The recent technological advances in molecular biology
forces us to take a more quantitative take on the subject. Mathematical
and physical approaches in biology cover a wide variety of topics. They
incorporate well established disciplines like molecular biophysics and
structural biology to resurgent areas like system level dynamical analysis of
Professor Gyan Bhanot
The availability of high throughput data has created the exciting new field of systems biology which is changing the face of clinical care and leading to new advances in our understanding of the genetic and molecular underpinnings of phenotypic diversity and disease processes. The research in my group is focused on developing algorithms to extract novel information from such data sets. In cancer biology, we are trying to identify bio-markers which can identify disease initiation, progression and metastasis. We then analyze these to understand altered biological mechanisms in the disease state. The goal of this research is to develop assays to quantitate disease risk, assess metastatic potential, identify pharmaceutically relevant drug targets and help the clinican to provide better care. We work closely with surgeons, clinicians and researchers in several hospitals to ensure that this research remains focused on the patient and is relevant to improving patient care and treatment outcome. In population genetics, we are trying to understanding the genetic basis of complex phenotypes in human diseases and evolution. We apply novel methods to analyze SNP and sequence data to study the interplay between selection, adaptation and population dynamics, the role of linked polymorphisms in complex disease processes and in the interconnected evolution of diverse organisms, from viruses to mammals. Contact: firstname.lastname@example.org, 848-391-7508.
We use computer simulations and statistical mechanics to study the structure, function, folding, and dynamics of proteins. Current research projects include the study of mechanisms for molecular recognition by proteins, homology modeling, and protein dynamics on longer time scales. We are interested in the interplay between computational models and experimental studies of biological assemblies at different levels of resolution. We are engaged in research in the area of Computational Molecular Biology - including the development of computational tools which will be useful for predicting the functions and structures of newly sequenced genes, as well as for studying the basic physical principles of protein folding.
Professor Alexandre Morozov
The goal of my current research is to analyze gene regulation on a whole-genome scale using methods from physics and statistics, including the stochastic and deterministic variations caused by environmental signals, developmental stage, and disease state. Broadly speaking, I seek to improve the current understanding of the "transcriptional and post-transcriptional regulatory code" that links the DNA sequence with gene expression levels. To this end, I and the members of my lab study chromatin structure and its effect on gene regulation,biophysical models of protein evolution, and evolution and energetics of DNA-binding proteins.
Our research combines a variety of computational approaches (e.g.,Metropolis-Monte Carlo, energy minimization, polymer statistical mechanics, normal mode analysis) with developments in polymer theory to study the sequence, structure, properties, and function of nucleic acids at different levels of resolution. Problems of current interest include: elastic models of the long threadlike DNA double helix that account for its local structure, direct recognition, mesoscopic looping, super coiling and knotting; conformational changes, including the overstretching and over twisting of single molecules; electrostatic effects governing macromolecular structure and recognition; interactions with proteins, drugs, and carcinogens.
Our research aims to understand information processing in the cellular enviroment. We are mostly focused on studying signaling and transcription. Our interests range from information theory of signaling cascades to bioinformatic tools for discovery of the transcriptional networks that affect gene expression as a result signaling events. We are also analyzing bistable genetic systems that can hold two different states for the same external cue. Currently, to complement our computational and theoretical work, we have set up a wet lab in the Waksman Institute to test our predictions and to generate data for further model building.
Revised June, 2010