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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF MASSACHUSETTS
Doing Business As Name:University of Massachusetts, Dartmouth
PD/PI:
  • Vanni Bucci
  • (508) 999-8219
  • vbucci@umassd.edu
Award Date:03/23/2015
Estimated Total Award Amount: $ 491,682
Funds Obligated to Date: $ 491,682
  • FY 2015=$491,682
Start Date:09/01/2015
End Date:08/31/2018
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.074
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:ABI Innovation: A new computational framework for the prediction of microbiome dynamics
Federal Award ID Number:1458347
DUNS ID:799477427
Parent DUNS ID:079520631
Program:ADVANCES IN BIO INFORMATICS
Program Officer:
  • Jennifer Weller
  • (703) 292-8470
  • jweller@nsf.gov

Awardee Location

Street:285 Old Westport Road
City:North Dartmouth
State:MA
ZIP:02747-2300
County:North Dartmouth
Country:US
Awardee Cong. District:09

Primary Place of Performance

Organization Name:University of Massachusetts, Dartmouth
Street:
City:
State:MA
ZIP:02747-2300
County:North Dartmouth
Country:US
Cong. District:09

Abstract at Time of Award

The dynamics of microbial communities play a fundamental role in the functioning of many natural, engineered and host-associated systems. Even though the application of DNA sequencing technologies has allowed profiling the response of these communities to external perturbations, the important knowledge resulting from this approach stems from descriptive and correlation-based analysis of these data. This strongly limits the understanding of the ecology (e.g. how the microbes interact) of these systems and, more importantly, hinders the ability to make quantitative predictions. This project will deliver new theoretical methods and related computational algorithms that, for the first time, allow forecasting microbiome dynamics that are typically constrained with sequencing surveys. This will benefit researchers working on host-associated and environmental microbiomes as it will enable them to computationally explore scenarios that are difficult to set-up experimentally. The tools developed in this project will be delivered as an open-source, freely downloadable and upgradable package, and will encourage and enable end-user contributions with the novel scripts and algorithms provided. Multiple graduate and undergraduate students will be included in the project and will benefit from interdisciplinary hands-on training in mathematical and computational biology, statistics, and microbial genetics. As the proposed methods combine concept from multi-linear regression and solution of large systems of differential equations, they will perfectly integrate with coursework in Biostatistics and Theoretical Biology at UMass Dartmouth. This research will deliver the first computational suite that allows for simulating and predicting microbiome dynamics consistent with metagenomics observations. This will be achieved by: 1) the development of new time-reverse engineering inference methods solved by a combination of regularized regression and quadratic programming for the estimation of an optimal set of model parameters, and 2) the application of computational tools for metagenome reconstruction based on modeling predictions. This research will also include the development of explicit and implicit numerical methods for the solution of large systems of differential equations to predict microbiome transient dynamics and the use of linear stability analysis to determine all possible microbiome predicted configuration states in response to different sets of perturbations. Method testing and validation against data from simple in silico and in vitro microbial ecosystem of known ecological structure will allow accuracy testing of the proposed approaches both for predicting temporal dynamics and in recovering the correct microbial interaction network in response to external perturbation. Method application (and validation) on diverse datasets will allow testing of fundamental hypotheses about the role of the intestinal microbiome in resisting colonization by foreign bacteria and in shaping host immunity. More information about this project can be found at: http://www.vannibucci.org/research-interests.html

Publications Produced as a Result of this Research

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Vanni Bucci, Belinda Tzen, Ning Li, Matt Simmons, Takeshi Tanoue, Elijah Bogart, Luxue Deng, Vladimir Yeliseyev, Mary L. Delaney, Qing Liu, Bernat Olle, Richard R. Stein, Kenya Honda, Lynn Bry, and Georg K. Gerber "MDSINE: Microbial Dynamical SystemsmINference Engine for microbiome time-series analyses" Genome Biology, v.17, 2016, p.. doi:https://doi.org/10.1186/s13059-016-0980-6 

John P Haran, Gregory Wu, Vanni Bucci, Andrew Fischer, Edward W Boyer, Patricia L Hibberd "Treatment of bacterial skin infections in ED observation units: factors influencing prescribing practice" The American journal of emergency medicine, v.33, 2016, p.. doi:https://doi.org/10.1016/j.ajem.2015.08.035 

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