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• W2164842504 abstract "Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest.We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions.The methods described in this article are made available as a Python package (LGPL license) and also accessible through a web interface. For more information, see https://team.inria.fr/ibis/wellinverter." @default.
• W2164842504 created "2016-06-24" @default.
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• W2164842504 date "2015-06-10" @default.
• W2164842504 modified "2023-10-16" @default.
• W2164842504 title "Robust reconstruction of gene expression profiles from reporter gene data using linear inversion" @default.
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• W2164842504 doi "https://doi.org/10.1093/bioinformatics/btv246" @default.
• W2164842504 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4765859" @default.
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