Advancing microbial sciences by individual-based modelling

Abstract

Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE).

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

“…Population level modeling efforts have been thoroughly summarized in a recent review [34]. Here, we mention their salient traits.…”

“…As an example, an IBM may characterize a microbial system using individual interactions/characterization [53,54]; these individuals can be single cells, species, or groups of microbes within a particular spatial and/or temporal context. Population level information emerges as a natural byproduct of the IBM's description [34]. IBMs are inherently more complex and case-specific, but offer highly descriptive predictions and are more suitable for modeling heterogeneity.…”

“…In comparison, spatial considerations have received relatively little attention, mostly to keep the models simple. However, there are many microbial systems for which these factors are essential in defining the dynamics within a community [34]. Microbes often exist in complex, spatially structured communities such as biofilms.…”

Editorial: The microbiome as a source of new enterprises and job creation

“…A great example of applying this approach is the identification of a relative of Clostridium difficile that inhibits C. difficile by converting a bile acid (Buffie et al ., 2014). Another ignored aspect of the gut microbiota is its spatial organization, usually destroyed when extracting DNA, although spatial structure has been extensively shown to have profound effects on population dynamics and ecosystems (Hellweger et al ., 2016). Notably, bottom‐up mathematical models can predict the self‐organization of microbes into spatial clusters and the effects these emergent structures can have on function (Hellweger et al ., 2016).…”

“…(2013) discuss mathematical approaches commonly used in bioengineering that would help biologists make sense of complex communities of microbes. We have argued that individual differences between cells should be considered in mathematical models to better represent the distribution of characteristics and to account for the effects of individuality on ecosystem function (Hellweger et al ., 2016). In conclusion, microbial community research can replicate the success of physics if it takes mathematical modelling on board – it will be both challenging and rewarding (Widder et al ., 2016).…”