Section: New Results

Going towards control

Quantitative synthetic biology. Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetise 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. Establishing which consortium is best for the production of a given compound or set thereof remains however a great challenge. The team introduced an initial model and a method, called MultiPus , 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 (Julien-Laferrière et al., Scientific Reports, 6, 2016).

Since the work on MultiPus , the team has been considering quantitative approaches for synthetic biology. We thus explored the concept of multi-objective optimisation in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we proposed multi-objective models, initially for a single species, to suggest reaction deletion strategies, and also to deal with situations where several functions must be optimised simultaneously, such as the maximisation of bioproducts while minimising toxicity (Hartmann et al., BMC Systems Biology, see https://www.ncbi.nlm.nih.gov/pubmed/29268790, just accepted and not yet visible in Hal-Inria). We compared our results with those obtained by using the well-known bi-level optimisation model of OptKnock , and studied two multi-objective optimisation problems arising from the metabolic engineering of microorganisms. One of them, using Yeast, has been validated experimentally. The work is submitted. The team has then started expanding it to communities (Master Thesis of Irene Ziska who is continuing into a PhD).