Scholar Profile

Ram Samudrala

Associate Professor
Department of Microbiology
University of Washington
Seattle, WA 98195-7242
Voice: 206-732-6122
Fax: 206-732-6055
Email: ram@compbio.washington.edu
Personal Homepage
2002 Searle Scholar

Research Interests

The Big Picture: from genotype to phenotype

How does the genome of an organism specify its behaviour and characteristics?
or
How does life happen, and can we simulate it?

Summary

A fundamental biological challenge is to understand how the linear information in an organism's genome is processed to produce the resulting behavior or phenotype. Genes, made up of DNA, are transcribed into RNA, and translated into proteins which together form the vast majority of functional elements in an organism. Evolutionary processes ensure that these functional elements interact with their environment in a manner that is beneficial to the organism, using a variety of molecules to catalyse reactions, recognise cellular signals, build cellular structures, and to perform a host of other diverse biological functions.

Our research elucidates these processes by developing computational algorithms to model, annotate, and understand the relationships between the sequences, structures, functions, and interactions of proteins, DNA, RNA, and metabolites, at both the molecular and the genomic/systems levels. The goal is to develop a coherent picture of the mechanistic basis (wiring diagram) of molecular and organismal structure, function, networks, and evolution within a fundamental scientific framework.

Our specific aims are to develop novel methods to:

Structure: Predict atomic level three dimensional structures of biologically important molecules (such as proteins, DNA, RNA, and small molecules with emphasis on proteins) given their linear chemical structure (sequence).
Function: Predict function using the resulting models with the aid of available experimental information.
Interaction: Predict interactions between and among these molecules, including biological substrates and inhibitors.
Systems: Predict the behaviour of pathways, systems, and whole cells by integrating the structure, function, and interaction information with the expression (copy number) of these molecules.
Evolution: Study the evolution of these biological molecules to ask and answer questions on origin of life and the conditions necesary to nurture it.
Design: Design novel biological entities not observed in nature using our prediction methodology and verify designed constructs in the laboratory.
Application: Apply the methodologies developed to study specific biological problems of interest in the areas of medicine and nanotechnology.
Infrastructure: Develop an infrastructure to publish the integrated information so that it is useful for biologists to pose and answer precise scientific questions about molecular, systems and organismal biology.

More detailed information on these methods are available as part of our ongoing research and also our list of publications.

Implications

We expect that the biological role of every protein can eventually be deduced from its three dimensional structure in the context of its environment in the cell. This information will enable us to probe that organism's cellular pathways with an exquisite degree of sensitivity and also help us understand and treat infectious and inherited disease in an increasingly efficient and rational manner. The development of algorithms and tools to understand organismal genomes will have practical utility for pharmacogenomics and genetic engineering, and will be of use to the general research community to pose and answer ever more precise biological questions.

Understanding organismal biology from a genomic perspective requires expertise in several scientific disciplines, including computing science, mathematics, physics, chemistry, and biology. The problems that need to be solved generally involve exploration of large search spaces and finding objects of interest within those spaces, as well as managing the large amount of data produced and making predictions from analysis of the data. Thus our research has significance in not only answering biological questions, but is also relevant for solving problems of a similar nature in other scientific disciplines.

Long term goals

Our research involves integrating knowledge from the fields of computing science, mathematics, biology, physics, and chemistry to:

Achieve better understanding of protein structure, protein function, and molecular evolution.
Analyse genomes and study interactions of individual genes and their corresponding proteins to understand and model their roles in infectious and inherited disease.
Model complete cellular pathways and systems within an organism of interest using knowledge about the structure of proteins, protein expression, protein-protein and protein-substrate interactions.
Develop therapeutics and molecular machines to improve human health and quality of life.
Devise simulations of all forms of life we can observe and others we can imagine.