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Genetic Algorithm Optimization for Obtaining Accurate Molecular Weight Distributions from Sedimentation Velocity Experiments

  • Emre BrookesEmail author
  • Borries Demeler
Conference paper
Part of the Progress in Colloid and Polymer Science book series (PROGCOLLOID, volume 131)

Abstract

Sedimentation experiments can provide a large amount of information about the composition of a sample, and the properties of each component contained in the sample. To extract the details of the composition and the component properties, experimental data can be described by a mathematical model, which can then be fitted to the data. If the model is nonlinear in the parameters, the parameter adjustments are typically performed by a nonlinear least squares optimization algorithm. For models with many parameters, the error surface of this optimization often becomes very complex, the parameter solution tends to become trapped in a local minimum and the method may fail to converge. We introduce here a stochastic optimization approach for sedimentation velocity experiments utilizing genetic algorithms which is immune to such convergence traps and allows high-resolution fitting of nonlinear multi-component sedimentation models to yield distributions for sedimentation and diffusion coefficients, molecular weights, and partial concentrations.

Analytical ultracentrifugation Genetic algorithms Sedimentation velocity analysis 

Abbreviations

GA

genetic algorithm

RMSD

residual mean square deviation

NNLS

non-negatively constrained linear least squares

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Authors and Affiliations

  1. 1.The University of Texas at San AntonioDept. of Computer ScienceSan AntonioUSA
  2. 2.The University of Texas Health Science Center at San AntonioDept. of BiochemistrySan AntonioUSA

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