Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts

Wolfgang Halter, Frank Allgöwer, Richard M. Murray, Andras Gyorgy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The fact that genes compete for shared cellular resources poses a fundamental challenge when identifying parameters of genetic parts. A recently developed model of gene expression tackles this problem by explicitly accounting for resource competition. In addition to accurately describing experimental data, this model only depends on a small number of easily identifiable parameters with clear physical interpretation. Based on this model, we outline a procedure to select the optimal set of experiments to characterize biomolecular parts in synthetic biology. Additionally, we reveal the role competition for shared resources plays, provide guidelines how to minimize its detrimental effects, and how to leverage this phenomenon to extract the most information about unknown parameters. To illustrate the results, we consider the case of part characterization in cell-free extracts, treat plasmid DNA concentrations as decision variables, and demonstrate the significant performance difference between naive and optimal experiment design.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1872-1879
Number of pages8
ISBN (Electronic)9781538613955
DOIs
StatePublished - Jan 18 2019
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December
ISSN (Print)0743-1546

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
CountryUnited States
CityMiami
Period12/17/1812/19/18

Fingerprint

Resources
Cell
Synthetic Biology
Experiment
Experiments
Gene expression
Leverage
Unknown Parameters
Gene Expression
DNA
Genes
Experimental Data
Model
Gene
Minimise
Demonstrate
Design
Interpretation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Halter, W., Allgöwer, F., Murray, R. M., & Gyorgy, A. (2019). Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 1872-1879). [8619039] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619039

Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts. / Halter, Wolfgang; Allgöwer, Frank; Murray, Richard M.; Gyorgy, Andras.

2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1872-1879 8619039 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Halter, W, Allgöwer, F, Murray, RM & Gyorgy, A 2019, Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619039, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 1872-1879, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8619039
Halter W, Allgöwer F, Murray RM, Gyorgy A. Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1872-1879. 8619039. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2018.8619039
Halter, Wolfgang ; Allgöwer, Frank ; Murray, Richard M. ; Gyorgy, Andras. / Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts. 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1872-1879 (Proceedings of the IEEE Conference on Decision and Control).
@inproceedings{af94e4d32a3a4134acd7233adf3428dd,
title = "Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts",
abstract = "The fact that genes compete for shared cellular resources poses a fundamental challenge when identifying parameters of genetic parts. A recently developed model of gene expression tackles this problem by explicitly accounting for resource competition. In addition to accurately describing experimental data, this model only depends on a small number of easily identifiable parameters with clear physical interpretation. Based on this model, we outline a procedure to select the optimal set of experiments to characterize biomolecular parts in synthetic biology. Additionally, we reveal the role competition for shared resources plays, provide guidelines how to minimize its detrimental effects, and how to leverage this phenomenon to extract the most information about unknown parameters. To illustrate the results, we consider the case of part characterization in cell-free extracts, treat plasmid DNA concentrations as decision variables, and demonstrate the significant performance difference between naive and optimal experiment design.",
author = "Wolfgang Halter and Frank Allg{\"o}wer and Murray, {Richard M.} and Andras Gyorgy",
year = "2019",
month = "1",
day = "18",
doi = "10.1109/CDC.2018.8619039",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1872--1879",
booktitle = "2018 IEEE Conference on Decision and Control, CDC 2018",

}

TY - GEN

T1 - Optimal Experiment Design and Leveraging Competition for Shared Resources in Cell-Free Extracts

AU - Halter, Wolfgang

AU - Allgöwer, Frank

AU - Murray, Richard M.

AU - Gyorgy, Andras

PY - 2019/1/18

Y1 - 2019/1/18

N2 - The fact that genes compete for shared cellular resources poses a fundamental challenge when identifying parameters of genetic parts. A recently developed model of gene expression tackles this problem by explicitly accounting for resource competition. In addition to accurately describing experimental data, this model only depends on a small number of easily identifiable parameters with clear physical interpretation. Based on this model, we outline a procedure to select the optimal set of experiments to characterize biomolecular parts in synthetic biology. Additionally, we reveal the role competition for shared resources plays, provide guidelines how to minimize its detrimental effects, and how to leverage this phenomenon to extract the most information about unknown parameters. To illustrate the results, we consider the case of part characterization in cell-free extracts, treat plasmid DNA concentrations as decision variables, and demonstrate the significant performance difference between naive and optimal experiment design.

AB - The fact that genes compete for shared cellular resources poses a fundamental challenge when identifying parameters of genetic parts. A recently developed model of gene expression tackles this problem by explicitly accounting for resource competition. In addition to accurately describing experimental data, this model only depends on a small number of easily identifiable parameters with clear physical interpretation. Based on this model, we outline a procedure to select the optimal set of experiments to characterize biomolecular parts in synthetic biology. Additionally, we reveal the role competition for shared resources plays, provide guidelines how to minimize its detrimental effects, and how to leverage this phenomenon to extract the most information about unknown parameters. To illustrate the results, we consider the case of part characterization in cell-free extracts, treat plasmid DNA concentrations as decision variables, and demonstrate the significant performance difference between naive and optimal experiment design.

UR - http://www.scopus.com/inward/record.url?scp=85062175082&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062175082&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8619039

DO - 10.1109/CDC.2018.8619039

M3 - Conference contribution

AN - SCOPUS:85062175082

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 1872

EP - 1879

BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -