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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 (Unpublished)

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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)
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.
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
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101035
Deposited By: Tony Diaz
Deposited On:31 Jan 2020 20:56
Last Modified:31 Jan 2020 20:56

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