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Project Information

TRIPODS+X:RES: COLLABORATIVE RESEARCH: IDENTIFICATION OF GENE REGULATORY NETWORK FUNCTION FROM DATA

Agency:
NSF

National Science Foundation
Project Number:
1839294
Contact PI / Project Leader:
MISCHAIKOW, KONSTANTIN M
Awardee Organization:
RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK

Description

Abstract Text:
Molecular and cellular biology are at the forefront of our quest to understand life and improve human health. However, the details of how genes and signals interact in real time to produce cell behavior, are not well understood. The problem is a lack of approaches that can integrate experimental data into models that describe the important behavior of the system, yet do not describe nonessential details, that would make it too cumbersome to compute in a reasonable time. This project will develop a new set of tools, that can predict the entire range of behaviors of a network of interdependent genes and do it so efficiently that behavior under the effect of a few mutations or behavior of alternative sets of genes can be readily explored. Furthermore, once such a description of behavior of a set of genes is available, it will be shared and used by other scientists. Continuous build-up of these results will rapidly increase their value for data science, cellular biology, and broader science. Molecular and cellular biology have witnessed a huge leap forward since science has acquired the ability to sequence genomes. However, while genomes are relatively static, the phenotypes result from the interaction of many genes that are expressed dynamically in time. Furthermore, since phenotypes arise from complex transcriptional networks, where the interactions tend to be nonlinear, data-based models will play a key role in understanding and ultimately controlling these phenotypes. Current modeling techniques, motivated by physics, struggle to bridge a fundamental conflict between low resolution of biological measurements informing model parameter values and the fundamental fact that dynamical systems are sensitive to initial conditions and parameters. This project develops a novel mathematical framework that provides a quantitative description of global dynamics that are compatible with coarse, noisy biological measurements. Combined with computationally efficient algorithms for the construction of databases that capture biologically relevant dynamics of a network, it can become the basis for shared science in the space of gene networks. These tools will be used to construct and validate network models from experimental data, interrogate dynamic behavior of a given network, and compare dynamics summaries across networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Project Terms:
Algorithms; Award; base; Behavior; Biological; cell behavior; Cellular biology; Complex; Conflict (Psychology); Data; Data Science; Databases; dynamic system; Evaluation; Experimental Models; Foundations; Genes; Genetic Transcription; Genome; Health; Human; improved; Life; Mathematics; Measurement; Mission; Modeling; Molecular and Cellular Biology; Mutation; Names; network models; novel; Phenotype; Physics; Play; Regulator Genes; Research; Resolution; Science; Scientist; Signal Transduction; System; Techniques; Time; tool

Details

Contact PI / Project Leader Information:
Name:  MISCHAIKOW, KONSTANTIN M
Other PI Information:
Not Applicable
Awardee Organization:
Name:  RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK
City:  PISCATAWAY    
Country:  UNITED STATES
Congressional District:
State Code:  NJ
District:  06
Other Information:
Fiscal Year: 2018
Award Notice Date: 10-Sep-2018
DUNS Number: 001912864
Project Start Date: 01-Oct-2018
Budget Start Date:
CFDA Code: 47.049
Project End Date: 30-Sep-2021
Budget End Date:
Agency: ?

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National Science Foundation
Project Funding Information for 2018:
Year Agency

Agency: The entity responsible for the administering of a research grant, project, or contract. This may represent a federal department, agency, or sub-agency (institute or center). Details on agencies in Federal RePORTER can be found in the FAQ page.

 FY Total Cost
2018 NSF

National Science Foundation

 		$190,000

Results

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