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