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Gilman J, Walls L, Bandiera L, et al. Statistical Design of Experiments for Synthetic Biology. ACS Synth Biol. 2021;10(1):1-18doi: 10.1021/acssynbio.0c00385.
Gilman, J., Walls, L., Bandiera, L., & Menolascina, F. (2021). Statistical Design of Experiments for Synthetic Biology. ACS synthetic biology, 10(1), 1-18. https://doi.org/10.1021/acssynbio.0c00385
Gilman, James, et al. "Statistical Design of Experiments for Synthetic Biology." ACS synthetic biology vol. 10,1 (2021): 1-18. doi: https://doi.org/10.1021/acssynbio.0c00385
Gilman J, Walls L, Bandiera L, Menolascina F. Statistical Design of Experiments for Synthetic Biology. ACS Synth Biol. 2021 Jan 15;10(1):1-18. doi: 10.1021/acssynbio.0c00385. Epub 2021 Jan 07. PMID: 33406821.
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ACS Synth Biol. 2021 Jan 15;10(1):1-18. doi: 10.1021/acssynbio.0c00385. Epub 2021 Jan 07.

Statistical Design of Experiments for Synthetic Biology.

ACS synthetic biology

James Gilman, Laura Walls, Lucia Bandiera, Filippo Menolascina

Affiliations

  1. Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K.

PMID: 33406821 DOI: 10.1021/acssynbio.0c00385

Abstract

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Keywords: design of experiments; metabolic engineering; optimal experimental design; process optimization

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