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.
- Analytical ultracentrifugation
- Curve fitting
- Genetical gorithms
- Molecular weight determination
- Two-dimensional spectrum analysis