Evolvable social agents for bacterial systems modeling.
We present two approaches to the individual-based modeling (IbM) of bacterial ecologies and evolution using computational tools. The IbM approach is introduced, and its important complementary role to biosystems modeling is discussed. A fine-grained model of bacterial evolution is then presented that is based on networks of interactivity between computational objects representing genes and proteins. This is followed by a coarser grained agent-based model, which is designed to explore the evolvability of adaptive behavioral strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of the two proposed individual-based bacterial models are discussed, and some results from simulation experiments are presented, illustrating their adaptive properties.
BioComputing and Computational Biology Research Group, Department of Computer Science, University of Liverpool, Liverpool L69 7ZF, UK.
Ray
R
Paton
Richard
R
Gregory
Costas
C
Vlachos
Jon
J
Saunders
Henry
H
Wu
D000222:Adaptation, Physiological / Q000502:physiology; D001185:Artificial Intelligence; D018407:Bacterial Physiological Phenomena; D001426:Bacterial Proteins / Q000502:physiology*; D003198:Computer Simulation; D019020:Directed Molecular Evolution / Q000379:methods*; D004777:Environment; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology / Q000379:methods
D001426:Bacterial Proteins