Modeling Brain Metabolism through Calculus/Statistical Isotopic Tracing: A Cost-Efficient Predictive Framework

Abstract

<title>Abstract</title> Isotopic tracing is a process in which metabolites are labeled with isotopes that can be tracked throughout specific cellular processes(1). An isotope is a version of a chemical that has the same number of protons and electrons, but differs in its neutron composition. This process works by substituting molecules with isotopes and monitoring their movement through systems over time. Because isotopes behave like their parent molecules but differ in mass, tracing them is feasible and reveals each molecule’s role in a system. However, determining the precise entry rate for labeled metabolites is challenging. The use of heavier isotopes makes reactions appear slower than they actually are, because they do not form/break bonds as readily. This phenomenon, Mass-Dependent Fractionation (MDF), causes measurements to underestimate the true reaction rate in a system. In a system that is as fast as brain metabolism, these issues can lead to falsified data. After experimentation, it was concluded that through mathematical applications in isotopic tracing models, the rate of entry can be accurately predicted, strengthening <italic>in vivo</italic> data. By doing so, the progression of isotopic labeling can be predicted without the influence of MDF bias. To test this, brain metabolism was modeled with sticky notes, with different colors modeling two carbon isotopes. Patterns were observed and analyzed as the experiment ran under numerous scenarios. This study demonstrates that using simple calculus to generate models enables preliminary experiment analysis, allowing researchers to compare observed results with model predictions, and control for potential MDF bias.

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