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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. +
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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 This property is a special property in this wiki.
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26 September 2018 13:46:45 + |
URL This property is a special property in this wiki.
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http://www.cds.caltech.edu/~murray/preprints/sm19-acc_s.pdf + |
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Cell-Free Extract Data Variability Reduction in the Presence of Structural Non-Identifiability + | Title |
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