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Next-generation computational optimization of protein expression systems

School of Biological Sciences

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Dr D Oyarzun No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

We are looking for a motivated doctoral student to work on an exciting project at the interface
of whole-cell modeling, optimization and industrial biotechnology. A key area of
biotechnology is production of heterologous proteins for applications in the energy, food and
pharmaceutical sectors. Expression systems are genetically engineered to produce large
amounts of protein in a specific cellular host. Key to commercialization of these processes is
the improvement of titer, productivity and yield. Yet strains are often manually optimized for
specific laboratory setups and sub-optimal conditions found in large-scale fermentations
often limit their production capacity. Moreover, because foreign genes use the host
resources for their expression, they perturb the homeostatic balance and cause growth
defects that impair production. The interplay between growth conditions, host physiology and
protein output is still poorly understood, resulting in costly rounds of trial-and-error testing
and strain characterization.

Our general aim is to develop a model-based framework for optimizing protein expression
systems in bacteria. We will use mathematical models that connect protein expression
systems commonly used in industry with our recently developed ‘whole-cell’ model for
bacterial growth [1]. This will result in an integrative, system-level, description of protein
expression systems and their interaction with the host where they reside.
The student will use techniques from dynamical systems and multiobjective optimization [2]
to create a computational platform for modeling and simulation of expression systems in
various bioreactor settings. With the platform we will quantify the impact of system
parameters commonly modified with genetic engineering, as well as assess the potential of
advanced feedback control strategies to mitigate growth defects [3]. Through the use of
multiobjective optimization and Pareto optimality, we will find designs that optimally trade-off
titer and productivity against growth defects, batch times, and other performance indicators.

Candidate: The successful candidate will join the group of Dr Diego Oyarzún
(, who will relocate to the University of Edinburgh in
January 2019. Our group develops quantitative methods for Systems & Synthetic Biology
applied to various challenges in biotechnology and healthcare. Large parts of our work are in
collaboration with wetlabs in the UK, Europe and the USA. This project is in close
collaboration with the wetlab of Dr Francesca Ceroni [3] at the Department of Chemical
Engineering of Imperial College London. The student will also join the thriving ecosystem of
SynthSys – the Edinburgh Centre for Systems and Synthetic Biology, one of the leading
venues in discipline.
The ideal candidate should have an excellent record and passion for quantitative methods in
the life sciences. We seek someone open-minded, creative and willing to join a diverse and
multidisciplinary team. The 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 Mathematics, Synthetic Biology, Bioengineering,
Biochemistry, Computer Science, Physics or Control Engineering. Knowledge of biochemical
modelling and/or optimization methods would be advantageous.

Funding Notes

The “Apply online” 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 12 noon on 13 December 2018 at the latest.


[1] Weisse, Oyarzún, Danos & Swain (2015), Mechanistic links between cellular trade-offs,
gene expression, and growth, PNAS
[2] Miettinen (1998), Nonlinear Multiobjective Optimization (Springer US)
[3] Ceroni et al (2018), Burden-driven feedback control of gene expression, Nature Methods
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