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Cell-Free Extract Data Variability Reduction in the Presence of Structural Non-Identifiability

Singhal, Vipul and Murray, Richard M. (2019) Cell-Free Extract Data Variability Reduction in the Presence of Structural Non-Identifiability. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200131-124409997

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Abstract

The bottom up design of genetic circuits to control cellular behavior is one of the central objectives within Synthetic Biology. Performing design iterations on these circuits in vivo is often a time consuming process, which has led to E. coli cell extracts to be used as simplified circuit prototyping environments. Cell extracts, however, display large batch-to-batch variability in gene expression. In this paper, we develop the theoretical groundwork for a model based calibration methodology for correcting this variability. We also look at the interaction of this methodology with the phenomenon of parameter (structural) non-identifiability, which occurs when the parameter identification inverse problem has multiple solutions. In particular, we show that under certain consistency conditions on the sets of output-indistinguishable parameters, data variability reduction can still be performed, and when the parameter sets have a certain structural feature called covariation, our methodology may be modified in a particular way to still achieve the desired variability reduction.


Item Type:Report or Paper (Discussion Paper)
ORCID:
AuthorORCID
Murray, Richard M.0000-0002-5785-7481
Additional Information:Submitted, 2019 American Control Conference (ACC). This work was supported by the SBIR-STTR grant W911NF-16-P-0003 and the 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.
Funders:
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:20200131-124409997
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200131-124409997
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101035
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:31 Jan 2020 20:56
Last Modified:31 Jan 2020 20:56

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