Award Abstract # 1160352
FRG: Collaborative Research: Variational multiscale approaches to biomolecular structure, dynamics and transport

NSF Org: DMS
Division Of Mathematical Sciences
Recipient: MICHIGAN STATE UNIVERSITY
Initial Amendment Date: September 4, 2012
Latest Amendment Date: February 20, 2014
Award Number: 1160352
Award Instrument: Standard Grant
Program Manager: Mary Ann Horn
DMS
 Division Of Mathematical Sciences
MPS
 Directorate for Mathematical and Physical Sciences
Start Date: September 15, 2012
End Date: August 31, 2015 (Estimated)
Total Intended Award Amount: $319,477.00
Total Awarded Amount to Date: $319,477.00
Funds Obligated to Date: FY 2012 = $319,477.00
History of Investigator:
  • Guowei Wei (Principal Investigator)
    wei@math.msu.edu
  • Kelin Xia (Co-Principal Investigator)
Recipient Sponsored Research Office: Michigan State University
426 AUDITORIUM RD RM 2
EAST LANSING
MI  US  48824-2600
(517)355-5040
Sponsor Congressional District: 07
Primary Place of Performance: Michigan State University
301 ADMINISTRATION BUILDING
East Lansing
MI  US  48824-1110
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): R28EKN92ZTZ9
Parent UEI: VJKZC4D1JN36
NSF Program(s): COMPUTATIONAL MATHEMATICS,
MATHEMATICAL BIOLOGY
Primary Program Source: 01001213DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1616, 9263
Program Element Code(s): 127100, 733400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

A major feature of biological science in the 21st Century will be its transition from a phenomenological and descriptive discipline to a quantitative and predictive one. Revolutionary opportunities have emerged for mathematically driven advances in biological research. Experimental exploration of self-organizing biomolecular systems, such as HIV viruses, molecular motors and proteins in Alzheimer's disease, has been a dominating driven force in scientific discovery and innovation in the past few decades. However, the emergence of complexity in self-organizing biological systems poses fundamental challenges to their quantitative description because of the excessively high dimensionality. This Focused Research Group (FRG) will provide a platform, led by leading researchers from Michigan State University, University of Wisconsin-Madison and Pennsylvania State University, who will synergistically merge their expertise in theoretical modeling, scientific computing and mathematical analysis, for quantitative descriptions of biomolecular systems. The research addresses grand challenges in the structure, function and dynamics of self-organizing biomolecular systems due to exceptionally massive data sets. These challenges are tackled through the introduction of new variational multiscale models, which reduces the dimensionality and number of degrees of freedom by a macroscopic continuum description of the aquatic/membrane environment, and a microscopic discrete description of biomolecules. Additionally, to further reduce the dimensionality of excessively large biomolecular systems, the investigators introduce a coarse-grained approach based on the density cluster dynamics which extracts stable manifolds in molecular dynamics simulations. This FRG project offers innovative new approaches to the massive data management, dimensionality reduction, computer simulation, theoretical modeling and mathematical analysis of biomolecular systems.

This project is a timely effort to promote the quantitative transition of biological science, which will lead to emerging new fields in both mathematical and biological sciences. In particular, the proposed effort will significantly strengthen the leading role that the U.S. researchers can play in mathematical molecular biosciences by aggressively pursuing cutting-edge research and collaboratively training a new generation of mathematicians in this emerging interdisciplinary field. Three annual workshops and international meeting will be held in Michigan State (Year 1), Wisconsin (Year 2) and Penn State (Year 3) to strengthen the collaboration and extend the societal impact.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 14)
Alexov, Emil; Wei, Guo-Wei "Special Issue on Modeling Electrostatics in Molecular Biology (MEMB) Preface" COMMUNICATIONS IN COMPUTATIONAL PHYSICS , v.13 , 2013 , p.1-5
Chen, Zhan; Zhao, Shan; Chun, Jaehun; Thomas, Dennis G.; Baker, Nathan A.; Bates, Peter W.; Wei, G. W. "Variational approach for nonpolar solvation analysis" JOURNAL OF CHEMICAL PHYSICS , v.137 , 2012 , p.1-10
Dennis G. Thomas, Jaehun Chun, Zhan Chen, Guo-Wei Wei, Nathan A. Baker "Parameterization of a geometric flow implicit solvation model" Journal of Computational Chemistry , v.24 , 2013 , p.687-695
Duan Chen and Guo-Wei Wei "Quantum dynamics in continuum models for proton transport I: Basic formulation" Communication in Computational Physics , v.13 , 2013 , p.285-324
Feng, Xin; Xia, Kelin; Tong, Yiying; Wei, Guo-Wei "Geometric modeling of subcellular structures, organelles, and multiprotein complexes" INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING , v.28 , 2012 , p.1198-1223
Guo-Wei Wei "Multiscale multiphysics and multidomain models, Journal of Theoretical and Computational Chemistry" Journal of Theoretical and Computational Chemistry , v.12 , 2013 , p.1341006
Hu, Langhua; Wei, Guo-Wei "Nonlinear Poisson Equation for Heterogeneous Media" BIOPHYSICAL JOURNAL , v.103 , 2012 , p.758-766
Jinkyoung Park and G.W. Wei "A molecular level prototype for mechanoelectrical transducers in mammalian hair cells" Journal of Computational Neuroscience , v.135 , 2013 , p.1-11
Kelin Xia, Kristopher Opron and Guo-Wei Wei "Multiscale multiphysics and multidomain models ? Flexibility and rigidity" Journal of Chemical Physics , v.139 , 2013 , p.194109
Langhua Hu, Duan Chen and Guo-Wei Wei "High-order fractional partial differential equations for molecular surface construction" Molecular Based Mathematical Biology , v.1 , 2012 , p.1-25
L. Mu, J. P. Wang, G. W. Wei, X. Ye and S. Zhao "Weak Galerkin methods for second order elliptic interface problems" Journal of Computational Physics , v.250 , 2013 , p.106-125
(Showing: 1 - 10 of 14)

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