BIO-LGCA: a cellular automaton modelling class for analysing collective cell migration (original) (raw)
1Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics - understood as population behaviour arising from the interplay of the constituting discrete cells - can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems.To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell a...
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Collective dynamics of migrating cell populations drive key processes in tissue formation and maintenance under normal and diseased conditions. Collective cell behavior at the tissue level is typically characterized by considering cell density patterns such as clusters and moving cell fronts. However, there are also important observables of collective dynamics related to individual cell behavior. In particular, individual cell trajectories are footprints of emergent behavior in populations of migrating cells. Lattice-gas cellular automata (LGCA) have proven successful to model and analyze collective behavior arising from interactions of migrating cells. There are well-established methods to analyze cell density patterns in LGCA models. Although LGCA dynamics are defined by cell-based rules, individual cells are not distinguished. Therefore, individual cell trajectories cannot be analyzed in LGCA so far. Here, we extend the classical LGCA framework to allow labeling and tracking of i...
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A.1. States in Lattice-Gas Cellular Automata A.2. Dynamics in Lattice-Gas Cellular Automata Appendix B Appendix C Appendix D References Understanding the precise interplay of moving cells with their typically heterogeneous environment is crucial for central biological processes as embryonic morphogenesis, wound healing, immune reactions or tumor growth. Mathematical models allow for the analysis of cell migration strategies involving complex feedback mechanisms between the cells and their microenvironment. Here, we introduce a cellular automaton (especially lattice-gas cellular automaton-LGCA) as a microscopic model of cell migration together with a (mathematical) tensor characterization of different biological environments. Furthermore, we show how mathematical analysis of the LGCA model can yield an estimate for the cell dispersion speed within a given environment. Novel imaging techniques like diffusion tensor imaging (DTI) may provide tensor data of biological microenvironments. As an application, we present LGCA simulations of a proliferating cell population moving in an external field defined by clinical DTI data. This system can serve as a model of in vivo glioma cell invasion.
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