Emergence of cell migration and aggregation strategies in a simulated evolutionary process

Dirk Drasdo and Matthias Kruspe

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Submission date: 31. Aug. 2004
published in: Advances in complex systems, 8 (2005) 2/3, p. 285-318 
DOI number (of the published article): 10.1142/S0219525905000415
Bibtex
MSC-Numbers: 68Q80, 82C22, 82C80, 91-XX
PACS-Numbers: 87.23.-n, 87.23.Kg, 87.18.-h, 97.18.Bb
Keywords and phrases: cell migration, cell aggregation, simulated evolution

Abstract:
Despite the spectacular progress in biophysics, molecular biology and biochemistry our ability to predict the dynamic behavior of multicellular systems under different conditions is very limited. An important reason for this is that still not enough is known about how cells change their physical and biological properties by genetic or metabolic regulation, and which of these changes affect the cell behavior. The rules that underly the regulation processes have been determined on the time scale of evolution, by selection on the phenotypic level of cells or cell populations. We illustrate by in silico simulations how cell behavior controlled by regulatory networks may develop as a consequence of an artificial evolutionary process, if either the cells, or populations of cells are subject to selection on particular features. Thereby our concept may be a first step to facilitate the prediction of which cell behavior may be best adapted to which specific environment. We consider two examples, migration strategies of single cells searching a signal source, or aggregation of two or more cells. Both can for example be found in the life cycle of Dictyostelium discoideum. The regulatory networks are represented by Boolean networks and encoded by binary strings. The latter may be considered as encoding the genetic information (the genotype) and are subject to mutations and crossovers. The cell behavior reflects the phenotype. We find that the networks that are selected during the artificial evolutionary process encode naturally found migration and aggregation strategies such as a random walk, systematic deterministic search, and chemotaxis. In a further step our concept may also be a useful starting point to study the principles underlying the emergence of the functional building blocks of regulatory networks.