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.
Paton
Ray
R
BioComputing and Computational Biology Research Group, Department of Computer Science, University of Liverpool, Liverpool L69 7ZF, UK.
Gregory
Richard
R
Vlachos
Costas
C
Saunders
Jon
J
Wu
Henry
H
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