Stochastic gene expression and lag time in bacteria

Fiori, Athos. Stochastic gene expression and lag time in bacteria. 2021, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: https://edoc.unibas.ch/83503/

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The survival of organisms in randomly fluctuating environments not only depends on their ability to grow in different conditions but also on the time needed to adapt to each new habitat. Recent works had shown that, like many other physiological quantities, the adaptation time fluctuates in a stochastic manner across single cells and that the underlying distribution can dramatically change across genotypes. To understand how natural selection may have acted on the distribution of single-cell lags we develop a mathematical theory of how the single-cell lag distribution determines the reproductive success at the population level. We show that lags at the population level are exponentially dominated by the shortest lags at the individual cell level. Consequently, analogous to the selection shadow theory of aging, there is virtually no selection against subsets of cells with very long lags, suggesting that persister-like phenotypes may very generally be expected to occur in microbial population. In addition, we show that the relationship between single-cell and population lags depends on the typical population size and that, while noisy single-cell lag distributions might be beneficial, they are only effective at large population sizes. This result suggests that, while large populations can employ bet-hedging strategies to deal with unexpected environmental changes, small populations will require regulated sense-and-response strategies in order to ensure short population lags. Experimental validation of these results can be done trough dedicated microfluidic devices combined with time lapse microscopy images. Unfortunately, these methods often lack the direct observation of important gene expression variables as the mRNA or the ribosome levels. We developed a dedicated biophysical model of gene expression which, together with a specific Bayesian inference scheme, allows to predict the dynamics of these latent variables. We first tested this method on time series data of single cell growth. The results show that cells growing in different media have similar cell-cycle and longer scales dynamics.
Advisors:van Nimwegen, Erik
Committee Members:Neher, Richard and Kutalik, Zoltán
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (van Nimwegen)
UniBasel Contributors:van Nimwegen, Erik
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:14247
Thesis status:Complete
Number of Pages:101
Identification Number:
  • urn: urn:nbn:ch:bel-bau-diss142476
edoc DOI:
Last Modified:10 Sep 2021 04:30
Deposited On:09 Sep 2021 07:29

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