Research Areas

Much as there has been a revolution in the area of genomics and proteomics, a similar revolution has occurred in the area of computational biology and bioinformatics. Computational approaches to understanding protein structure, imaging of biological systems, and the analysis of large data sets are expected to contribute enormously to the analysis of biological systems and problems. The Yale BBS program offers an extraordinary opportunity for research in these exciting areas.

Fundamentally, the genome encodes the structures of molecules, the machines that carry out the work of the cell. Analyzing structures involves dealing with complex 3D shapes and simulating them based on physical principles. One of the grand challenges to computational biology is ab initio prediction of protein structures as well as elucidation of structure-to-function relationships. Yale has unique strength in both theoretical and empirical structural biology including rna and protein structure determination, protein packing, protein classification, and macromolecular motions.

A central problem in bioinformatics is the analysis of genomic information, leading up to the entire human genome. Research in whole genome analyses include finding genes and pseudogenes, assigning protein structures and functions to known genes, and comparing genomes in terms of a wide variety of features. Closely tied to this work is the development of computational approaches for comparing and characterizing sequences and predicting structure and function (e.g. identifying membrane proteins) from sequence. A major new development is the advent of functional genomics information with standardized experimental information over the entire genome. For example, one of the major types of functional information is genome-wide gene expression data. All members of the Track faculty are interested in the general problem of achieving an integrative and systems understanding of the whole genome.

Yale has a diverse set of activites in the areas of genomics and proteomics that are relevant to computational biology and bioinformatics. The genomes and proteomes of a wide variety of organisms are currently being studied: these include yeasts, flies, mice, worms, viruses, humans, and plants. In addition, Yale has in place a Center of Excellence in Genomic Science. Students also have access to a variety of state-of-the-art technologies. These include DNA microarrays, transposon tagging, gene and disease locus mapping, and many large scale technologies that were first developed at Yale, including in situ hybridization, rolling circle amplification, protein microarrays (the first proteome chip), and large-scale protein localization strategies. Core facilities containing state-of-the-art equipment are available for students to obtain hands-on experience with many important technologies.

All of bioinformatics deals with biological information. What is the best way of storing and organizing this on the computer? How does one best connect different types of information (e.g. protein features and expression data)? How can we interconnect distributed databases, handle terabytes of data, and provide biologically meaningful queries? Storing, managing, and accessing information is a non-trivial complex process that is one of the foundations of bioinformatics. Yale has long-standing strength in biomedical informatics as well as research in the development and interoperation of heterogeneous biomedical databases and related tools.

Theoretical analysis and modeling is becoming increasingly important in computational biology and bioinformatics. Yale has a multitude of activities in statistical genomics, molecular evolution, computational development, cell simulations, and molecular dynamics. In particular, many research problems involve abstract modeling and questions. Example questions include: What are the functional modules of an integrated genome? Can we understand molecular processes as a form of computation? Both theoretical and practical biological problems generate unique algorithmic and computational problems including: alignments, motif searching, combinatorial optimization, machine learning, and high-performance computing. For example, even simple processing of the extremely large-scale data generated by state-of-the-art genomics facilities requires considerable software and hardware development. Yale has experts in algorithmic research, statistics, and advanced computing.