Food fermentation as a model system for predictive synthetic community design under community-level inhibition

Abstract

Microbial food fermentation continues to gain interest from the industry and the scientific community due to the potential nutritional benefits and promising use for advancing food security and sustainability. A valuable strategy for improving fermented foods is in-situ prediction of synthetic community functioning to reduce experimental screening time of potential microbial starter cultures. In synthetic community design, adoption of such methods is low and limited to communities with only a few members due to the emergence of negative ecological dynamics with increasing community complexity and the consequently increased computational investment and costs. Yet, deciphering the microbial and environmental interactions driving such emergent behaviours will enable more efficient and accurate prediction of complex community functioning in fermentation and across microbial disciplines. In this, food fermentation provides an excellent model system due to the well-described metabolic dynamics present in the many available fermented foods across the globe. In this review, we provide an overview of (i) potential microbial sources in fermentation, (ii) examples of functions that microbial communities are optimised for (iii) the current knowledge on different modes of inhibition in fermentation and (iv) an overview of applicable computational frameworks to explore modes of inhibition on microbial communities in fermentation and related fields. In doing so, we provide a basis for microbiologists and bioinformaticians across microbial disciplines to come together and use the unique advantages of food fermentation to further the field of synthetic community design.