Stochastic Processes as a Source of Cell to Cell Diversity and Cellular Ageing
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Even populations of monoclonal cells exhibit phenotypic diversity. There are several sources generating such diversity, including stochasticity in the dynamics of gene expression, and the stochastic partitioning of molecules during division. This thesis focuses on the construction and simulation of a realistic model of gene expression and on the stochastic partitioning of cellular components during cell division. First, we present and make use of statistical methods to extract information on the kinetics of gene expression from live-cell measurements at the single RNA molecule level. This information allows us to characterize the kinetics of the multi-stepped process of transcription initiation, including the degree of noise in transcript production, as well as the kinetics of partitioning of protein aggregates by the cell’s poles. A model of single gene expression in a growing population of cells and a model of ageing in bacteria are then constructed based upon these measurements. Next, we present a new simulator which uses the Stochastic Simulation Algorithm to simulate the dynamics of intracellular processes in populations of cells, each of which able to grow and divide with random partitioning of molecules. Cells are represented in the simulator by compartments that can be created and destroyed at runtime. Logarithmic simulation algorithms and efficient data structures were designed and are here presented, which minimize the computational cost of simulating the dynamics of large cell populations that involve a large number of chemical reactions.