个人简历

Education:
B.S., Stony Brook University, USA, 2003　
Ph.D. Stony Brook University, USA, 2010

Academic experiences:
2013-2018: Assistant Professor, Universitat Pompeu Fabra, Barcelona, Spain.
2010-2013: Postdoc with Eran Segal, Weizmann Institute of Science, Israel.

research summary
Understanding how an individual will respond to a drug, or the effect of a mutation on some phenotype, is the major goal of modern biology.
The ability to predict the outcomes of genetic and environmental perturbations is necessary for everything from personalized medicine to building designer organisms.
Our high-throughput quantitative systems biology research group combines mathematical modeling with measurements of thousands to millions of genotypes and single cells to understand drug resistance and cell growth at the single-cell level.
Our goal is to be able to predict the of a drug or mutation in single cells, and to understand why seemingly identical cells respond differently to the same treatment.
To do so we use high-throughput time-lapse microscopy, flow-cytometry, single-cell RNA & DNA sequencing, machine learning and quantitative data-driven mathematical models to generate novel hypotheses and to draw conclusions from complex high dimensional biological data.
The following are some examples of questions we’re working on:
Why do mutations and drug treatments have different outcomes in different individuals? Within a single population of cells, not all cells will be killed by a drug, and identical mutations can have no effect in some individuals but result in a severe disease phenotype in others. Why are only some individuals and single cells 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 genotype determine phenotype we build large libraries containing millions of genetic variants, measure the phenotype of each variant, and develop novel computational 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 in lab experiments, while other genotypes do not. How is the ability to evolve a phenotype encoded in DNA?
Job Openings
北京大学定量生物学中心Carey实验室招聘实验技术员/科研工作者
本课题组招聘一名对科研充满热情的技术员，以及具有一定英文沟通能力、拥有科学好奇心的科研工作者，参与高通量测序分子生物学和遗传学实验。
关于Carey实验室
了解突变的影响以及表型是如何在基因组中被编码，是现代生物学的两个主要目标。对基因突变的影响进行定量预测能在个性化医学、农业、生物修复和设计生物以及其他许多领域中取得突破。然而，尽管现在生物科研在测量基因型和表型方面取得了重大进展，大多数突变的后果仍然无法预测。
为了实现生物学中的定量预测，我们将数学模型与高通量定量实验相结合，以确定单细胞遗传和非遗传异质性影响基因型到表型映射的新机制。我们使用高通量延时显微镜、流式细胞术、单细胞RNA和DNA测序、机器学习和定量数据的数学建模来理解和预测单细胞和生物体的命运。
必要能力
• 优秀的中英文书写能力和口语表达能力
• 硕士学位或学士学位，具有丰富的分子生物学实验经验。
加分能力
• 酵母遗传学
• 构建质粒、染色质IP、qPCR、蛋白质纯化
• 流式细胞术分析和分类
• 第二代测序建库（尤其是mRNA-seq）与质量检测
• 计算机编程（任何语言）

近期论文

Selected publications:
1. Schikora-Tamarit, MA ... Carey, LB. Promoter activity buffering reduces the fitness cost of misregulation. Cell Reports. (2018).
2. Espinar, L*, Schikora Tamarit, MÀ*, Domingo, J*, Carey LB. Promoter architecture determines cotranslational regulation of mRNA. Genome Research. (2018).
3. van Dijk, D*, Sharon, E*, … Segal, E†, Carey, LB†. Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators. Genome Research. (2017).
4. van Dijk, D, … Carey, LB. Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage. Nature Communications. (2015).
5. Carey, LB. RNA polymerase errors cause splicing defects and can be regulated by differential expression of RNA polymerase subunits. eLife. (2015).
6. van Dijk, D., Manor, O. & Carey, LB. Publication metrics and success on the academic job market. Current Biology. (2014).
7. Carey, LB*, van Dijk, D*… Segal, E. Promoter sequence determines the relationship between expression level and noise. PLoS Biology. (2013).