Design and Analysis of Massively Parallel Reporter Assays Using FORECAST

Pierre-Aurelien M A Gilliot; Thomas E Gorochowski

doi:10.1007/978-1-0716-2617-7_3

Design and Analysis of Massively Parallel Reporter Assays Using FORECAST

Pierre-Aurelien M A Gilliot, Thomas E Gorochowski^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

3 Citations (Scopus)

Original language	English
Title of host publication	Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology
Pages	41-56
Number of pages	16
Volume	2553
DOIs	https://doi.org/10.1007/978-1-0716-2617-7_3
Publication status	Published - 2023

Publication series

Name	Methods in Molecular Biology
Volume	2553
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Bibliographical note

Funding Information:
This work was supported by BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre grant BB/L01386X/1 (T.E.G.), a EPSRC/ BBSRC Centre for Doctoral Training in Synthetic Biology grant EP/L016494/1 (P.-A.G.), a Turing Fellowship from The Alan Turing Institute under the EPSRC grant EP/N510129/1 (T.E.G.), and a Royal Society University Research Fellowship grant UF160357 (T.E.G.).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Research Groups and Themes

Bristol BioDesign Institute

Access to Document

10.1007/978-1-0716-2617-7_3

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Cite this

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author = "Gilliot, {Pierre-Aurelien M A} and Gorochowski, {Thomas E}",

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doi = "10.1007/978-1-0716-2617-7_3",

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Design and Analysis of Massively Parallel Reporter Assays Using FORECAST. / Gilliot, Pierre-Aurelien M A; Gorochowski, Thomas E.
Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology. Vol. 2553 2023. p. 41-56 (Methods in Molecular Biology; Vol. 2553).

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

TY - CHAP

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AU - Gilliot, Pierre-Aurelien M A

AU - Gorochowski, Thomas E

N1 - Funding Information: This work was supported by BrisSynBio, a BBSRC/EPSRC Synthetic Biology Research Centre grant BB/L01386X/1 (T.E.G.), a EPSRC/ BBSRC Centre for Doctoral Training in Synthetic Biology grant EP/L016494/1 (P.-A.G.), a Turing Fellowship from The Alan Turing Institute under the EPSRC grant EP/N510129/1 (T.E.G.), and a Royal Society University Research Fellowship grant UF160357 (T.E.G.). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2023

Y1 - 2023

U2 - 10.1007/978-1-0716-2617-7_3

DO - 10.1007/978-1-0716-2617-7_3

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C2 - 36227538

VL - 2553

T3 - Methods in Molecular Biology

SP - 41

EP - 56

BT - Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology

ER -