Methods for improving the visualization and deconvolution of isotopic signals

Abstract

Stable isotopes and their associated mechanistic frameworks have provided the means to model biological transformations from inorganic sources to organic sinks, and their impact on long-term terrestrial carbon reservoirs. However, stable isotopes have also the potential to diagnose the mechanisms by which biological systems operate, when using 'multidimensional' analyses of the isotopic outputs. For this purpose, we suggest that isotopic signals may be treated as mathematical vectors to reveal their interrelationships. These visualizations may be achieved, thanks to multidimensional representations (the 'isotopology' method), or by appreciating the colinearity of isotopic vectors to develop a clustering analysis (the 'isotopomic' method) similar to that used in molecular biology. Both methods converge to the same mathematical form, i.e. both lead to a covariation approach. Using simple practical examples, we argue that such procedures allow the deconvolution of plant biological systems by revealing the hierarchy of contributory physiological processes.