Colloid and Polymer Science

, Volume 286, Issue 2, pp 129–137 | Cite as

Monte Carlo analysis of sedimentation experiments

  • Borries DemelerEmail author
  • Emre Brookes
Original Contribution


High resolution analysis approaches for sedimentation experiments have recently been developed that promise to provide a detailed description of heterogeneous samples by identifying both shape and molecular weight distributions. In this study, we describe the effect experimental noise has on the accuracy and precision of such determinations and offer a stochastic Monte Carlo approach, which reliably quantifies the effect of noise by determining the confidence intervals for the parameters that describe each solute. As a result, we can now predict reliable confidence intervals for determined parameters. We also explore the effect of various experimental parameters on the confidence intervals and provide suggestions for improving the statistics by applying a few practical rules for the design of sedimentation experiments.


Two-dimensional spectrum analysis Genetic algorithms UltraScan Analytical ultracentrifugation Molecular weight determination Curve fitting 



We would like to thank Mr. Jeremy Mann for the assistance with the supercomputing facility at the UTHSCSA Bioinformatics Core Facility. Funding from the National Science Foundation (Grant #DBI-9974819), the National Institutes of Health (Grant 1 R01 RR022200-01A1), and the San Antonio Life Science Institute (SALSI #10001642) is gratefully acknowledged.


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of BiochemistryUniversity of Texas Health Science Center at San AntonioSan AntonioUSA
  2. 2.Department of Computer ScienceUniversity of Texas at San AntonioSan AntonioUSA

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