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Analyzing and Identifying Stochastic Models Using Flow Cytometry and Fluorescence Microscopy
Abstract: The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant phenotypic differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques.
In this tutorial, we will illustrate stochastic methods to analyze and extract additional information from this data. We show how random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.
