Understanding individual heterogeneity in ageing from stochastic dynamics of sigma factor regulatory network in bacteria

Ageing is controlled at the molecular level via genetic and metabolic pathways. Ageing rates and patterns vary substantially even with fixed genetics and environment. However, molecular mechanisms of ageing generating individual heterogeneity are poorly understood.

Stochastic dynamics of gene expression can be a main source of the variation in cellular damage dynamics leading to individual heterogeneity in ageing.

The sigma factor regulatory networks constitute the core part of the bacterial gene expression networks and therefore play important role in deciding the fate of bacterial cells.

In this project, I focus on exploring the role and interplays of four major sigma factors rpoD, rpoS, rpoH, rpoN in individual E. coli cells.

I plan to (1) obtain these sigma factors’ expression dynamics and the demographic fates at individual E. coli cells in microfluidic platforms under fluorescent microscope; (2) process and analyze big image data in an automated manner; (3) develop mathematical models to interpret expression and demographic signals for damage and ageing.

I will combine the knowledge and methods from the disciplines of molecular biology, genetics, fluorescent microscopy and microfluidics, machine learning, population and evolutionary dynamics and mathematical modelling to shed light on the link from stochastic expression dynamics in sigma factor regulatory network towards individual variations observed in ageing fates.