Development of High-Performance Whole Cell Biosensors Aided by Statistical ModelingCitation formats

  • Authors:
  • Adokiye Berepiki
  • Ross Kent
  • Leopoldo F.m. Machado
  • Neil Dixon

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Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling. / Berepiki, Adokiye; Kent, Ross; Machado, Leopoldo F.m.; Dixon, Neil.

In: ACS Synthetic Biology, Vol. 9, No. 3, 20.03.2020, p. 576-589.

Research output: Contribution to journalArticlepeer-review

Harvard

Berepiki, A, Kent, R, Machado, LFM & Dixon, N 2020, 'Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling', ACS Synthetic Biology, vol. 9, no. 3, pp. 576-589. https://doi.org/10.1021/acssynbio.9b00448

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Author

Berepiki, Adokiye ; Kent, Ross ; Machado, Leopoldo F.m. ; Dixon, Neil. / Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling. In: ACS Synthetic Biology. 2020 ; Vol. 9, No. 3. pp. 576-589.

Bibtex

@article{2f3a904d85d04de89a538a3837c4baa0,
title = "Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling",
abstract = "Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.",
keywords = "definitive screening design, design of experiments, ferulic acid, protocatechuic acid, whole cell biosensors",
author = "Adokiye Berepiki and Ross Kent and Machado, {Leopoldo F.m.} and Neil Dixon",
year = "2020",
month = mar,
day = "20",
doi = "10.1021/acssynbio.9b00448",
language = "English",
volume = "9",
pages = "576--589",
journal = "ACS Synthetic Biology",
issn = "2161-5063",
publisher = "American Chemical Society",
number = "3",

}

RIS

TY - JOUR

T1 - Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling

AU - Berepiki, Adokiye

AU - Kent, Ross

AU - Machado, Leopoldo F.m.

AU - Dixon, Neil

PY - 2020/3/20

Y1 - 2020/3/20

N2 - Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

AB - Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.

KW - definitive screening design

KW - design of experiments

KW - ferulic acid

KW - protocatechuic acid

KW - whole cell biosensors

U2 - 10.1021/acssynbio.9b00448

DO - 10.1021/acssynbio.9b00448

M3 - Article

C2 - 32023410

VL - 9

SP - 576

EP - 589

JO - ACS Synthetic Biology

JF - ACS Synthetic Biology

SN - 2161-5063

IS - 3

ER -