Russian Journal of Genetics: Applied Research. 2016, Vol. 6, No. 8, pp. 845–853 [full text]

A Review of Simulation and Modeling Approaches in Microbiology

Klimenko A.I., Matushkin Yu.G., Kolchanov N.A., Lashin S.A.

Department of Systems Biology, Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia; Novosibirsk State University, Novosibirsk, Russia, e-mail: klimenko[_at_]


Bacterial communities are closely interrelated systems consisting of numerous species making it challenging to analyze their structure and relations. At present, there are several experimental techniques providing heterogeneous data, concerning various aspects of this research object. The recent avalanche of available metagenomic data challenges not only biostatisticians but also biomodelers, since these data are essential for improving the modeling quality, while simulation methods are useful for understanding 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 focused on the description of microbial community aspects such as trophic structure, metabolic and population dynamics, genetic diversity, as well as spatial
heterogeneity and expansion dynamics, are considered. We also propose a classification of the existing software designed for the simulation of microbial communities. It has been shown that, in spite of the prevailing trend for using multiscale/hybrid models, 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 for modeling microbial communities is based on the necessity of taking into account the heterogeneous data obtained from various sources by applying high-throughput genome investigation methods.

Keywords: microbial communities, ecological simulation, evolutionary modeling, prokaryotes

Bibliography link: A. I. Klimenko, Z. S. Mustafin, A. D. Chekantsev, R. K. Zudin, Yu. G. Matushkin, and S. A. Lashin A Review of Simulation and Modeling Approaches in Microbiology // Russian Journal of Genetics: Applied Research, 2016, Vol. 6, No. 8, pp. 845–853 DOI: 10.1134/S2079059716070066