title: Maximizing microbial perchlorate degradation using a genetic algorithm: consortia optimization.
abstract: Microorganisms in consortia perform many tasks more effectively than individual organisms and in addition grow more rapidly and in greater abundance. In this work, experimental datasets were assembled consisting of all possible selected combinations of perchlorate reducing strains of microorganisms and their perchlorate degradation rates were evaluated. A genetic algorithm (GA) methodology was successfully applied to define sets of microbial strains to achieve maximum rates of perchlorate degradation. Over the course of twenty generations of optimization using a GA, we saw a statistically significant 2.06 and 4.08-fold increase in average perchlorate degradation rates by consortia constructed using solely the perchlorate reducing bacteria (PRB) and by consortia consisting of PRB and accompanying organisms that did not degrade perchlorate, respectively. The comparison of kinetic rates constant in two types of microbial consortia additionally showed marked increases.
authors.affiliation: Environmental Biotechnology Institute, Environmental Science Program, University of Idaho, Moscow, ID, 83844, USA. kkucharzyk@vandals.uidaho.edu
authors.forename: Katarzyna H
authors.initials: KH
authors.lastname: Kucharzyk
authors.forename: Terence
authors.initials: T
authors.lastname: Soule
authors.forename: Thomas F
authors.initials: TF
authors.lastname: Hess
mesh_terms: D000064:Acclimatization; D000465:Algorithms; D001419:Bacteria / Q000378:metabolism*; D001673:Biodegradation, Environmental; D002704:Chlorates / Q000302:isolation & purification*; D007700:Kinetics; D059013:Microbial Consortia; D008957:Models, Genetic; D010472:Perchlorates / Q000302:isolation & purification* / Q000378:metabolism*; D025341:Principal Component Analysis; D013997:Time Factors
chemical_list: D002704:Chlorates; D010472:Perchlorates; C494474:perchlorate