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Discovery of Dynamical 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) Discovery of Dynamical Network Models for Genetic Circuits from Time-Series Data with Incomplete Measurements. . (Unpublished)

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Synthetic biological gene networks are typically conceptualized and visualized as static graphs with nodal and edge dynamics that are time invariant. This conceptualization of biological programming stands in stark contrast to the transient nature of biological dynamics, which are driven by labile biomolecules. Here we demonstrate the use of dynamical structure function theory to evaluate and visualize network dynamics within synthetic biological circuits. We introduce the theory of dynamical structure functions as a tool for understanding network dynamics in synthetic gene networks. We show in particular, that canonical biological crosstalk and resource loading effects in synthetic biology can be quantified directly using dynamical structure functions from simulation and experimental data. We illustrate the importance of knowing these loading effects through several example systems, showing that crosstalk imbalance in feed-forward loops can explain circuit failure or performance limitations. Finally, we show how dynamical structure functions can be used to diagnose crosstalk and network imbalance to explain failure modes in two types of synthetic biocircuits: an in vitro genelet repressilator and an E. coli based transcriptional event detector. We show that dynamical structure functions can be used as a form of inverse modeling, to pinpoint biological parts within a complex biological circuit that need revision or improvement.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemData
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
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. This version posted March 10, 2021. We would like to acknowledge Sean Warnick, Vipul Singhal, Shara Balakrishnan, and Anandh Swaminanthan for insightful conversations on network reconstruction algorithms. We would like to thank and acknowledge Victoria Hsiao, Ophelia Venturelli, Clarmyra Hayes, Emmanuel de los Santos, Joe Meyerowitz, and Zachary Sun 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 FA9550-14-1-0060, the Defense Advanced Research Projects Agency, Grants HR0011-12-C-0065 and FA8750-19-2-0502, the Army Research Office Young Investigator Program, Grant W911NF-20-1-0165, the National Science Foundation, Grant 1317291, and the John and Ursula Kanel Charitable Foundation. Author 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 modeling. J. G. and R. M. M. secured research funding. R. M. M. supervised the research process. Data Accessibility: All data files and network reconstruction code can be obtained from the GitHub repository
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
John and Ursula Kanel Charitable FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20210311-095327802
Persistent URL:
Official Citation:Discovery of Dynamical Network Models for Genetic Circuits from Time-Series Data with Incomplete Measurements. Enoch Yeung, Jongmin Kim, Ye Yuan, Jorge Goncalves, Richard M Murray. bioRxiv 2021.03.10.434835; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108395
Deposited By: Tony Diaz
Deposited On:12 Mar 2021 18:06
Last Modified:12 Mar 2021 18:06

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