Internship (M2): Numerical investigation of control strategies for microbial communities

M2 internship (4-6 months)

Numerical investigation of control strategies for microbial communities

Eugenio Cinquemani ,,

In nature, single microbial species rarely occur in isolation. Rather, different species are usually found in a given environment, competing for common resources and interacting by the exchange of metabolites and other small molecules. Microbial communities are also utilized in biotechnological applications, where division of labor among different species has the potential to improve performance in the accomplishment of the process of interest. In this context, a novel frontier is the design and implementation of feedback control strategies, whereby the interaction dynamics of duly synthesized consortia are optimized by the real-time application of chemical/physical control stimuli in response to the observed system dynamics [1,2].

The proposed internship is about the exploration of feedback control for a synthetic microbial community. In previous work [3], we have developed a mathematical model of a prototypical microbial community comprising two engineered Escherichia coli strains, which put in place a mutually beneficial interaction for the improved synthesis of a target protein product. Based on extensions of the proposed differential equation model aimed at taking into account possible control inputs, the objective of the internship is to investigate different control laws and their potential for the protein synthesis performance. The proposed internship program is as follows:

– Familiarization with the model in [3]
– Familiarization with traditional and modern techniques (PID, MPC) for bioreactor process control
– Implementation of a numerical simulation of the model in [3] in presence of control inputs
– Simulation study of the response of the microbial community to control inputs
– Implementation and simulation of several microbial community control laws
– Analysis of protein synthesis performance results
– Reporting

The project is to be developed at Inria Grenoble – Rhône-Alpes within the Systems Biology project-team Ibis (, which includes members of the experimental lab LIPhy of Université Grenoble-Alpes ( The internship will be supervised by Eugenio Cinquemani and will involve collaboration with colleagues of the team. It will profit from ongoing research activity of the team on the subject, from active collaborations with biology and control teams across France and beyond, as well as from ongoing in vivo investigation of similar problems on an experimental platform developed by the team.

The interested candidate is expected to have solid mathematical preparation, interest toward applications in biology/biotechnology, prior knowledge or strong interest in automatic control. He/she will be working an a lively international environment. Exchanges among team members occur in French and English. Proficiency in English is a plus.

[1] Treloar et al., Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comp Biol 2020
[2] Zhu et al., Model predictive control of continuous yeast bioreactors using cell population balance models, Chem Eng Sci 2000
[3] Mauri et al., Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs. PLoS Comp Biol 2020

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