Dynamic control of metabolism for biotechnology applications
Precise control of metabolism is key for producing high-value chemicals with microbes. In this project we will combine cutting-edge time course metabolomics and control theory to create new strategies for controlling metabolic production pathways. Potential strategies include computer-based control (cell-in-the-loop), as well as in vivo control using suitable molecular machinery for sensing and actuation.
In the project the student will: i) collect time-course data using mass spectrometry-based metabolomics techniques, ii) build machine learning algorithms to infer metabolic models from time-course data, iii) use models to design suitable control strategies that maximize production, iv) iterate with our wetlab partners for further characterization and improvement of the control strategies. The project will make use of nonlinear dynamics and mathematical optimization to build a robust pipeline for data processing, model inference and pathway control.
The student will join the Biomolecular Control Group led by Diego Oyarzún (homepages.inf.ed.ac.uk/doyarzun/) in close collaboration with the Burgess lab, who have leading expertise in metabolomics technologies. Our group gathers a diverse mix of students and postdocs working at the interface between biology, engineering and computation. The ideal candidate should have excellent mathematical and computational skills, as well as outstanding presentation skills for various audiences. Applicants must hold a First Class or an Upper Second Class degree (or equivalent overseas qualification) in a discipline relevant to the project such as control engineering, (bio)chemical engineering, machine learning or applied mathematics.
The “Visit Website” button on this page will take you to our Online Application checklist. Please complete each step and download the checklist which will provide a list of funding options and guide you through the application process.
If you would like us to consider you for one of our scholarships you must apply by 5 January 2020 at the latest.
Oyarzún & Chaves, Dynamics of complex feedback architectures in metabolic pathways, Automatica, 2019.
Liu et al, Dynamic metabolic control: towards precision engineering of metabolism, Journal of Industrial Microbiology and Biotechnology, 2018.
Oyarzún & Stan, Synthetic gene circuits for metabolic control: design trade-offs and constraints, Journal of the Royal Society Interface, 2013.
How good is research at University of Edinburgh in Biological Sciences?
FTE Category A staff submitted: 109.70
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