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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 33-40 |
| Number of pages | 8 |
| Journal | Progress in Colloid and Polymer Science |
| Volume | 131 |
| DOIs | |
| State | Published - 2006 |
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Keywords
- Analytical ultracentrifugation
- Genetic algorithms
- Sedimentation velocity analysis
ASJC Scopus subject areas
- Physical and Theoretical Chemistry
- Chemistry (miscellaneous)
- Colloid and Surface Chemistry
- Polymers and Plastics
Cite this
Genetic algorithm optimization for obtaining accurate molecular weight distributions from sedimentation velocity experiments. / Brookes, Emre H; Demeler, Borries.
In: Progress in Colloid and Polymer Science, Vol. 131, 2006, p. 33-40.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Genetic algorithm optimization for obtaining accurate molecular weight distributions from sedimentation velocity experiments
AU - Brookes, Emre H
AU - Demeler, Borries
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Analytical ultracentrifugation
KW - Genetic algorithms
KW - Sedimentation velocity analysis
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U2 - 10.1007/2882_004
DO - 10.1007/2882_004
M3 - Article
VL - 131
SP - 33
EP - 40
JO - Progress in Colloid and Polymer Science
JF - Progress in Colloid and Polymer Science
SN - 0340-255X
ER -