Cell size homeostasis and optimal viral strategies for host exploitation
Date
2018
Authors
Journal Title
Journal ISSN
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Publisher
University of Delaware
Abstract
The first part of this thesis address a question formulated more than 80 years
ago (and still remains elusive): how does a cell control its size? Growth of a cell and its
subsequent division into daughters is a fundamental aspect of all cellular living systems.
During these processes, how do individual cells correct size aberrations so that they
do not grow abnormally large or small? How do cells ensure that the concentration of
essential gene products are maintained at desired levels, in spite of dynamic/stochastic
changes in cell size during growth and division? ☐ In chapter 1, we introduce the reader to the field of cell size/content homeostasis.
We review how advances in singe-cell technologies and measurements are providing
unique insights into these questions across organisms from prokaryotes to human cells.
More specifically, how diverse strategies based on timing of cell-cycle events, regulating
growth, and number of daughters are employed to maintain cell size homeostasis. We
further discuss how size-dependent expression or gene-replication timing can buffer
concentration of a gene product from cell-to-cell size variations within a population. ☐ In chapter 2, we propose the use of stochastic hybrid systems as a framework for
studying cell size homeostasis. We assume that cell grows exponentially in size (volume)
over time and probabilistic division events are triggered at discrete time intervals. We first consider a scenario, where a timer (i.e., cell-cycle clock) that measures the time
since the last division event regulates both the cellular growth and division rates. We
also study size-dependent growth / division rate regulation mechanisms. We provide
bounds on different statistical indicators (mean, variance, skewness, etc). Additionally,
we assess the effect of different physiological parameters (growth rate, partition errors,
etc) on cell size distribution. ☐ Chapter 3 introduces a mechanistic model that might explain the recently uncovered
added principle, i.e., selected species add a fixed size (volume) from birth to
division, irrespective of their size at birth. To explain this principle, we consider a
timekeeper protein that begins to get stochastically expressed after cell birth at a rate
proportional to the volume. Cell-division time is formulated as the first-passage time
for protein copy numbers to hit a fixed threshold. Consistent with data, the model
predicts that the noise in division timing increases with size at birth. We show that the
distribution of the volume added between successive cell-division events is independent
of the newborn cell size. This fact is corroborated through experimental data available.
The model also suggest that the distribution of the added volume when scaled
by its mean become invariant of the growth rate, a fact also veri ed through available
experimental data. ☐ In part 2 of this thesis, we study which strategies are implemented by a viral
species, ranging from bacteriophages to human immunodeficiency virus (HIV), in order
to exploit host resources. In chapter 4, we review the classical theory of viral-host
dynamics and describe the key knobs that viruses tweak to exploit a cell population.
This theory suggest that viruses might evolved to have infinite infectivity and virulence.
In the case of infectivity, chapter 5 gives an alternative to infinite infectivity: virus will
evolve to moderate infectivity because of local interactions. As an example, we study
a phage attacking a bacterial population. We include the effect of local interactions by
assuming that the phage needs to scape from bacterial death remains (debris). ☐ Infinite virulence is also challenged as evolutionary alternative for viral propagation.
In chapter 5 we study environments where availability of susceptible bacteria
fluctuates across time. Under such scenarios bacteria behaves contrary to classical
ecology theory: phages evolve to a moderate virulence (lysis time). We present this
insights through the use of the stochastic hybrid system framework. ☐ In chapter 7, we present a mathematical model of HIV transmission including
cell-free and cell-cell transmission pathways. A variation of this model is considered
including two populations of virus. The first infects cells only by the cell-free virus
pathway, and the second infects cells by either the cell-free or the cell-cell pathway
(synapse-forming virus). Synapse-forming HIV is shown to provide an evolutionary
advantage relative to non synapse-forming virus when the average number of virus
transmitted across a synapse is a su ciently small fraction of the burst size. ☐ HIV disease is well-controlled by the use of combination antiviral therapy (cART),
but lifelong adherence to the prescribed drug regimens is necessary to prevent viral rebound
and treatment failure. Populations of quiescently infected cells form a "latent
pool" which causes rapid recurrence of viremia whenever antiviral treatment is interrupted.
A "cure" for HIV will require a method by which this latent pool may be
eradicated. Current efforts are focused on the development of drugs that force the
quiescent cells to become active. Previous research has shown that cell-fate decisions
leading to latency are heavily in
uenced by the concentration of the viral protein Tat.
While Tat does not cause quiescent cells to become active, in high concentrations it
prevents a newly infected cell from becoming quiescent. In chapter 8, we introduce a
model of the effects of two drugs on the latent pool in a patient on background suppressive
therapy. The first drug is a quiescent pool stimulator, which acts by causing
quiescent cells to become active. The second is a Tat analog, which acts by preventing
the creation of new quiescently infected cells. We apply optimal control techniques to
explore which combination therapies are optimal for different parameter values of the
model.
Description
Keywords
Biological sciences, Applied sciences, Hybrid systems, Stochastic dynamics