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Monte Carlo analysis of sedimentation experiments

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Abstract

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.

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Acknowledgments

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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Correspondence to Borries Demeler.

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Demeler, B., Brookes, E. Monte Carlo analysis of sedimentation experiments. Colloid Polym Sci 286, 129–137 (2008). https://doi.org/10.1007/s00396-007-1699-4

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  • DOI: https://doi.org/10.1007/s00396-007-1699-4

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