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Sources and propagation of stochasticity in cellular metabolism

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  • Full or part time
    Dr D Oyarzun
    Dr R Grima
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Background: We are looking for a motivated doctoral student to join us on an exciting
project at the interface of stochastic modelling, systems and synthetic biology. Heterogeneity
is ubiquitous across all domains of life. In microbes, clonal populations display variable
phenotypes due to fluctuations in gene expression, cell division and many other processes.
Variability in gene transcription and translation is well recognized, yet many relevant
phenomena result from fluctuations in the metabolic state of a cell. It remains unclear if
fluctuations in gene expression permeate to metabolism and shift metabolic activity across
different operating regimes. Stochasticity in metabolism is often overlooked on the basis that
large molecular numbers average out fluctuations. But an increasing amount of
theoretical and experimental evidence [1,2], suggests that fluctuations in gene expression
have a key role in shaping metabolic phenotypes and growth.
The aim of the project is to develop mathematical methods to predict metabolic pathway
variability in bacterial populations. The student will develop techniques from stochastic
chemical kinetics to uncover the roles of enzyme expression and feedback regulation
dynamics on fluctuations in metabolic fluxes. This will include analysis and simulation
approaches, via stochastic simulation algorithms and solutions of the chemical master
equation with state-of-the-art approximation methods [3]. A key application of these methods
will be to exploit metabolic variability as a means to increase production yields of high-value
metabolites with engineered microbes [4].

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 biotechnology and healthcare. Large parts of our work are in collaboration with
partner wetlabs in the UK, Europe and the USA. This project is in close collaboration with the
stochastic modelling group of Dr Ramon Grima (, who offer leading
expertise in approximation methods for analysis of stochastic chemical systems. The student
will also join the thriving ecosystem of SynthSys – the Edinburgh Centre for Systems and
Synthetic Biology, one of the leading venues in the 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 Physics, Mathematics, Bioengineering,
Biochemistry, Computer Science, or Control Engineering. Knowledge of biochemical
modelling and/or stochastic analysis 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] Oyarzún et al, Noise propagation in synthetic gene circuits for metabolic control. ACS
Synthetic Biology, 2015
[2] Kiviet et al, Stochasticity of metabolism and growth at the single-cell level, Nature, 2014
[3] Cao & Grima, Linear mapping approximation of gene regulatory networks with stochastic
dynamics, Nature Communications, 2018
[4] Liu et al, Dynamic metabolic control: towards precision engineering of metabolism, J
Industrial Microbiology and Biotechnology, 2018

How good is research at University of Edinburgh in Biological Sciences?

FTE Category A staff submitted: 109.70

Research output data provided by the Research Excellence Framework (REF)

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