Predicting partner fitness based on spatial structuring in a light-driven microbial community

Abstract

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

Cyanobacteria as cell factories for the photosynthetic production of sucrose

“…Preliminary analyses and multi-omics approaches have been used to predict hidden interactions within synthetic consortia, providing insight on areas of cooperation and competition that could be validated and exploited to design more robust co-cultures ( Carruthers, 2020 ; Zuñiga et al, 2020 ; Ma et al, 2022 ). Synthetic co-cultures also present a simpler set of variables in comparison to natural communities which may be more amenable to simulations, such as agent-based modeling, for predicting emergent behaviors in a population ( Sakkos et al, 2022 ). Finally, additional layers of metabolic exchange can be designed into the synthetic co-culture system to experimentally probe and validate hypotheses of inter-species exchange.…”

Quantifying drift-selection balance using an agent-based biofilm model of identical heterotrophs under low-nutrient conditions

“…The agent-based model employed NUFEB [9,10], which is based on the LAMMPS [9] molecular dynamics simulation framework and has successfully been used to model multi-species biofilms [10], including development and detachment [7], trade-offs in extracellular polymeric substance production [11], and phototroph-heterotroph metabolic interactions [12].…”

Quantifying Drift-Fitness Balance Using an Agent-Based Biofilm Model of Identical Heterotrophs Under Low Nutrient Conditions

“…The agent-based model employed NUFEB (Newcastle University Frontiers in Engineering Biology), 9,10 which is based on the LAMMPS 9 molecular dynamics simulation framework and has successfully been used to model multi-species biofilms, 10 including development and detachment, 7 trade-offs in extracellular polymeric substance production, 11 and phototroph-heterotroph metabolic interactions. 12 . NUFEB is not lattice based, cells were positioned in three dimensions and had individual dynamic sizes.…”