Looking for postdocs interested in using high-throughput synthetic genetics and mathematical modeling to understand fundamental mechanisms in proliferation and gene expression at the single-cell level

Towards quantitative mechanistic prediction in biology

Lucas Carey, a new associate professor at the Center for Quantitative Biology at Peking University in Beijing, is looking for postdocs.

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.

Example projects

  • Why do identical mutations and drug treatments have different outcomes in different single cells?
  • Machine learning to predict mutational impacts in heterogeneous genetic backgrounds.
  • What mechanisms result in the predictable reproducible evolution of drug resistance during treatment?

Necessary qualifications

A PhD in biology, computer science, physics, or any computational and/or experimental field

Optional qualifications

  1. Yeast genetics and molecular biology
  2. Cell-culture, particularly cancer cell lines and drug screening
  3. Flow-cytometry analysis and sorting
  4. Microscopy and computational image analysis
  5. Computer programming (any language)

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Send your C.V. to for more information.

new paper: A genome-scale quantitative time-lapse microscopy screen implicates mitochondria in single cell variation in proliferation and drug resistance in yeast

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell variation in proliferation, drug resistance and mutation outcome


Riddhi Dhar, Alsu Missarova, Ben Lehner* & Lucas Carey*

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we developed a high-throughput automated microscopy assay and used it to quantify the impact of deleting >1,500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondria state – but not content – varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.


Figure 1: High-throughput analysis of single cell proliferation rate heterogeneity (A) High throughput microscopy setup – log phase yeast cells were diluted onto conA coated microscopy plate using Biomek NX liquid handling system to have similar cell density across wells. Cells were observed using an ImageXpress Micro system. Images were processed using custom scripts and data for area of microcolony vs. time were obtained. The points in the area vs. time graph show actual data and the solid lines show lowess fits. Data collected from all fields of view in a well constitute a microcolony proliferation rate distribution for a strain. The common lab yeast strain BY4741 (WT) has ~10% slow proliferating sub-population. The density shows mean density and the shaded areas in grey represent ±1 s.d. value at each point. The dotted red line shows the expected proliferation distribution if it were normally distributed. (B) Natural strains of yeast [39] also have slow proliferating sub-populations. Each point represents data for one strain. Solid lines show median value. (C) WT strain re-created the original proliferation distribution even after 20 generations of growth. The plot shows data from two replicate measurements.