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Cell-Free Extract Data Variability Reduction in the Presence of Structural Non-Identifiability
Abstract The bottom up design of genetic circuits t …
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 cer- tain structural feature called covariation, our methodology may be modified in a particular way to still achieve the desired variability reduction.
achieve the desired variability reduction.  +
Authors Vipul Singhal and Richard M. Murray  +
Funding Rapid, Reliable and Repeatable Platforms for Cell-Free Prototyping +
ID 2018d  +
Source Submitted, 2019 American Control Conference (ACC)  +
Tag sm19-acc  +
Title Cell-Free Extract Data Variability Reduction in the Presence of Structural Non-Identifiability +
Type Conference paper  +
Categories Papers
Modification date
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26 September 2018 13:46:45  +
URL
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http://www.cds.caltech.edu/~murray/preprints/sm19-acc_s.pdf  +
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