Abstract: | |
Since the mechanism governing the partitioning behavior of biomolecules, such as proteins and enzymes, in polyethylene glycol (PEG)-salt aqueous two-phase systems (ATPS) is complex and not easily predictable, many laborious experiments have to be performed for an optimization of these systems, causing increased overall cost. However, the multivariate statistical design of experiments (DoE) methodology is representing a promising and efficient optimization technique which can overcome the limitations of traditional optimization methods. Therefore, DoE has emerged as a powerful and efficient optimization tool for PEG-salt ATPS, since it is faster, more efficient and cost-effective, allowing a simultaneous and rigorous evaluation of process/system parameters. In the present review, different DoE process steps are represented to highlight the feasibility of this approach to operate as a promising and efficient optimization tool, thus facilitating the evaluation of the partitioning behavior, recovery and purification of different proteins and enzymes in PEG-salt ATPS. In this context, several experimental designs, such as factorial and response surface designs, have been discussed and evaluated by statistical regression analysis and analysis of variance (ANOVA), as well as various applications of PEG-salt ATPS using DoE have been outlined which may further promote the optimization of these systems.
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License of this version: | CC BY-NC-ND 4.0 Unported - https://creativecommons.org/licenses/by-nc-nd/4.0/ |
Publication type: | Article |
Publishing status: | acceptedVersion |
Publication date: | 2015 |
Keywords english: | Analysis of variance, Aqueous two-phase systems, Central composite design, Design of experiments, Factorial experimental design, Response surface methodology, macrogol, sodium chloride, Box Behnken design, central composite circumscribed design, central composite face centered design, cost effectiveness analysis, experimental design, factorial design, feasibility study, fractional factorial design, full factorial design, investigative procedures, methodology, multiple linear regression analysis, prediction, priority journal, process optimization, response surface method, response variable, Review |
DDC: | 500 | Naturwissenschaften |
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