A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation

BMC Systems Biology
Stanislav Sokolenko, Marc G Aucoin

Abstract

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were ...Continue Reading

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Citations

Sep 14, 2016·BMC Systems Biology·Stanislav SokolenkoMarc G Aucoin
Nov 30, 2016·Oncology Letters·Lidao BaoAgula Bo

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Methods Mentioned

BETA
nuclear magnetic resonance
NMR
profiler

Software Mentioned

Linux
Chenomx NMR Suite
mgcv
R
Apache
dplyr
gam

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