Modeling the crystallization of proteins and small organic molecules in nanoliter drops

Dombrowski, Richard D, Litster, James D, Wagner, Norman J and He, Yinghe (2010) Modeling the crystallization of proteins and small organic molecules in nanoliter drops. AIChE Journal, 56 1: 79-91. doi:10.1002/aic.12001

Author Dombrowski, Richard D
Litster, James D
Wagner, Norman J
He, Yinghe
Title Modeling the crystallization of proteins and small organic molecules in nanoliter drops
Journal name AIChE Journal   Check publisher's open access policy
ISSN 0001-1541
Publication date 2010-01-01
Year available 2009
Sub-type Article (original research)
DOI 10.1002/aic.12001
Volume 56
Issue 1
Start page 79
End page 91
Total pages 13
Editor Stanley I. Sandler
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Language eng
Abstract Drop-based crystallization techniques are used to achieve a high degree of control over crystallization conditions in order to grow high-quality protein crystals for X-ray diffraction or to produce organic crystals with well-controlled size distributions. Simultaneous crystal growth and stochastic nucleation makes it difficult to predict the number and size of crystals that will be produced in a drop-based crystallization process. A mathematical model of crystallization in drops is developed using a Monte Carlo method. The model incorporates key phenomena in drop-based crystallization, including stochastic primary nucleation and growth rate dispersion (GRD) and can predict distributions of the number of crystals per drop and full crystal size distributions (CSD). Key dimensionless parameters are identified to quickly screen for crystallization conditions that are expected to yield a high fraction of drops containing one crystal and a narrow CSD. Using literature correlations for the solubilities, growth, and nucleation rates of lactose and lysozyme, the model is able to predict the experimentally observed crystallization behavior over a wide range of conditions. Model-based strategies for use in the design and optimization of a drop-based crystallization process for producing crystals of well-controlled CSD are identified.
Keyword Crystallization
Crystal growth
Mathematical modeling
Particle technology
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article first published online: 9 September 2009.

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
School of Chemical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 9 times in Scopus Article | Citations
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Created: Sun, 17 Jan 2010, 10:08:11 EST