Stochasticity in microbiology: managing unpredictability to reach the Sustainable Development Goals

Abstract

Pure (single) cultures of microorganisms and mixed microbial communities (microbiomes) have been important for centuries in providing renewable energy, clean water and food products to human society and will continue to play a crucial role to pursue the Sustainable Development Goals. To use microorganisms effectively, microbial engineered processes require adequate control. Microbial communities are shaped by manageable deterministic processes, but also by stochastic processes, which can promote unforeseeable variations and adaptations. Here, we highlight the impact of stochasticity in single culture and microbiome engineering. First, we discuss the concepts and mechanisms of stochasticity in relation to microbial ecology of single cultures and microbiomes. Second, we discuss the consequences of stochasticity in relation to process performance and human health, which are reflected in key disadvantages and important opportunities. Third, we propose a suitable decision tool to deal with stochasticity in which monitoring of stochasticity and setting the boundaries of stochasticity by regulators are central aspects. Stochasticity may give rise to some risks, such as the presence of pathogens in microbiomes. We argue here that by taking the necessary precautions and through clever monitoring and interpretation, these risks can be mitigated.

Fig. 1

Schematic overview of the differential impact of stochasticity with respect to closed single culture and open mixed culture (microbiome) systems. The key difference is the contribution of immigration to open systems, in contrast to closed systems.

Fig. 2

Visualization of the potential impacts of immigration in open systems in relation to nutrient availability and indigenous microbial community diversity. A low nutrient availability prevents the growth of invasive species, while a high indigenous microbial diversity promotes homeostasis to eliminate potential invaders.

Fig. 3

Schematic overview of the different steps for stochasticity monitoring and control in microbiomes. Full lines represent actual decisions and actions, while dashed lines represent flows of information.