It is well-known that gene expression response to environmental stimuli may follow different dynamics over isogenic cell populations. Modern experimental devices, such as fluorescence microscopy combined with microfluidic devices, provide the means to quantify this variability across cells and even within single cells, thus allowing for single-cell mathematical modelling of gene expression dynamics.
In this talk I will discuss mixed-effects modelling of gene expression variability in yeast. Inspired by pharmacokinetics where it was originally developed, Mixed-Effects (ME) modelling emphasizes the nature of different cells as being part of a common population. Leveraging this, ME identification methods are capable of robustly inferring population properties (whence individual properties in a second step) from limited amounts of data per individual, and few individuals. We will show performance and robustness of ME identification in the modelling of yeast osmotic shock response from single-cell experimental data, and discuss the interest of the results in terms of the exploration of the biological properties of the system.
Modelling cellular heterogeneity of gene expression: The Mixed-Effects approach
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