Complexity Explorer Santa Few Institute

Uncovering volume dependence in stochastic gene expression

mentors: James Holehouse & Maell Cullen

Cells are akin to a soup of chemical reactions where transcription, translation, regulation and most of the events necessary for life take place. In order to function they must maintain homeostasis with respect to concentrations of mRNA and proteins, but generally the mechanisms through which this homeostasis occurs is poorly understood. This is especially true where one considers the growth of a cell in time, and the aim of this project is to begin to uncover the volume dependence of key mechanisms in gene expression, and how this relates to cellular homeostasis and the suppression of noise. By analyzing mother machine data, which allows for simultaneous protein fluorescence measurements and cell length measurements at a high time resolution, we can develop mathematical models to better
understand the molecular processes underlying transcription, translation, and degradation. Preliminary results seem to indicate that volume dependence is linear in the protein production rate, but more work needs to be done to understand volume dependence on the protein burst size and degradation rate. This project includes components of data analysis (from synthetic and/or real experiments, see [1]), stochastic modelling (master equations) and inference methods (maximum likelihood and/or Bayesian inference) and can be tailored to the students interests within these bounds.

[1] Tanouchi, Y., Pai, A., Park, H., Huang, S., Buchler, N. E., & You, L. (2017). Long-term growth data of Escherichia coli at a single-cell level. Scientific data, 4(1), 1-5.