Mathematical Modeling of Microbial Community Dynamics: A Methodological Review

Abstract

Microorganisms in nature form diverse communities that dynamically change in structure and function in response to environmental variations. As a complex adaptive system, microbial communities show higher-order properties that are not present in individual microbes, but arise from their interactions. Predictive mathematical models not only help to understand the underlying principles of the dynamics and emergent properties of natural and synthetic microbial communities, but also provide key knowledge required for engineering them. In this article, we provide an overview of mathematical tools that include not only current mainstream approaches, but also less traditional approaches that, in our opinion, can be potentially useful. We discuss a broad range of methods ranging from low-resolution supra-organismal to high-resolution individual-based modeling. Particularly, we highlight the integrative approaches that synergistically combine disparate methods. In conclusion, we provide our outlook for the key aspects that should be further developed to move microbial community modeling towards greater predictive power.

Advanced nutrient removal from surface water by a consortium of attached microalgae and bacteria: A review

“…The multi-species communities present in the algal-bacterial biofilms have higher-order properties that are not present in the individual microorganisms, but arise from their interactions (Song et al, 2014). Based on a system-level knowledge, mathematical models can provide a better understanding of the underlying principles and mechanisms of pollutant fate and microbial functionalities, including the dynamic mass balance and metabolic functions.…”

“…This understanding will significantly improve both process design and operational conditions (Lindemann et al, 2016;Gonçalves et al, 2017). For instance, stoichiometric metabolic network based models can provide mechanistic insights of the interactions within microbial species and between microorganisms and their environment, as well as the estimation of flux distributions within individual species and the consortium (Song et al, 2014).…”

“…Considering the complex structure of attached algal-bacterial communities and conditions in surface water, the use of integrative approaches combining multiple relevant methods in building a reliable model will be crucial (Fovet et al, 2010;Song et al, 2014). In this context, many challenges in the development of models arise such as: i) the dynamic interspecies interactions and metabolic shifts in individual species should be considered during the design of population based models (Lindemann et al, 2016), and ii) the dynamic changes of environmental conditions in surface water (e.g., temperature, water flow, pH, pollutants input) need to be adequately described based on their key influence on the accuracy and reliability of the models (Larned, 2010).…”

The impact of resource dependence of the mechanisms of life on the spatial population dynamics of an in silico microbial community

“…53 For these and other reasons, the use of individual-based modelling in microbiological studies has gained increasing favour in recent years. [54][55][56][57] To localize the interactions in our model, twodimensional space is represented by a square regular grid and individuals may only interact with their nearest neighbours, which are defined as the four grid cells located in their von Neumann neighbourhood. These interactions occur stochastically, as they are simulated using the Gillespie algorithm.…”

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

“…The resulting stoichiometric matrices relate the flux rates of enzymatically-driven reactions to time derivatives of metabolic concentrations [21]. This type of model can then be used for Flux Balance Analysis (FBA) [21] and allows investigators to correlate a genotype to its phenotype (in an individual cell or in a community) through the derivation of metabolic fluxes [25][26][27].…”