Approximate Bayesian computation and agent based modelling for inference of size dependence in stochastic gene expressionof Size Dependence in Stochastic Gene Expression
File(s)
Author(s)
Bowman, Anthony
Type
Thesis
Abstract
Gene expression is an inherently stochastic process, but it is also subject to sources of extrinsic
noise. Chief among these sources is the cell cycle, both through its different stages, but also
through the change in cell size that happens throughout it. In order to fully understand these
sources of variability, mathematical modelling and quantitative gene expression data must be
combined. Unfortunately, when working with agent-based models of gene expression that
incorporate the cell cycle, we do not have access to an analytical likelihood function. These
types of models can only be simulated. Approximate Bayesian computation (ABC) is a class of
simulation-based likelihood free inference methods that is useful for dealing with problems
of this nature. In this thesis we have firstly developed more efficient ABC methods for model
selection that can help us decide among different competing biological mechanisms. Secondly
we have integrated models of gene expression, including the totally asymmetric exclusion
process (TASEP), into the cell cycle. We have done so by developing agent-based models of
growing and dividing cells, where each cell contains biochemical reactions. In this way, we
are able to couple gene expression to cell size and the cell cycle. Finally, in collaboration with
biologists, we have applied these models and our ABC methods to single cell transcription and
cell size snapshot data in S.pombe, in order to uncover the precise molecular mechanisms that
control transcription rates scaling with cell size. We find that transcription of both constitutive
and periodic genes is a Poisson process with transcription rates scaling with cell size and
without evidence for transcriptional off states. Modelling and experimental data indicate that
scaling relies on the coordination of RNA polymerase II (RNAPII) transcription initiation
rates with cell size and that RNAPII is a limiting factor. Our modeling results are validated by
real-time quantitative imaging that shows size increase is accompanied by a rapid concentrationindependent recruitment of RNAPII onto chromatin. Overall, this thesis highlights applications
of mathematical modelling and statistical inference to biological data, to uncover biological
mechanisms
noise. Chief among these sources is the cell cycle, both through its different stages, but also
through the change in cell size that happens throughout it. In order to fully understand these
sources of variability, mathematical modelling and quantitative gene expression data must be
combined. Unfortunately, when working with agent-based models of gene expression that
incorporate the cell cycle, we do not have access to an analytical likelihood function. These
types of models can only be simulated. Approximate Bayesian computation (ABC) is a class of
simulation-based likelihood free inference methods that is useful for dealing with problems
of this nature. In this thesis we have firstly developed more efficient ABC methods for model
selection that can help us decide among different competing biological mechanisms. Secondly
we have integrated models of gene expression, including the totally asymmetric exclusion
process (TASEP), into the cell cycle. We have done so by developing agent-based models of
growing and dividing cells, where each cell contains biochemical reactions. In this way, we
are able to couple gene expression to cell size and the cell cycle. Finally, in collaboration with
biologists, we have applied these models and our ABC methods to single cell transcription and
cell size snapshot data in S.pombe, in order to uncover the precise molecular mechanisms that
control transcription rates scaling with cell size. We find that transcription of both constitutive
and periodic genes is a Poisson process with transcription rates scaling with cell size and
without evidence for transcriptional off states. Modelling and experimental data indicate that
scaling relies on the coordination of RNA polymerase II (RNAPII) transcription initiation
rates with cell size and that RNAPII is a limiting factor. Our modeling results are validated by
real-time quantitative imaging that shows size increase is accompanied by a rapid concentrationindependent recruitment of RNAPII onto chromatin. Overall, this thesis highlights applications
of mathematical modelling and statistical inference to biological data, to uncover biological
mechanisms
Version
Open Access
Date Issued
2023-05
Date Awarded
2023-10
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Shahrezaei, Vahid
Sponsor
Engineering and Physical Sciences Research Council
Publisher Department
Mathematics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)