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)


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 



genetic algorithm


residual mean square deviation


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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