Uncertainty Quantification of Random Microbial Growth in a Competitive Environment via Probability Density Functions

Abstract

The Baranyi–Roberts model describes the dynamics of the volumetric densities of two interacting cell populations. We randomize this model by considering that the initial conditions are random variables whose distributions are determined by using sample data and the principle of maximum entropy. Subsequenly, we obtain the Liouville–Gibbs partial differential equation for the probability density function of the two-dimensional solution stochastic process. Because the exact solution of this equation is unaffordable, we use a finite volume scheme to numerically approximate the aforementioned probability density function. From this key information, we design an optimization procedure in order to determine the best growth rates of the Baranyi–Roberts model, so that the expectation of the numerical solution is as close as possible to the sample data. The results evidence good fitting that allows for performing reliable predictions.

Uncertainty-based Gompertz growth model for tumor population and its numerical analysis

“…This feature has prompted several recent contributions to develop mathematical models that integrate ambiguity to understand evolutionary processes using differential equations.The traditional perspective of uncertainty has been transformed, and the usual view regards uncertainty as undesirable in research efforts and must be eradicated by all feasible measures. The contemporary viewpoint accepts ambiguity and thinks that science should solve it [9][10][11][12]. Possibility theory focuses on ambiguity inherent in natural languages and is "possibilistic" rather than probabilistic.…”