Since its first edition in 1994, the ACRI conference focused on challenging problems and new research on Cellular Automata (CA). Its primary goal is to offer both scientists and engineers in academies and industries an opportunity to enforce international collaborations on Cellular Automata and express their views on current trends, challenges, and state-of-the art solutions to various problems in several scientific fields: biology, computer science, chemistry, ecology, economy, engineering, geology, medicine, physics, sociology, etc.
The main conference will take place from September 19 to September 21, 2018. It will be preceeded by one day of workshops an the First Intensive School on Cellular Automata.
Invited Speakers
Raúl Rechtman
Instituto de Energias Renovables
Universidad Nacional Autónoma de México
Phase transitions of cellular automata
Andreas Deutsch
Centre for Information Services and High Performance Computing (ZIH)
Technische Universität Dresden
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Head of department “Methods of Innovative Computing”, Centre for Information Services and High Performance Computing, Technische Universität Dresden; studied mathematics and biology at the Universities of Mainz (Germany) and Bergen (Norway), PhD from the University of Bremen (Germany). Research interests: mathematical and systems biology, biological self-organisation, cancer, collective migration, cellular automata. Author of monograph on cellular automata (Birkhäuser, Boston, 2018: 2nd edition). Cofounder of intercultural music project DHUN (
www.dhun-music.net).
Biological lattice-gas cellular automaton models for the analysis of collective behaviour in interacting cell populations
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As a cellular automaton, a BIO-LGCA is defined on a regular lattice, where the nodes of the lattice take a certain number of discrete states. As a lattice-gas, the state space of a BIO-LGCA is related to the lattice geometry. Each node can be occupied by ``biological agents'', e.g. biological cells, characterised by their velocities which are restricted to the unit vectors connecting a node to its nearest neighbors. Agents move along the links and interact on the nodes of the lattice. This interaction can change the number of agents at individual nodes (birth/death processes) and may depend on the states in neighbouring nodes which allows to model collective effects. Meanwhile, the BIO-LGCA has been established as discrete lattice- and agent-based model which permits multi-scale analysis and efficient large-simulations. We provide BIO-LGCA model examples for single and collective cell migration as well as problems motivated by cancer invasion.
Ref.:
Deutsch, A., Dormann, S.: Cellular automaton modeling of biological pattern formation: characterization, applications, and analysis. Birkhauser, Boston, 2018
