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Your Environment. Your Health.

Progress Reports: Boston University: Bioinformatics and Molecular Modeling Core

Superfund Research Program

Bioinformatics and Molecular Modeling Core

Project Leader: Stefano Monti
Co-Investigator: Sandor Vajda
Grant Number: P42ES007381
Funding Period: 2000-2020
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

Learn More About the Grantee

Visit the grantee's eNewsletter page Visit the grantee's eNewsletter page Visit the grantee's Twitter page View the grantee's Factsheet(377KB)

Progress Reports

Year:   2019  2018  2017  2016  2015  2014  2013  2012  2010  2009  2008  2007  2006  2005  2004 

Computational biology, or doing the math rather than long-term, expensive laboratory studies, is now an essential element to any biomedical study. For example, the ability to picture molecules in 3-D computer "poses" is an important way to understand how receptors, such as those studied in other projects (Environmental PPARγ Pathway Activators: Multifaceted Metabolic Disruptors Impacting Adipose and Bone Homeostasis Project, Mechanisms and Impacts of PCB Resistance in Fish Project, and A Novel Mechanism of Ortho-PCB-induced Toxicity: Targeting Nuclear Receptors in Brain of Fish Project) function and, by inference, how environmental chemicals alter their function. It’s like using Google to answer questions rather than leafing through an encyclopedia. Towards these ends, the Bioinformatics and Molecular Modeling Core (BMMC) has had extremely productive interactions with the three projects listed above in experimental design, data generation, data processing, analysis, and computer modeling. Indeed, the BMMC has constructed 3-D computer images of protein receptors in two of the three projects that the BMMC serves, determining where chemicals bind to the receptor and, in some cases, to predict what chemicals are likely to bind. Interaction with the projects also motivated the Core’s development of mathematical tools for the robust and reproducible analysis of gene data, such as defining the profile of genes affected by PCB exposure in mice, humans, and fish, the latter including sentinel killifish populations from a Superfund site (New Bedford Harbor) and a non-contaminated reference site (Scorton Creek). With funds from an administrative supplement to the Boston University Superfund Research Program (BUSRP), the BMMC also developed a totally novel, fast, and economical high-throughput experimental and computational platform for predicting which of the 85,000 chemicals registered by the EPA may cause cancer. Results of this study are now on-line and available to the general public. Finally, in collaboration with the Environmental PPARγ Pathway Activators: Multifaceted Metabolic Disruptors Impacting Adipose and Bone Homeostasis Project, the BMMC developed a similar high throughput/computational approach to predict environmental obesogens, i.e., chemicals that aberrantly stimulate fat formation. This latter development demonstrates how the BMMC adapts to serve specific high throughput and computational needs of BUSRP projects.

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