title: Using a genetic algorithm to drive a microbial ecosystem in a desirable direction.
abstract: The functioning of natural microbial ecosystems is influenced by various biotic and abiotic conditions. The careful experimental manipulation of environmental conditions can drive microbial ecosystems toward exhibiting desirable types of functionality. Such manipulations can be systematically approached by viewing them as a combinatorial optimization problem, in which the optimal configuration of environmental conditions is sought. Such an effort requires a sound optimization technique. Genetic algorithms are a class of optimization methods that should be suitable for such a task because they can deal with multiple interacting variables and with experimental noise and because they do not require an intricate understanding or modelling of the ecosystem of interest. We propose the use of genetic algorithms to drive undefined microbial ecosystems in desirable directions by combinatorially optimizing sets of environmental conditions. We tested this approach in a model system where the microbial ecosystem of a human saliva sample was manipulated in successive steps to display increasing amounts of azo dye decoloration. The results of our experiments indicated that a genetic algorithm was capable of optimizing ecosystem function by manipulating the presence or absence of a set of 10 chemical supplements. Genetic algorithms hold promise for use as a tool in environmental microbiology for the efficient control of the functioning of natural and undefined microbial ecosystems.
authors.affiliation: Department of Biological and Agricultural Engineering, University of Idaho, Moscow, ID 83844-0904, USA.
authors.forename: Frederik P J
authors.initials: FP
authors.lastname: Vandecasteele
authors.forename: Ronald L
authors.initials: RL
authors.lastname: Crawford
authors.forename: Thomas F
authors.initials: TF
authors.lastname: Hess
mesh_terms: D000465:Algorithms; D017753:Ecosystem; D004783:Environmental Microbiology; D008954:Models, Biological; D008957:Models, Genetic; D008962:Models, Theoretical