The Program for Mathematical Genomics is an interdisciplinary effort to devleop quantitative approaches to understand biological systems. As biology and medicine are becoming extremely data rich disciplines, there is an urgent need for algorithms to analyze data and mathematical models to interpret the results. Genomics, the study of the genomes of organisms, is becoming a paradigm of the quantification of biological sciences due to the quality and large amounts of data, underscoring the critical need for data analytic techniques and mathematical models for interpretation. Recent developments have led to the identification of the genetic causes across many different diseases.
PMG brings together collaborative research opportunities across several disciplines: computer science, enginering, biological sciences, statistics, evolutionary biology and more. Take a look at some of the exciting areas PMG is exploring:
The AlQuraishi Lab uses machine learning to model biological molecules and their interactions, and how such interactions give rise to signaling networks central to multiple diseases, principally cancer. Methods developed in the AlQuraishi Lab have helped explain how mutations arising in tumors alter the structure and function of proteins to make them oncogenic.
David M. Blei's research includes Topic Modeling, Probabilistic Modeling and Approximate Bayesian inference. Most of the group's published work is attached to open-source software. See the Blei Lab's GitHub page.
Iuliana Ionita-Laza's main research interests lie at the interface of statistics and genomics. She is interested in developing statistical and computational methods for the analysis of high-dimensional genetic and functional genomics data.
The Korem Lab develops high-resolution bioinformatic and machine-learning approaches to identify mechanisms of interactions between humans and their microbiomes, with the goal of developing microbiome-based therapeutics and diagnostics. Their work focuses on the role of the microbiome in adverse pregnancy outcomes and in cancer.
The Pe'er Lab develops and applies computational methods for the analysis of high-throughput data in germline human genetics; their researchers focus on characterizing genetic variation that is unique to isolated populations, including the effects of such variation on phenotype.
The Przeworski Lab is interested in modeling how genetic and evolutionary mechanisms give rise to and maintain variation, and in learning from patterns of genetic variation about the underlying processes involved. Work in the group combines large-scale genomic data analysis, statistical modeling and data collection.
Main scientific interests of the Rabadan Lab include modeling and understanding the dynamics of biological systems through the lens of genomics. The group's work focuses on three distinct topics: cancer, infectious diseases and electronic health records.
Researchers in the Sella Lab study the evolutionary processes that give rise to genetic and phenotypic differences between individuals, populations and closely related species. They use mathematical models to better understand these processes, and are particularly interested in the evolutionary processes behind adaptation and disease.
The Shen Lab aims to identify genetic causes of developmental disorders and to understand the dynamics of human adaptive immune system under normal and disease conditions. To this end, their researchers are developing computational and statistical methods and analyzing data generated by high-throughput genomic technologies.
Chris Wiggins' area of focus include analysis of microarray data, comparative genomics and population genetics, statistical inference in single-molecule biophysics, and biological network inference and analysis.
Research in Andrew Yates' group integrates theoretical and computational tools with more traditional experimental approaches to study lymphocyte dynamics. Their aim is to develp a mechanistic understanding of the rules underlying lymphocyte development, homeostasis and their trajectories in response to antigen challenge.