Exploiting Community Drivers for Population Control in Microbial Communities

Introduction

Cellular communities are collections of interacting microbial species found in all natural environments ranging from plant root ecosystems to the human gut microbiome. They are highly dynamic systems that respond to changes in their environment, but also influence their environment in extremely consequential ways: e.g. affecting crop yield or drought resistance in plants, or causing diseases in humans.

While improvements in sequencing technology allow for a growing understanding of these communities and their relationship with their environment, the ability to control and direct them towards beneficial compositions are needed to reap the benefits.

Control mechanisms for communities also allow them to be used for biotechnological production. While current applications in biotechnology predominantly involve a single strain in an axenic monoculture, a community of several strains has many potential benefits such as the ability to utilise more complex substrates, increased robustness to environmental fluctuations, or division of labour for decreased metabolic burden.

The inability to maintain population ratios is a key limitation preventing widespread adoption of microbial communities across biomedicine and manufacturing: these systems are susceptible to population crashes where they move away from an ideal population with maximal yield to one where dominant species outcompete and overwhelm others.

Aims & Method

This research aims to engineer control mechanisms that regulate and fine tune the composition of cellular communities in real time based on the concept of 'driver species', a subset of species that are sensitive to easily controlled inputs such as light or chemical inducers. Because they interact with other species in the community (e.g. by competing for resources or producing toxic metabolites), these driver species can be regulated to steer overall community composition.

While a theoretical framework has been published, this has yet to be shown empirically, possibly due to the complexity in implementing the control scheme.

This research will consist of growing pairs of species in the laboratory and measuring the strength of inter-species interactions from steady-state abundance data in order to define the ecological network (i.e. the relationship between the individuals in the community). This will be used to generate mathematical models of the community's growth and dynamics and develop control schemes.

The species can then be combined into a community using Chi.Bio, an automated robotic platform developed by the lab which performs the in-situ measurement and control necessary for implementing control schemes based on these models, as real-time data of community composition is required to regulate feedback. After testing this in small engineered synthetic communities, the control schemes will then be tested with a (subset of) a natural microbiome to engineer its behaviour and achieved a desired application composition.

This project falls directly within the EPSRC Engineering research theme, and particularly the areas of Control Engineering and Synthetic Biology. Similarly, outcomes of this project will have broad impact in realising EPSRC Priorities including 21st Century Products (by making smart, multi-functional communities), and Sustainable Industries (by unlocking new methods for distributed biomanufacturing).