Genetic Algorithm Applied to Simultaneous Parameter Estimation in Bacterial Growth

HECTOR A., PEDROZO; DALLAGNOL, ANDREA M.; SCHVEZOV, CARLOS E.

Journal of Bioinformatics and Computational Biology

World Scientific Publishing Co. Pte Ltd

Año: 2020

0219-7200

Several mathematical models have been developed to understand the interactions of microorganismsin foods and predict their growth. The resulting model equations for the growth of interacting cellsinclude several parameters that must be determined for the specific conditions to be modeled. In thepresent report, these parameters were determined by using inverse engineering and a multi-objectiveoptimization procedure that allows fitting more than one experimental growth curve simultaneously.A genetic algorithm 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 was applied to threeexperimental data set of simultaneous growth of lactic acid bacteria (LAB) and Listeriamonocytogenes (LM). Then, the proposed method was compared with a conventional mono-objectivesequential fit. We concluded that the multi-objective fit by the genetic algorithm gives superior resultswith lower standard errors and standard deviations than the conventional sequential approach.