Automated design of synthetic microbial communities

Abstract

Microbial species rarely exist in isolation. In naturally occurring microbial systems there is strong evidence for a positive relationship between species diversity and productivity of communities. The pervasiveness of these communities in nature highlights possible advantages for genetically engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigate issues often found in engineering a monoculture, especially as functional complexity increases. Here, we demonstrate a methodology for designing robust synthetic communities that include competition for nutrients, and use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We computationally explore all two- and three- strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and further tuning the community composition.

Beneficial Relationships Between Endophytic Bacteria and Medicinal Plants

“…Given the importance of the endophytic bacteria-medicinal plants interactions, studies of such bacteria may enable the successful development of new areas of natural medicine. Under natural environments, microbial communities with mixed species can exhibit competitive advantages in metabolic complexity, productivity, resistance to invasion, and resource efficiency over monocultures (Karkaria et al, 2021). Being capable of reproducibly and predictably constructing microbial communities for biotechnological or synthetic biological applications would guarantee the application of such advantages.…”

MiMiC: a bioinformatic approach for generation of synthetic communities from metagenomes

“…Moreover, communities of surprisingly low complexity were inferred using random selection of metabolite pairs (substrate product), and no information was provided on the complexity of usage and computation time. The second method aimed to generate communities that would be stable within a chemostat based on a user‐provided list of strains with known quorum sensing and bacteriocin production and sensitivity values (Karkaria et al ., 2021). This method was designed for application to engineered strains, for which such data would be known due to having been designed into the strains genome, and not for environmental isolates.…”

Control of synthetic microbial consortia in time, space, and composition

“…As a result, a great deal of time is often spent in testing and iterating population-control mechanisms. Researchers have recently utilized approximate Bayesian computation with sequential Monte Carlo sampling to automate community design [61]. This work promises to improve the community engineering process and revealed optimal designs and critical population parameters for two-and three-strain communities.…”