Publication Date:
2012-06-01
Description:
Since the efficiency and speed of computing has increased significantly in the last decades, in silico-approaches, e.g., quasi-experimental analyses based on mechanistic simulations combined with Monte Carlo (MC) methods, are on the rise for uncertainty analyses and estimation of uncertainty propagation. The power and convenience of these approaches for high-throughput processes will be demonstrated with a case study including miniaturized screenings on robotic platforms: a binding study for lysozyme on the adsorbent SP Sepharose FF in 96-well format. All relevant uncertainties during the experimental preparations and automated high-throughput experimentation were identified, quantified, and then embedded in a simulation algorithm for the calculation of uncertainty propagation based on MC sampling. A proof-of-concept for this approach is then followed by the simulation-based analysis of various case scenarios. The MC-based approach can easily be transferred to uncertainty analyses in other high-throughput processes. In a case study including miniaturized screenings on robotic platforms it could be demonstrated that a simulation algorithm in combination with Monte Carlo sampling is applicable for uncertainty propagation calculation in a high-throughput process. Based on this example, a general strategy for uncertainty analysis in more complex high-throughput experimentation should be developed as a standard.
Print ISSN:
0930-7516
Electronic ISSN:
1521-4125
Topics:
Chemistry and Pharmacology
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Process Engineering, Biotechnology, Nutrition Technology
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