Skip to main content
Dryad logo

Synthetic microbial consortia with programmable ecological interactions

Citation

Li, Shuyao et al. (2022), Synthetic microbial consortia with programmable ecological interactions, Dryad, Dataset, https://doi.org/10.5061/dryad.gmsbcc2qh

Abstract

1. Central to the composition, structure and function of any microbial community is the complex species interaction web. But understanding the overwhelming complexity of ecological interaction webs has been challenging, owing at least partly to the lack of efficient tools for disentangling species interactions in natural or artificial microbial communities.

2. In this study, we developed a microbial experimental system which allows for rapidly generating microbial consortia with programmable ecological interactions. We engineered the model organism Escherichia coli to construct metabolism- and quorum sensing-based modules. The two engineered modules were used to create synthetic microbial consortia of synergy, competition and exploitation.

3. We showed that each of synthetic microbial consortia displayed the unique mode of population dynamics under certain initial inoculation conditions. We also demonstrated that the transitions between exploitation and the types of competition or synergy based on the same paired strains were plausible by tuning the two engineered modules. We lastly derived mathematical models to quantitatively capture the experimentally observed population dynamics of these synthetic microbial consortia.

4. This approach offers a fresh angle to engineering microbial systems for experimentally testing ecological questions with a much greater control and manipulation.

Methods

Mathematical modeling

Three different general frameworks were proposed to describe population dynamics within synthetic microbial consortia. Models Ⅰ, Ⅱ and  are all built using ordinary differential equations. Specifically, these mathematical models consisted of two to five variables that were classified into the following compounds: 1) cell density of each strain Ni; 2) concentration of the common resource (e.g. glucose) and amino acids Li ; 3) the signaling molecules QCustom-tailored codes were developed to simulate models in MATLAB (MATLAB R2020b, Mathworks, Inc., Natick, MA, USA). The ode45 subroutine was used to solve the ordinary differential equations and the lsqnonlin algorithm was performed to conduct the least square regression. The procedure of parameter estimation was based on multiple local optimizations of different parameter initial values (D'Alessandro et al., 2015). The “RandomStartPointSet” object was employed to randomly generate 10,000 initial point sets for each interaction in each model. The priori-knowledge strategy (Walter, 1987) was applied to constrain the obtained 10,000 parameter set estimations and the optimal one was selected with the minimum sum of squares for error (SSE).

Funding

The National Natural Science Foundation of China, Award: 32025024

The National Natural Science Foundation of China, Award: 91951107

The National Natural Science Foundation of China, Award: 32101246

Fundamental Research Funds for the Central Universities, Award: K20200026

Ecological Civilization Project of Zhejiang University