Digital shadow for the optimization of bioprocesses

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Laboratory environnement

TBI (Toulouse, France) is an academic laboratory structured into 4 scientific poles and 1 technological pole with over 350 scientists whose expertise ranges from enzyme and metabolic engineering to microbial system engineering, particularly in biotechnology fields for energy and green chemistry.
The PhD student will be integrated in the Transfer Interfaces and Mixing (TIM) team. Please see https://www.toulouse-biotechnology-institute.fr/en/poles/equipe-tim/ for more information about our research. A transversal activity of this group is the modelling of bioreactors (Kinetic Phenomenological models, Hydrodynamic Models, Hybrid Dynamic Model, Model of Population) for bioprocess simulation and optimization (Soft-Sensors, Fault-Detection, Control).
 

The Laboratory of Environmental Biotechnology is a research unit of the National Research Institute for Agriculture, Food and Environment (INRAE) - Occitanie-Montpellier Centre - located in Narbonne. The LBE is attached to the TRANSFORM (pilot department), AgroEcoSystem and MICA departments. It is part of the I-site Montpellier Université d'Excellence, the l'Institut Carnot 3BCar, 'ICIREWARD, the UNESCO International Center for Water, and LabEx Agro. https://eng-narbonne.montpellier.hub.inrae.fr/lbe/presentation
Symbolized by the production of bioenergy (e.g., via biomethane and biohydrogen by anaerobic ecosystems), the LBE's research seeks to treat and/or valorize the waste products of human activity, whether they are liquid effluents (agri-food in particular), solid residues (agricultural residues, household waste and sludge from wastewater treatment plants) or specific biomasses such as micro or macroalgae. Its research covers a very broad spectrum of disciplinary skills: microbiology, microbial ecology, biological engineering, process engineering, modeling, automation, life cycle analysis, project engineering, and industrial transfer.
 

The mission of Toulouse White Biotechnology (TWB) is to contribute to the development of a bio-economy based on the use of renewable carbon as a raw material to fuel industry in the future while respecting existing food and feed chains. The target areas of application are the production of intermediate products for the chemicals industry, biomaterials, biopolymers and biofuels. TWB covers a wide range of research and industrial development activities, from biological engineering (enzymatic and metabolic engineering, synthetic biology) to the development of processes on the pre-industrial pilot scale. TWB carries out its projects with creativity using ethical approaches and applying the principles of sustainable development. https://www.toulouse-white-biotechnology.com/

Work environment, missions and activities

Project Context

Since the beginning of the 21st century, the biotechnology market and bioprocessing have been growing quickly (Martin et al. 2021). The development of breakthrough technologies like CRISPR-CAS9 genome editing has revolutionized microbiology field. Introducing mutations or inserting genes to rewire the metabolism of microorganisms has become increasingly easy and efficient. However, these bio-machines require optimal physico-chemical conditions such as oxygen, substrate, mixing, pH… The optimisation of bioprocesses is increasingly supported by in-silico approaches (Helgers et al. 2022a, Helgers et al. 2022b), sensor miniaturisation and Machine Learning algorithms. This is why bioprocesses are being tilted towards a technological evolution that allows not only to ensure the quality and quantity of a product, but also that allows a fast way towards the screening of strains to optimize all the process.
From a general point of view, the management and the modelling of the vast amounts of biological data flowing into databases using microorganisms is a rather complex task due to the different interactions between biological, chemical, physical phenomena and the loss of the gene of interest. Machine learning (ML) approaches have a high potential to rationally explore large design spaces while exploiting experimental facilities most efficient (Helleckes et al. 2023). The exploration of large design spaces needs to apply the intensified Design of Experiments (iDoE) to reduce the overall number of experiments (von Stosch and M. and Willis, M.J. 2017). ML approaches could be coupled to Digital Twins schemas to optimize the bioprocess.

Project

In this Ph.D. project, we propose the development of new Digital Tools (AI, statistics, on-line optimisation, hybrid models, Reinforcement Learning,  etc) with the objectives: to select the bests strains and to optimise the production of metabolites of interest. The framework is envisioned to employ a combination of omics data, physical models and data-driven approaches (ensembles of trees, neural networks, ...) to predict and optimize the properties of interest. The proposed framework can be applied to bioprocesses that work with micro-organisms, with or without genetic modification (e.g. recombinant proteins) as part of a Design−Build−Test−Learn (DTBL) cycle. 
This project will be enriched by the expertise of researchers from three research teams belonging to UMR 792-TBI, the UR 050-LBE, the UMS 1337-TWB, bioprocess TWB, working in the fields of bioprocess modelling (TBI) and optimisation and Artificial Intelligence (LBE).

Training and skills

The ideal candidate will hold a Master’s degree in Computer Science Engineering, Bio and Process Engineering, Computer Science, Mathematics (or related field), or an equivalent engineering degree. We are looking for a motivated and dynamic candidate. Candidates should be comfortable working in an interdisciplinary team and have the ability to work independently. Proficiency in English is a requirement; knowledge of French is an advantage (but not a requirement). Supervisors collectively speak English, French, Spanish. 

INRAE's life quality

The position is available for 3 years, and is based at INSA-Toulouse. Salary follows national directives including full social and health benefits (gross monthly salary 2 300). PhD Students have 45 days of leave/year. The successful applicant will have opportunities and funding for professional development, for presenting their research results at international conferences.
The PhD student will be supervised by Jean-Philippe STEYER and Dr. Cesar Aceves Lara. The PhD student could benefit from the synergy between the three laboratories.

How to apply

I send my CV and my motivation letter