A data-driven method for quantifying the impact of a genetic circuit on its host

Aqib Hasnain; Diveena Becker; Atsede Siba; Narendra Maheshri; Ben Gordon; Chris Voigt; Enoch Yeung; Subhrajit Sinha; Yuval Dorfan; Amin Espah Borujeni; Yongjin Park; Paul Maschhoff; Uma Saxena; Joshua Urrutia; Niall Gaffney

doi:10.1109/BIOCAS.2019.8919140

A data-driven method for quantifying the impact of a genetic circuit on its host

Aqib Hasnain
, Diveena Becker
, Atsede Siba
, Narendra Maheshri
, Ben Gordon
, Chris Voigt
, Enoch Yeung
, Subhrajit Sinha
, Yuval Dorfan
, Amin Espah Borujeni
, Yongjin Park
, Paul Maschhoff
, Uma Saxena
, Joshua Urrutia
, Niall Gaffney

University of California at Santa Barbara
Pacific Northwest National Laboratory
Massachusetts Institute of Technology
University of Texas at Austin
Gingko Bioworks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Genetic circuits aredesigned to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-Throughput RNAseq measurements collected from wild-Type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.

Original language	English
Title of host publication	BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509006175
DOIs	https://doi.org/10.1109/BIOCAS.2019.8919140
State	Published - Oct 2019
Externally published	Yes
Event	2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, Japan Duration: 17 Oct 2019 → 19 Oct 2019

Publication series

Name	BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference	2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019
Country/Territory	Japan
City	Nara
Period	17/10/19 → 19/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

ACKNOWLEDGEMENTS The authors gratefully acknowledge the funding of DARPA grants FA8750-17-C-0229, HR001117C0092, HR001117C0094, DEAC0576RL01830. The authors would also like to thank Professors Igor Mezic, Alexandre Mauroy, Nathan Kutz, Steve Haase, John Harer, and Eric Klavins for insightful discussions. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Defense Advanced Research Project Agency, the Department of Defense, or the United States government. This material is based on work supported by DARPA and AFRL under contract numbers FA8750-17-C-0229, HR001117C0092, HR001117C0094, DEAC0576RL01830.

Funders	Funder number
DARPA and AFRL
Defense Advanced Research Projects Agency	HR001117C0092, HR001117C0094, DEAC0576RL01830, FA8750-17-C-0229

Access to Document

10.1109/BIOCAS.2019.8919140

Cite this

Hasnain, A., Becker, D., Siba, A., Maheshri, N., Gordon, B., Voigt, C., Yeung, E., Sinha, S., Dorfan, Y., Borujeni, A. E., Park, Y., Maschhoff, P., Saxena, U., Urrutia, J., & Gaffney, N. (2019). A data-driven method for quantifying the impact of a genetic circuit on its host. In BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings Article 8919140 (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2019.8919140

@inproceedings{dd243377dc3d4dab9bf6c68201af83f9,

title = "A data-driven method for quantifying the impact of a genetic circuit on its host",

abstract = "Genetic circuits aredesigned to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-Throughput RNAseq measurements collected from wild-Type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.",

author = "Aqib Hasnain and Diveena Becker and Atsede Siba and Narendra Maheshri and Ben Gordon and Chris Voigt and Enoch Yeung and Subhrajit Sinha and Yuval Dorfan and Borujeni, \{Amin Espah\} and Yongjin Park and Paul Maschhoff and Uma Saxena and Joshua Urrutia and Niall Gaffney",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 ; Conference date: 17-10-2019 Through 19-10-2019",

year = "2019",

month = oct,

doi = "10.1109/BIOCAS.2019.8919140",

language = "אנגלית",

series = "BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings",

address = "ארצות הברית",

}

Hasnain, A, Becker, D, Siba, A, Maheshri, N, Gordon, B, Voigt, C, Yeung, E, Sinha, S, Dorfan, Y, Borujeni, AE, Park, Y, Maschhoff, P, Saxena, U, Urrutia, J & Gaffney, N 2019, A data-driven method for quantifying the impact of a genetic circuit on its host. in BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings., 8919140, BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019, Nara, Japan, 17/10/19. https://doi.org/10.1109/BIOCAS.2019.8919140

A data-driven method for quantifying the impact of a genetic circuit on its host. / Hasnain, Aqib; Becker, Diveena; Siba, Atsede et al.
BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8919140 (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A data-driven method for quantifying the impact of a genetic circuit on its host

AU - Hasnain, Aqib

AU - Becker, Diveena

AU - Siba, Atsede

AU - Maheshri, Narendra

AU - Gordon, Ben

AU - Voigt, Chris

AU - Yeung, Enoch

AU - Sinha, Subhrajit

AU - Dorfan, Yuval

AU - Borujeni, Amin Espah

AU - Park, Yongjin

AU - Maschhoff, Paul

AU - Saxena, Uma

AU - Urrutia, Joshua

AU - Gaffney, Niall

PY - 2019/10

Y1 - 2019/10

N2 - Genetic circuits aredesigned to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-Throughput RNAseq measurements collected from wild-Type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.

AB - Genetic circuits aredesigned to implement certain logic in living cells, keeping burden on the host cell minimal. However, manipulating the genome often will have a significant impact for various reasons (usage of the cell machinery to express new genes, toxicity of genes, interactions with native genes, etc.). In this work we utilize Koopman operator theory to construct data-driven models of transcriptomic-level dynamics from noisy and temporally sparse RNAseq measurements. We show how Koopman models can be used to quantify impact on genetic circuits. We consider an experimental example, using high-Throughput RNAseq measurements collected from wild-Type E. coli, single gate components transformed in E. coli, and a NAND circuit composed from individual gates in E. coli, to explore how Koopman subspace functions encode increasing circuit interference on E. coli chassis dynamics. The algorithm provides a novel method for quantifying the impact of synthetic biological circuits on host-chassis dynamics.

UR - https://www.scopus.com/pages/publications/85077045664

U2 - 10.1109/BIOCAS.2019.8919140

DO - 10.1109/BIOCAS.2019.8919140

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85077045664

T3 - BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

BT - BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019

Y2 - 17 October 2019 through 19 October 2019

ER -

Hasnain A, Becker D, Siba A, Maheshri N, Gordon B, Voigt C et al. A data-driven method for quantifying the impact of a genetic circuit on its host. In BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8919140. (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings). doi: 10.1109/BIOCAS.2019.8919140

A data-driven method for quantifying the impact of a genetic circuit on its host

Abstract

Publication series

Conference

Bibliographical note

Funding

Access to Document

Other files and links

Fingerprint

Cite this