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Data-driven network models for genetic circuits from time-series data with incomplete measurements

Yeung, Enoch and Kim, Jongmin and Yuan, Ye and Gonçalves, Jorge and Murray, Richard M. (2021) Data-driven network models for genetic circuits from time-series data with incomplete measurements. Journal of the Royal Society Interface, 18 (182). Art. No. 20210413. ISSN 1742-5689. doi:10.1098/rsif.2021.0413. https://resolver.caltech.edu/CaltechAUTHORS:20210311-095327802

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Abstract

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1098/rsif.2021.0413DOIArticle
https://doi.org/10.1101/2021.03.10.434835DOIDiscussion Paper
https://github.com/YeungRepo/NetworkReconRelated ItemData
ORCID:
AuthorORCID
Yeung, Enoch0000-0001-7630-7429
Kim, Jongmin0000-0002-2713-1006
Yuan, Ye0000-0001-8669-7950
Gonçalves, Jorge0000-0002-5228-6165
Murray, Richard M.0000-0002-5785-7481
Alternate Title:Discovery of Dynamical Network Models for Genetic Circuits from Time-Series Data with Incomplete Measurements
Additional Information:© 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. Manuscript received 18/05/2021; Manuscript accepted 12/08/2021; Published online 08/09/2021. We would like to acknowledge Sean Warnick, Shara Balakrishnan, Vipul Singhal and Anandh Swaminanthan for insightful conversations on network reconstruction algorithms. We would like to especially thank Shara Balakrishnan for editorial comments through the writing process. We would like to thank and acknowledge Zachary Sun, Victoria Hsiao, Ophelia Venturelli, Clarmyra Hayes, Emmanuel de los Santos and Joe Meyerowitz for guidance with experimental techniques. This work was supported by the Engineering and Physical Sciences Research Council, the Luxembourg National Research Foundation, Air Force Office of Scientific Research, grant no. FA9550-14-1-0060, the Defense Advanced Research Projects Agency, grant nos. HR0011-12-C-0065 and FA8750-19-2-0502, the Army Research Office Young Investigator Program, grant no. W911NF-20-1-0165, the National Science Foundation, grant no. 1317291, and the John and Ursula Kanel Charitable Foundation. Data accessibility: All data files, network reconstruction code and visualization scripts can be obtained from the GitHub repository https://github.com/YeungRepo/NetworkRecon. Authors' contributions: E.Y. wrote the paper. E.Y, J.G., Y.Y., J.K. and R.M.M. edited drafts of the paper. E.Y. and J.K. designed and carried out experiments and processed experimental data. E.Y. performed analysis and modelling. J.G. and R.M.M. secured research funding. R.M.M. supervised the research process. We declare we have no competing interests.
Funders:
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)UNSPECIFIED
Luxembourg National Research FoundationUNSPECIFIED
Air Force Office of Scientific Research (AFOSR)FA9550-14-1-0060
Defense Advanced Research Projects Agency (DARPA)HR0011-12-C-0065
Defense Advanced Research Projects Agency (DARPA)FA8750-19-2-0502
Army Research Office (ARO)W911NF-20-1-0165
NSFCCF-1317291
John and Ursula Kanel Charitable FoundationUNSPECIFIED
Subject Keywords:genetic networks, network reconstruction, genetic circuits, data-driven modelling, dynamical structure functions, dynamic networks
Issue or Number:182
DOI:10.1098/rsif.2021.0413
Record Number:CaltechAUTHORS:20210311-095327802
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210311-095327802
Official Citation:Yeung E, Kim J, Yuan Y, Gonçalves J, Murray RM. 2021 Data-driven network models for genetic circuits from timeseries data with incomplete measurements. J. R. Soc. Interface 18: 20210413. https://doi.org/10.1098/rsif.2021.0413
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108395
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:12 Mar 2021 18:06
Last Modified:15 Sep 2021 22:16

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