2018.06.21 Towards quantitative mechanistic prediction in biology

2019-07-07 00:56:12 2



    目:Towards quantitative mechanistic prediction in biology

报告人:Dr. Lucas B. Carey

Systems Bioengineering program, Department of Experimental and Health Sciences, Universitat Pompeu Fabra



主持人汤超 教授

摘 要:

Understanding the effects of mutations and how phenotypes are encoded in the genome are two of the primary goals of modern biology. The ability to make quantitative predictions as to the effects of complex genetic changes will enable breakthroughs in personalized medicine, designer organisms for agriculture and bioremediation, and many other applications.

However, despite major advances in measuring genotypes and phenotypes, the consequences of most mutations still cannot be predicted.

I will discuss some of the complexity that makes phenotype prediction so hard and the approach that my lab takes to solve these problems. Generally, we combine mathematical modeling with high-throughput quantitative experiments to identify new mechanisms by which genetic and non-genetic heterogeneity in single cells affects the mapping from genotype to phenotype.

I will first discuss how complex higher-order genetic interactions affect evolutionary trajectories. Genetic variants that are pathogenic in humans are often harmless in other organisms, and the same mutation can be neutral, deleterious, or even beneficial in different individuals. We measured fitness for over 4,000,000 variants of a single gene. We found that, while a given mutation is often neutral, beneficial or deleterious depending on the genetic background in which it occurs, this complex non-linear behavior can be predicted from sequence. 

Next I will discuss recent results on the mechanisms by which non-genetic variability in metabolic state can generate semi-heritable phenotypic variability. We performed a high-throughput time-lapse microscopy screen in which we measured the proliferation of single cells for over 1500 mutants in budding yeast. We found that cell-to-cell variability in mitochondrial state is responsible for generating phenotypic variability in proliferation rates, mutational impact, and antifungal resistance.   

Finally, I will present on-going and future work involving high-throughput quantitative systems biology to understand the molecular causes for massive gene copy-number increases during the evolution of fungal pathogenesis and the molecular causes of heterogeneity in drug resistance among tumor cells. 


2013-now Assistant professor, Dept. of Experimental & Health Science, Universitat Pompeu Fabra

2010-2013 Postdoc, Dept. of Computer Science (Prof. Eran Segal), Weizmann Institute of Science, Israel

2007-2010 High Performance Computing programmer. Cold Spring Harbor Labs, United States.

2004-2010 Ph.D., Genetics Dept. (Prof. Bruce Futcher), Stony Brook University, United States