Genome engineering using whole-cell modelling and machine learning

Abstract

Mathematical models and artificial intelligence are becoming indispensable tools for making progress in engineering biology. Their impact is twofold. On the one hand, mathematical models help researchers to speed-up experimental design by simulating several
experimental scenarios on a computer. On the other hand, machine learning and artificial intelligence allow researchers to accelerate computations even more and to make sense of the
data produced both by the mathematical models and by experiments. The aim of this thesis
is to show how the latest whole-cell model (WCM) of bacteria Escherichia coli (E. coli) can be
used to design minimal genomes and produce non-native biomolecules such as violacein. The
WCM is used to simulate the effect of different genetic modifications on the cell, while machine
learning and statistical algorithms are used to speed-up computations and to understand the
effect of these genetic modifications. Finally, we discuss how this work can be combined, using the
minimal genomes as chassis to improve the metabolic performance of the cell for the production
of violacein.

Date of Award	9 Dec 2025
Original language	English
Awarding Institution	University of Bristol
Supervisor	Lucia Marucci (Supervisor) & Claire S Grierson (Supervisor)