Modal reduction of mathematical models of biological molecules

  • Authors:

  • Aiqin Li;Earl H. Dowell

  • Affiliations:

  • Department of Mechanical Engineering and Materials Science, Duke University, 144 Hudson Hall, P.O. Box 90302, Durham, NC 27708-0300, United States;Department of Mechanical Engineering and Materials Science, Duke University, 144 Hudson Hall, P.O. Box 90302, Durham, NC 27708-0300, United States

  • Venue:
  • Journal of Computational Physics
  • Year:
  • 2006

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Abstract

This paper reports a detailed study of modal reduction based on either linear normal mode (LNM) analysis or proper orthogonal decomposition (POD) for modeling a single @a-d-glucopyranose monomer as well as a chain of monomers attached to a moving atomic force microscope (AFM) under harmonic excitations. Also a modal reduction method combining POD and component modal synthesis is developed. The accuracy and efficiency of these methods are reported. The focus of this study is to determine to what extent these methods can reduce the time and cost of molecular modeling and simultaneously provide the required accuracy. It has been demonstrated that a linear reduced order model is valid for small amplitude excitation and low frequency excitation. It is found that a nonlinear reduced order model based on POD modes provides a good approximation even for large excitation while the nonlinear reduced order model using linear eigenmodes as the basis vectors is less effective for modeling molecules with a strong nonlinearity. The reduced order model based on component modal synthesis using POD modes for each component also gives a good approximation. With the reduction in the dimension of the system using these methods the computational time and cost can be reduced significantly.