Genetic algorithm applied to simultaneous parameter estimation in bacterial growth

Abstract

Several mathematical models have been developed to understand the interactions ofmicroorganisms in foods and predict their growth. The resulting model equations forthe growth of interacting cells include several parameters that must be determined forthe specific conditions to be modeled. In the present report, these parameters weredetermined by using inverse engineering and a multi-objective optimization procedurethat allows fitting more than one experimental growth curve simultaneously. A geneticalgorithm was applied to obtain the best parameter values of a model that permit theconstruction of the front of Pareto with 50 individuals or phenotypes. The method wasapplied to three experimental data sets of simultaneous growth of lactic acid bacteria(LAB) and Listeria monocytogenes (LM). Then, the proposed method was comparedwith a conventional mono-objective sequential fit. We concluded that the multiobjectivefit by the genetic algorithm gives superior results with more parameteridentifiability than the conventional sequential approach.