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Lucas Carey



Lucas Carey

Lab Homepage: http://cqb.pku.edu.cn/careylab


Research Area:

The goal 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. 

The following are some examples of questions we’re working on:


  • Why do mutations and drug treatments have different outcomes in different individuals? Within an isogenic population of cells, not all microbes are killed by an antibiotic and not all cancer cells are killed by chemotherapy. In addition, the effect of a mutation varies across individuals; identical mutations often have no effect in some people but result in a severe disease phenotype in others. Why are only some individuals affected by a drug or mutation?


  • Machine learning to predict mutational impacts in heterogeneous genetic backgrounds. The effect of each mutation depends on the genetic background in which it occurs. To discover fundamental principles that govern how genetic interactions determine phenotype we build large libraries containing millions of genetic variants and measure the phenotype of each genotype. To understand the resulting large high-dimensional datasets we develop novel machine-learning based approaches to quantify and predict the impact of each mutation on phenotype.


  • What mechanisms result in the predictable evolution of drug resistance during treatment? Some tumors, fungi and bacteria strains consistently and reproducibly acquire multidrug resistance in both patients and lab experiments, while others do not. Why? How is the ability to evolve a given phenotype encoded in the genome?


Selected Publications:

1. Dhar R, Missarova A, Lehner B*, Carey LB*. Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation. eLife. 2019.

2. Schikora-Tamarit MÀ, … Carey LB. Promoter Activity Buffering Reduces the Fitness Cost of Misregulation. Cell Reports. 2018. 

3. Espinar L, Schikora Tamarit MÀ, Domingo J, Carey LB. Promoter architecture determines cotranslational regulation of mRNA. Genome Research. 2018.

4. Pokusaeva V, Usmanova D, Putintseva E, … Carey LB*, Kondrashov FA*. Experimental assay of a fitness landscape on a macroevolutionary scale. bioRxiv. 2018.

5. Ivanova T, Maier M, Missarova A, Ziegler-Birling C, Carey LB*, Mendoza M*. Budding yeast complete DNA replication after chromosome segregation begins. bioRxiv. 2018.

6. van Dijk D, Sharon E, Lotan-Pompan M, Weinberger A, Segal E*, Carey LB*. Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators. Genome Research. 2017.

7. Carey LB. RNA polymerase errors cause splicing defects and can be regulated by differential expression of RNA polymerase subunits. eLife. 2015.

8. van Dijk D, Dhar R, Missarova AM, Espinar L, Blevins WR, Lehner B, Carey LB. Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage. Nature Communications. 2015.

9. van Dijk D, Manor O, Carey LB. Publication metrics and success on the academic job market. Current Biology. 2014.

10. Carey LB, van Dijk D, Sloot PMA, Kaandorp JA, Segal E. Promoter sequence determines the relationship between expression level and noise. PLoS Biology. 2013.

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