Microbial markets: socio-economic perspective in studying microbial communities

Abstract

Studying microbial communities through a socio-economic lens, this paper draws parallels with human economic transactions and microbes' race for resources. Extending the 'Market Economy' concept of social science to microbial ecosystems, the paper aims to contribute to comprehending the collaborative and competitive dynamics among microorganisms. Created by a multidisciplinary team of an economist, microbiologists, and mathematicians, the paper also highlights the risks involved in employing a socio-economic perspective to explain the complexities of natural ecosystems. Navigating through microbial markets offers insights into the implications of these interactions while emphasizing the need for cautious interpretation within the broader ecological context. We hope that this paper will be a fruitful source of inspiration for future studies on microbial communities.

Keywords: biological market theory; comparative advantage; evolutionary game theory; spatial economics.

Figure 1.

Schematic representation of the microbial interactions, depicting their opportunistic behavior. Cooperators provide products for the whole community by either directly releasing products or secreting enzymes, whereas cheaters directly acquire products from the common pool generated by others without contributing additional labor. Cooperative behavior can involve cross-feeding, metabolite or enzyme sharing, or increasing resilience against pathogens. Figure inspired by Smith and Schuster (2019), Tang (2019), and Figueiredo and Kramer (2020).

Figure 2.

Synthetic combination of microorganisms of choice. Micrograph of autofluorescent cyanobacteria (bright red) and the two fungal model organisms, Saccharomyces cerevisiae (light red) and Ustilago maydis (green), growing in the yeast form and carrying fluorescent reporters. In addition, unlabeled hyphae of a U. maydis laboratory strains are present. The species have been artificially combined for microscopy. Scale bar, 10 µm.

Figure 3.

(A) Schematic representation of the RPS game applied to three E. coli phenotypes, where each strategy dominates and gets dominated by exactly one other strategy. (B) In evolutionary game theory, each individual’s selection for reproduction and death is proportional to individuals’ fitnesses calculated based on two separate payoff matrices (here: birth/reproduction and death). The average payoffs of each individual is calculated based on its direct neighborhood in the population (in a 2D game each individual has eight neighbors). (C) The stochastic dynamics is implemented in terms of random birth and death events, like in the Moran process, where in each time a random individual is chosen for reproduction and for death (Bak and Rozlach 2020). (D) Simulated population evolution for given payoff matrices (20 000 generations) using Python package [Py]cess, assuming the Moran model (Bak and Rozlach 2020).

Figure 4.

(A) Picture of a Peltigera lichen growing on a rock close to Dusseldorf, Germany. (B) Microscopic view on the structure of a lichen in a thallus intersection. Blue, mycobiont stained with calcofluor white; green, chlorophyll autofluorescence of the photobiont cells. (C) Simple schematic representation of lichens as a microbial marketplace, where trading of different goods and services is exhibited to support the resilience of the symbiotic association.