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Whole-cell model development using machine learning for bioengineering applications

Project Description

This PhD aims at developing state-of-the-art computational models to recapitulate the behaviour of living cells across multiple scales. Cells are complex: their functioning relies on many interconnected parts working in concert, acting from the molecular to cellular scale. Whole-cell models have been recently developed that are able to simulate all core cellular functions on a computer: the Mycoplasma genitalium whole-cell model (1) is, currently, the only existing model that fully considers genomic context, describing the smallest natural culturable self-replicating organism. The cost of such a detailed model though is the huge numbers of physical parameters, which are often difficult to fit to data, and the excessive simulation time due to the large numbers of underlying equations.

This project will attempt to alleviate some of these difficulties by developing a more abstract model of a cell, which simplifies some existing processes and extends to consider other features thought to play a role in cellular functions. A starting point could be a model for bacterial cells that couples gene expression with physical properties of a cell, like growth rate (2) or to consider the role that a non-homogeneous spatial distribution of resources has on behaviour. Furthermore, the project will aim to investigate to what extent simplified whole-cell models can be automatically inferred purely from data considering basic biochemistry and biophysics using machine learning methods.

This truly interdisciplinary project merges mathematical modelling, high-performance computational simulation and biology. The student will work with supervisors (Dr Marucci, Prof. Grierson, Dr Gorochowski, Dr Ray) across Departments (Engineering Mathematics, Computer Science) and Schools (Engineering, Biological Sciences), and in collaboration with international leaders in Systems Biology (Dr Karr, Mount Sinai School of Medicine, New York).

First Class or Upper Second Class Honours degree. The candidate needs to meet the standard RCUK residency requirements ( either home UK students, or EC nationals who have lived in the UK for 3 years prior to starting the PhD (and if the 3 years have been spent in higher education, then they have to have been resident in the EEA or Switzerland immediately before this).
Applicant’s degree: Maths, Physics, Computer Science, Engineering, or related subjects.

Deadline for applications: 31/10/2019.
Please contact the supervisor Dr Lucia Marucci with a short CV and motivating your interest by 20/10/2019.
For other academic enquiries, please email Dr Filippo Simini (), Departmental Postgraduate Director, or Dr Paul Marshall (), School Postgraduate Director.
For enquiries on the application process, please email

Applicants should start an application Postgraduate Study through the University of Bristol system: ( They should select “Postgraduate Research” as the type of study.
Once a profile has been created, log in via the link sent to your email, start an application and fill out details under each heading. Please ensure that you select “Engineering Mathematics (PhD)” under “Which programme are you applying to”. Please upload a research proposal under “research statement” and include a personal statement and CV.

Funding Notes

Covers tuition fees, tax-free stipend of £14,777 per year plus a travel/consumables budget.


(1) Karr J. et al. Cell 150, Issue 2 (2012); (2) Weiße A.Y. et al. PNAS 112, E1038–E1047 (2015).

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