Statistical experimental designs for the production of secondary metabolites in plant cell suspension cultures

Statistical experimental designs, also known as the “design of experiments” (DoE) approach, are widely used to improve not only technical processes but also to answer questions in the agricultural, medical and social sciences. Although many articles have been published about the application of DoE i...

Full description

Saved in:
Bibliographic Details
Published in:Biotechnology letters 2016-12, Vol.38 (12), p.2007-2014
Main Authors: Schmitz, Christian, Fritsch, Leonie, Fischer, Rainer, Schillberg, Stefan, Rasche, Stefan
Format: Article
Language:English
Subjects:
Applied Microbiology
Biochemistry
Biomedical and Life Sciences
Biotechnology
Cell Culture Techniques - methods
Cosmetics
Culture
Data Interpretation, Statistical
Design improvements
Design of experiments
Feedstock
Life Sciences
Metabolites
Microbiology
Plant biology
Plant Cells - metabolism
Plants (organisms)
Productivity
Research Design
Review
Secondary metabolites
Social sciences
Statistical analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Statistical experimental designs, also known as the “design of experiments” (DoE) approach, are widely used to improve not only technical processes but also to answer questions in the agricultural, medical and social sciences. Although many articles have been published about the application of DoE in these fields, few studies have addressed the use of DoE in the plant sciences, particularly in the context of plant cell suspension cultures (PCSCs). Compounds derived from PCSCs can be developed as pharmaceuticals, chemical feedstocks and cosmetic ingredients, and statistical experimental designs can be used to improve the productivity of the cells and the yield and/or quality of the target compounds in a cost efficient manner. In this article, we summarize recent findings concerning the application of statistical approaches to improve the performance of PCSCs and discuss the potential future applications of this approach.
ISSN:0141-5492
1573-6776
DOI:10.1007/s10529-016-2206-0