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Thesis by Sahak YEGHIAZARYAN (2023 - 2026)

Coupling genome based and energy based approaches of syntrophic microbial interactions for the modelling of high rate anaerobic digestion

Thesis by Sahak YEGHIAZARYAN (LBE, 2023-2026). This thesis sets out to develop thermodynamically constrained metabolic models of syntrophic interactions that can be included in the widely-used industry reference model for anaerobic digestion, allowing the development of more robust and predictive mechanization models to suit current engineering needs.

  • Co-funded thesis
  • Starting date : december 2023
  • Research laboratory :  Laboratory of Environmental Biotechnology
  • Thesis director : Nicolas Bernet (LBE) 
  • Supervisors : Elie Le Quéméner (LBE), Simon Labarthe (Pleiade)
  • Metaprogramme axis : Axis 1 (Deciphering the functions of living matter at multiple scales: regulation and integration of biological processes)

Summary :

Mechanization is one of the few mature technologies available for producing energy in the form of biogas from organic waste or sludge. The process involves a complex cascade of biochemical reactions catalyzed by a variety of microorganisms described in a widely used reference model (ADM1: Anaerobic Digestion Model n°1). Today, the development of anaerobic digestion requires processes operating at higher loading rates and adapting to a wider range of organic matter (codigestion). Unfortunately, the ADM1 model is not capable of correctly predicting performance under these conditions, which severely limits current engineering capabilities. Indeed, the syntrophic oxidation of organic acids, which is the kinetically limiting step, is not currently included in the model. In this thesis project, we propose to develop thermodynamically constrained metabolic models of syntrophic interactions, and then to reduce these models for inclusion in ADM1. A back and forth process between modelling and experiments on synthetic communities will enable the models to be validated step by step. We believe that this project will enable the development of more robust and predictive mechanization models, in line with current engineering needs

Sahak Yeghiazaryan

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