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

{{Paper
|Title=Cell-Free Extract Data Variability Reduction in the Presence of Structural Non-Identifiability
|Authors=Vipul Singhal and Richard M. Murray
|Source=Submitted, 2019 American Control Conference (ACC)
|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 cer- tain structural feature called covariation, our methodology may be modified in a particular way to still achieve the desired variability reduction.
|URL=http://www.cds.caltech.edu/~murray/preprints/sm19-acc_s.pdf
|Type=Conference paper
|ID=2018d
|Tag=sm19-acc
|Funding=Synvitrobio SBIR
}}