Instituto de Materiales de Misiones (CONICET-UNaM), Felix de Azara 1552, 3300 Posadas, Argentina.
J Bioinform Comput Biol. 2021 Feb;19(1):2050045. doi: 10.1142/S0219720020500456. Epub 2021 Jan 27.
Several mathematical models have been developed to understand the interactions of microorganisms in foods and predict their growth. The resulting model equations for the growth of interacting cells include several parameters that must be determined for the specific conditions to be modeled. In this study, these parameters were determined by using inverse engineering and a multi-objective optimization 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 the construction of the front of Pareto with 50 individuals or phenotypes. The method was applied to three experimental data sets of simultaneous growth of lactic acid bacteria (LAB) and (LM). Then, the proposed method was compared with a conventional mono-objective sequential fit. We concluded that the multi-objective fit by the genetic algorithm gives superior results with more parameter identifiability than the conventional sequential approach.
已经开发了几种数学模型来理解食品中微生物的相互作用并预测它们的生长。用于相互作用细胞生长的模型方程包括几个必须根据特定条件确定的参数。在这项研究中，这些参数是通过使用逆工程和多目标优化程序确定的，该程序允许同时拟合多个实验生长曲线。应用遗传算法获得允许构建具有 50 个个体或表型的 Pareto 前沿的模型的最佳参数值。该方法应用于三个同时生长的乳酸细菌（LAB）和（LM）的实验数据集。然后，将所提出的方法与传统的单目标顺序拟合进行了比较。我们得出的结论是，遗传算法的多目标拟合比传统的顺序方法具有更好的结果，并且具有更高的参数可识别性。
