Computational Models for Gene Silencing: Elucidating A Pervasive Biological Defense

Start Date: 09/01/2004
End Date: 08/01/2009

In recent years, an exciting new phenomenon has been discovered in biology, known variously as RNA interference (RNAi), post-transcriptional gene silencing (PTGS), or gene silencing. From the realization that RNAi is a naturally occurring cellular defense mechanism, biologists and pharmaceutical scientists have been quick to explore its potential for transgenic research, therapeutic intervention, drug discovery, and development of novel biological agents. RNAi technologies are rapidly evolving to achieve higher value and to fit new uses. A current trend is the development of high-throughput RNAi screening technologies and commercial enterprises built around such capabilities. The scientific study of RNAi is not only worthy of intrinsic scientific merit, but also important for its myriad applications in defense, public health, and national priorities facing our country.

This project is a multi-institutional, multi-disciplinary research effort to advance our understanding of RNAi mechanisms and to provide a resource for rapid analysis of RNAi effectiveness. Researchers from Virginia Tech and New York University are developing an integrated systems biology framework—CMGS (Computational Models for Gene Silencing)—that models RNAi function at both the molecular and cellular levels. CMGS will integrate molecular modeling and simulation techniques, physical annotation capabilities, curation of data from literature and experiments, as well as inferencing technologies to model and infer the effects that an RNAi intervention might have. The computational investigators leverage considerable expertise in building software tools useful to life science collaborators and in building scientific problem solving environments; the integration of enhanced tools into the CMGS system will provide a computationally powerful approach to pose questions about RNAi, analyze data from interference experiments, and reason about RNAi processes.

 

See: Project Description

Grant Institution: National Science Foundation

Amount: $1,267,410

People associated with this grant:

Alexey Onufriev
Lenwood Heath
Naren Ramakrishnan