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Transforming Data Across Environments Despite Structural Non-Identifiability

Singhal, Vipul and Murray, Richard M. (2019) Transforming Data Across Environments Despite Structural Non-Identifiability. In: 2019 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 5639-5646. ISBN 978-1-5386-7926-5.

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The phenomenon of parameter (structural) non-identifiability can pose significant challenges to the use of parametrized dynamical models. We demonstrate that, for the case of models being used to transform data across environments, it is possible to derive conditions under which the presence of structural non-identifiability does not hinder our modeling objective. We also show that when the non-identifiability has a certain structural feature called (thin) covariation, these conditions are violated, and the transformation methodology must be modified. We demonstrate these results on the problem of correcting batch effects in cell extracts, which are used as rapid prototyping platforms in synthetic biology.

Item Type:Book Section
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URLURL TypeDescription
Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2019 AACC. This work was supported by SBIR-STTR grant W911NF-16-P-0003 and AFOSR grant FA9550-14-1-0060. The authors would like to thank Samuel Clamons, Wolfgang Halter, William Poole, Anandh Swaminathan and Andras Gyorgy for useful discussions.
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)W911NF-16-P-0003
Air Force Office of Scientific Research (AFOSR)FA9550-14-1-0060
Record Number:CaltechAUTHORS:20190905-153443641
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Official Citation:V. Singhal and R. M. Murray, "Transforming Data Across Environments Despite Structural Non-Identifiability," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 5639-5646. URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98447
Deposited By: Tony Diaz
Deposited On:05 Sep 2019 22:39
Last Modified:03 Oct 2019 21:41

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