Related Experiment Video
Updated: Feb 7, 2026

Assembly, Loading, and Alignment of an Analytical Ultracentrifuge Sample Cell
Published on: November 5, 2009
Prediction and analysis of analytical ultracentrifugation experiments for heterogeneous macromolecules and
J García de la Torre1, J G Hernández Cifre2, A I Díez Peña2
1Department of Physical Chemistry, University of Murcia, 30071, Murcia, Spain. jgt@um.es.
Abstract:
In the prediction of sedimentation profiles in analytical ultracentrifugation, the counterflow due to diffusion must be taken into account for a proper analysis of experimental data in the determination of molecular properties. This is usually achieved by numerical solution of the Lamm equation. This paper presents an alternative approach, in which the displacement of the solute in the cell, resulting from the opposite effects of ultracentrifugal force and diffusional drift, is described by Brownian dynamics simulation of the solute particles. The formalism is developed for heterogeneous solutes, composed of several species, and implemented in computational schemes and tools. The accuracy of the procedure is verified by comparison with other methods based on the Lamm equation, and its efficiency is illustrated. The possibilities offered by the Brownian dynamics methods in the determination of solute properties and sample composition are demonstrated.
Related Concept Videos
ATP and Macromolecule Synthesis
Most macromolecules are composed of single subunits, or building blocks, called monomers. The monomers combine with each other using covalent bonds to form larger molecules known as polymers.
Conversion of...
Dynamic Equilibrium
Predicting Molecular Geometry
Development of Analytical Methods
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Analyte Adsorption and Distribution

