A Combinatorial Algorithm for Microbial Consortia Synthetic Design

Abstract

Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.

Scalable and exhaustive screening of metabolic functions carried out by microbial consortia

“…This task entails identifying species of interest among all the microbiota in order to fit various criteria depicting the added value of the microbiome over an individual organism. Along these lines, Julien-Laferrière et al. (2016) proposed to extend the gap-filling concept used in metabolic network reconstruction frameworks ( Pan et al.…”

“…Although not applied in the same context in terms of the size of datasets considered or the optimization performed, both methods entail optimizing a parsimonious criterion. In one case, the MultiPus algorithm minimizes the total cost of the synthesis, mostly resulting from exchanges and exogenous reactions ( Julien-Laferrière et al. , 2016 ), whereas the second algorithm minimizes the size of the community ( Eng et al.…”

BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits

“…K. pneumoniae is a natural producer of 1,3-propanediol, which is an important building block for polymer production. Our results demonstrated that co-cultivation of K. pneumoniae with M. mazei may enable obtaining higher yields of this compound in Klebsiella (Julien-Laferrière et al 2016). This supported a previous study involving co-cultures of Clostridium sp.…”

“…As part of BacHBerry, we introduced an initial topological model and a combinatorial algorithm that enabled us to propose optimal consortia to produce compounds that were either exogenous to the consortium, or were endogenous but where interaction between the species in the consortium could improve the production line (Julien-Laferrière et al 2016). For initial validations of the model and the method, we applied it to two case-studies taken from the literature.…”

FLYCOP: metabolic modeling-based analysis and engineering microbial communities

“…Pathway modularization is also a great advantage of communities ( Eng and Borenstein, 2016 ; Julien-Laferrière et al , 2016 ). Diverse metabolic functions could be split and distributed among different strains in the community.…”