UAlberta Mathbio Seminar: Changhan He

  • Date: 03/14/2022
  • Time: 14:00
Changhan He, University of California (Irvine)



E. Coli Growth and Spatiotemporal Patterning by Partial
Differential Equations


Synthetic biology has become an important field of science focusing on designing and engineering new biological parts and systems or redesigning existing biological systems for useful purposes. In Synthetic biology's entire history, mathematical modeling has always been an indispensable approach to predict experimental outcomes, improve experimental design and obtain a mechanistic understanding of the biological systems. Escherichia coli (E. coli) are the most studied microbes in science and medicine due to their simplicity and relatively low cost in experiments. The dynamics of E. coli, including cell growth and colony morphogenesis, is a fundamental topic. Our research work is focused on describing bacteria colony growth and pattern formation by using spatiotemporal data verified PDE models. In the first part, we introduce a population-based reaction-diffusion model to present E. coli colony growth under different control factors. We challenge the model by using experimental E. coli colony growth data and we found that the model is capable of predicting the whole colony expansion process in both time and space under different conditions. In the second part, we model the circuit-driven bacterial pattern formation by using reaction-diffusion equations. We show that the model can systematically describe the biological system and predict the experimental results. In the third part, we combine the above two models to capture the moving boundary during the pattern formation period. This new model allows us to simulate both bacterial patterning and colony growth all at once. We think this multiscale modeling approach can bring us valuable insights and interesting potential questions, and more importantly, it is applicable to a host of broader biology topics.

Other Information: 

Seminar time: 2:00pm Pacific Time/3:00 Mountain



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Meeting ID: 915 8083 8150

Passcode: 32123