Quantitative high-throughput systems biology: 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 cannot be predicted.
The main focus of our group is to determine how the genotype-to-phenotype map is modulated by both genetic and non-genetic heterogeneity. To do so we take a high-throughput quantitative systems biology approach to determine the genetic and non-genetic determinants of phenotypic variability, with a focus on evolution, gene expression, proliferation and drug resistance in single cells.
We use high-throughput time-lapse microscopy, flow-cytometry, single-cell RNA & DNA sequencing, machine learning and quantitative data-driven mathematical models to predict the fates of single cells and of organisms.
- Why do identical mutations and drug treatments have different outcomes in different cells?
- Machine learning to predict mutational impacts in heterogeneous genetic backgrounds.
- What mechanisms result in the predictable evolution of drug resistance during treatment?
A PhD in biology, computer science, physics, or any computational and/or experimental field
- Yeast genetics and molecular biology
- Cell-culture and/or ES cells
- Flow-cytometry analysis and sorting
- Microscopy and computational image analysis
- Computer programming (any language)
Read more at: http://cqb.pku.edu.cn/careylab
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