Genetic algorithm optimization for obtaining accurate molecular weight distributions from sedimentation velocity experiments

Emre Brookes, Borries Demeler

Research output: Contribution to journalArticle

35 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)33-40
Number of pages8
JournalProgress in Colloid and Polymer Science
StatePublished - Dec 1 2006



  • Analytical ultracentrifugation
  • Genetic algorithms
  • Sedimentation velocity analysis

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Polymers and Plastics
  • Colloid and Surface Chemistry
  • Materials Chemistry

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