

Preprint 56/2004
Emergence of cell migration and aggregation strategies in a simulated evolutionary process
Dirk Drasdo and Matthias Kruspe
Contact the author: Please use for correspondence this email.
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.