A review of simulation and modeling approaches in microbiology

Abstract

Bacterial communities are tightly interconnected systems consisting of numerous species making it challenging to analyze their structure and relations. There are several experimental techniques providing heterogeneous data concerning various aspects of this object. A recent avalanche of metagenomic data challenges not only biostatisticians but also biomodelers, since these data are essential to improve the modeling quality while simulation methods are useful to understand the evolution of microbial communities and their function in the ecosystem. An outlook on the existing modeling and simulation approaches based on different types of experimental data in the field of microbial ecology and environmental microbiology is presented. A number of approaches focusing on a description of such microbial community aspects as its trophic structure, metabolic and population dynamics, genetic diversity as well as spatial heterogeneity and expansion dynamics is considered. We also propose a classification of the existing software designed for simulation of microbial communities. It is shown that although the trend for using multiscale/hybrid models prevails, the integration between models concerning different levels of biological organization of communities still remains a problem to be solved. The multiaspect nature of integration approaches used to model microbial communities is based on the need to take into account heterogeneous data obtained from various sources by applying high-throughput genome investigation methods.