Climbing the mountain: experimental design for the efficient optimization of stem cell bioprocessing
| dc.contributor.author | Toms, Derek | |
| dc.contributor.author | Deardon, Rob | |
| dc.contributor.author | Ungrin, Mark | |
| dc.date.accessioned | 2018-09-26T12:10:38Z | |
| dc.date.available | 2018-09-26T12:10:38Z | |
| dc.date.issued | 2017-12-04 | |
| dc.date.updated | 2018-09-26T12:10:38Z | |
| dc.description.abstract | Abstract “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” – R.A. Fisher While this idea is relevant across research scales, its importance becomes critical when dealing with the inherently large, complex and expensive process of preparing material for cell-based therapies (CBTs). Effective and economically viable CBTs will depend on the establishment of optimized protocols for the production of the necessary cell types. Our ability to do this will depend in turn on the capacity to efficiently search through a multi-dimensional problem space of possible protocols in a timely and cost-effective manner. In this review we discuss approaches to, and illustrate examples of the application of statistical design of experiments to stem cell bioprocess optimization. | |
| dc.identifier.citation | Journal of Biological Engineering. 2017 Dec 04;11(1):35 | |
| dc.identifier.doi | https://doi.org/10.1186/s13036-017-0078-z | |
| dc.identifier.uri | http://hdl.handle.net/1880/108023 | |
| dc.language.rfc3066 | en | |
| dc.rights.holder | The Author(s) | |
| dc.title | Climbing the mountain: experimental design for the efficient optimization of stem cell bioprocessing | |
| dc.type | Journal Article |