### 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 |

### Fingerprint

### 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

*Progress in Colloid and Polymer Science*,

*131*, 33-40. https://doi.org/10.1007/2882_004

**Genetic algorithm optimization for obtaining accurate molecular weight distributions from sedimentation velocity experiments.** / Brookes, Emre H; Demeler, Borries.

Research output: Contribution to journal › Article

*Progress in Colloid and Polymer Science*, vol. 131, pp. 33-40. https://doi.org/10.1007/2882_004

}

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

UR - http://www.scopus.com/inward/record.url?scp=33749430638&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33749430638&partnerID=8YFLogxK

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 -