The Chemical Engineering Department is hosting a special graduate seminar on Deciphering the complexity of microbial systems.
Abstract:
Microbes are abundant and diverse in all ecosystems, playing essential roles in climate change, agriculture, energy, and human health. The microbial communities are very complex, typically consisting of thousands of microbial species and trillions of microbial cells. The progress on microbiology, such as cell cultures, molecular biology, and multi-omics, have advanced our understanding of the complex microbial system. However, there are still many challenges: how to cultivate and domesticate functional microbes in the lab, what the microbial gene functions are, how microbes interact with each other, how microbes respond to the environmental perturbations, and in which way we can expedite the design-build-test-learn cycle in the synthetic biology pipeline. In this seminar, Dr. Fangchao Song is going to talk about how he tackles these challenges using droplet microfluidics, functional genomics, high throughput sequencing, and mathematical modeling.
Biography:
Dr. Fangchao Song is a postdoctoral scholar at Lawrence Berkeley National Laboratory (LBNL) working with Dr. Adam Arkin. He works on developing high throughput methods to reveal microbial interactions and profile microbial phenotypes in complex communities under the DOE funded project of Ecosystems and Networks Integrated with Genes and Molecular Assemblies. Prior to joining LBNL, he completed his Ph.D. in Chemical Engineering with Dr. Dacheng Ren at Syracuse University, where he demonstrated bacteria can sense and respond to material stiffness like eukaryotic cells, and identified genes that are related to the bacterial mechanosensing. Dr. Song received his B.S. in Shandong University and M.S in Zhejiang University, both in Chemical Engineering, where he worked on polymer synthesis and mathematical modelling. Having been trained as both Chemical Engineer and Microbiologist, Dr. Song’s future research will focus on using engineering principles, modeling, and genetics to solve problems related to microbial systems