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Title: On the coupling of noisy processes in biology to produce functional phenotypic variability
Author: Patange, Om
ISNI:       0000 0004 7968 4280
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2019
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Noise is ubiquitous in biology. Recent studies have demonstrated that both gene expression and physiological processes, such as growth, can be noisy. The cell-to-cell variation resulting from this stochasticity has been implicated in survival strategies for bacterial populations. However, it remains unclear how single cells couple gene expression with growth to implement these strategies. In this thesis we show how noisy expression of a key stress response regulator, RpoS, allows E. coli to modulate its noisy growth dynamics to survive future adverse environments. We first demonstrate that single cells in bulk, exponential phase cultures have heterogeneous rpoS expression. Combining microfluidics and time-lapse microscopy we reveal multi-generation RpoS activity pulses are responsible for this heterogeneity. We next show that RpoS and growth have stochastic dynamics and are anti-correlated. With a stochastic simulation of chemical reactions coupled to a deterministic cell growth model we show that a mutual inhibition loop between RpoS activity and growth rate is sufficient to capture the observed dynamics. We test our model by performing experimental perturbations and find good agreement between theory and experiment. Next, we demonstrate the functionality of this phenotypic variability by using the microfluidic platform to apply a short, intense period of oxidative stress. By tracking cells prior to the stress and testing for survival after the stress we reveal that E. coli prepare for sudden stressful events by entering prolonged periods of slow growth mediated by RpoS. This dynamic phenotype is captured by the RpoS-growth feedback model. Our synthesis of noisy gene expression, growth, and survival paves the way for further exploration of functional phenotypic variability.
Supervisor: Locke, James C. W. Sponsor: Microsoft Research ; European Research Council ; Gatsby Foundation ; Human Frontier Science Programme
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: noise ; rpos ; growth ; stress response ; e. coli ; phenotypic heterogeneity ; single-cell ; time-lapse microscopy ; quantitative biology ; stochastic simulation ; Gillespie algorithm ; Mother Machine ; microfluidic ; bet-hedging ; stochastic gene expression