(132f) A Comprehensive Brownian Dynamics Based Forward Model for Analytical (Ultra) Centrifugation

Wawra, S. E. - Presenter, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Thajudeen, T., Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Walter, J., Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Peukert, W., University of Erlangen-Nuremberg
Analytical ultracentrifugation (AUC) is considered to be the gold standard for particle characterization in colloidal systems. Together with Analytical centrifugation (AC) techniques, it covers a wide particle size range from sub 1 nanometer to a few microns. Considerable advances have been made over the last decade in the characterization of biomolecules as well as nanoparticles using A(U)C. SEDFIT and Ultrascan have proved to be very reliable and robust in calculating the sedimentation coefficient distributions from centrifugation data [1]. It is possible to retrieve information with and without the effect of particle diffusion in the colloidal systems. Recently, we had extended direct boundary modelling for analyzing the data obtained from Lumisizer [2]. A Brownian dynamics (BD) based tool, originally developed by Diez et al., was improved upon to study the effect of neglecting diffusion while extracting information from the centrifugation data [3].

Currently, there is appreciable interest in using A(U)C techniques for characterizing non-spherical nanoparticles as well as retrieving the optical properties of nanoparticles [4]. Equally interesting is the use of ramp speed data to analyze multimodal distributions and size distributions with larger standard deviations. Development of new tools for sedimentation coefficient analysis (and thereby the size analysis) requires robust forward models that can generate numerical solutions to the Lamm’s equation. BD model represents a viable option for this. The model is based on the motion of particles in a centrifugal field and the change in concentration of the particles can be monitored as a function of time and radial space.

The model can be extended to account for different cell geometries, size distribution of particles as well as multimodal particle size distributions. A major advantage is in generating solution of Lamm equation for ramp speed data, where the rotor speed varies as a function of time. Overall, BD simulations function as an alternative tool to numerical Lamm equation solvers and in certain cases performs as the only viable forward model. The features of ramp speed (along with initial acceleration phase) and multimodal size distributions have also been incorporated. The forward model is also equipped to handle light scattering based on Mie’s theory [5, 6].

It is well known that the sedimentation of macromolecules and particles in the AUC correlates with concentration effects [7]. Hence, transport properties of such analytes are always related to their spatial distribution. As interparticulate interactions alter the sedimentation behavior, it is necessary to consider non-ideality phenomena behavior when analyzing AUC or AC data of concentrated systems [8]. As nanoparticulate systems are rarely monodisperse in size, available software tools for analyzing A(U)C data reach their limits when considering non-ideality. BD is a promising approach towards theoretical description of microscopic inter-particle interactions, since its algorithm allows tracking the trajectory of individual particles. Concentration dependency of the sedimentation and diffusion coefficient has been implemented and tested in our latest BD model. For monodisperse systems, we it can be shown that profiles obtained by BD are in excellent agreement with well-established Lamm equation solvers. Moreover, simulations for polydisperse systems including non-ideal concentration effects are currently being incorporated.


[1] http://www.analyticalultracentrifugation.com/default.htm,


[2] J. Walter, T. Thajudeen, S. Süß, et al., Nanoscale, 2015, 7 (15), 6574-6587.

[3] A. I. Diez, A. Ortega, G. de la Torre, BMC Biophys. 2011, 4, 6.

[4] J. Walter, P. J. Sherwood, W. Lin, et al., Anal. Chem. 2015, 87 (6), 3396-3403.

[5] J. Walter, K. Löhr, E. Karabudak, et al., ACS Nano 2014, 8 (9), 8871-8886.

[6] T. Thajudeen, J. Walter, M. Uttinger et al., Part. & Part. Sys. Charact., 2016,34, 1600229.

[7] S. E. Harding, P. Johnson, Biochem. J. 1985, 231, 543–547.

[8] A. Solovyova; P. Schuck; L Costenaro; C. Ebel, Biophys., J. 2001, 81, 1868-1880.