We study the organizing principles of the genome and how these principles regulate cell identity and developmental switches. We combine Biochemistry and Biophysical methods such as NMR and Hydrogen-Deuterium Exchange-MS with Cell Biology, and Genetics to explore genome organization across length and time scales and understand how cells leverage the diverse biophysical properties of chromatin to regulate genome function.
The Sherlock lab uses experimental approaches to understand the evolutionary process, specifically interested in i) what's the rate of beneficial mutation, ii) what is the distribution of fitness effects of beneficial mutations, iii) what are the identities of beneficial mutations (and are they gain or loss of function, are they recessive, dominant or overdominant, are the genic or regulatory?) and iv) how do each of these change as a function of genotype, ploidy and environment.
Dr. Cong's group is developing novel technology for genome editing and single-cell genomics, leveraging scalable methods inspired by data science and machine learning and artificial intelligence.
The Hernandez-Lopez Lab works at the interface of mechanistic, synthetic, and systems biology to understand and program cellular recognition, communication, and organization. We are currently interested in engineering biomedical relevant cellular behaviors for cancer immunotherapy. We are also launching new multidisciplinary projects.
We study the genetic and molecular mechanisms that regulate proliferation and differentiation in adult stem cell lineages, using the Drosophila male germ line as a model. Our current work is focused on the switch from mitosis to meiosis and how the new gene expression program for cell type specific terminal differentiation is turned on. One emerging surprise is the potential role of alternative processing of nascent mRNAs in setting up the dramatic change in cell state
We aim to understand the genetic basis of diabetes and related metabolic conditions and to use this to leverage a better understanding of what causes diabetes and how we can improve treatment options for patients. Our work is predominantly focused on understanding what causes pancreatic islets to release insufficient insulin to control blood glucose levels after a meal in patients with type 2 diabetes, but often extends to efforts to relate this to metabolic dysfunction in other relevant tissues such as fat and liver.
We are a chemical biology laboratory focused on the development of technologies to map molecules, cells, and functional circuits. We apply the technologies to understand signaling in the mitochondria and in the mammalian brain.
The gene encoding the p53 transcription factor is the most commonly mutated gene in human cancer, yet we lack a complete understanding of how its loss promotes cancer and how to target this pathway therapeutically. My lab studies p53 in the context of two very deadly and common cancer, pancreatic cancer and lung cancer, to understand how p53 loss promotes tumor initiation and progression. We are investigating not only how p53 mutation changes tumor cells themselves but also how these changes in tumor cells alter the cells of the tumor microenvironment to promote cancer development.
The Sherlock lab uses experimental approaches to understand the evolutionary process, specifically interested in i) the beneficial mutation rate, ii) the distribution of fitness effects (DFE) of beneficial mutations, iii) the identities of beneficial mutations (are they gain or loss of function, are they recessive, dominant or overdominant, are the genic or regulatory?) and iv) how do each of these change as a function of genotype, ploidy and environment.
The Steinmetz group develops experimental approaches to read, edit and write entire genomes across scales. By applying these technologies, members of the lab aim at understanding the genetic basis of complex phenotypes, the mechanisms of transcription, and the molecular systems underpinning disease. One of the most daunting obstacles in biomedicine is the complex nature of most phenotypes (including cancer, diabetes, heart disease and several rare diseases) due to epistatic interactions between multiple genetic variants and environmental influences.