Metabolic Flux Modelling with 13C tracers with applications for human cerebral metabolism
Title: Metabolic Flux Modelling with 13C tracers with applications for human cerebral metabolism
SNIC Project: LiU-2019-16
Project Type: LiU Compute
Principal Investigator: Nicolas Sundqvist <nicolas.sundqvist@liu.se>
Affiliation: Linköpings universitet
Duration: 2019-05-09 – 2021-06-01
Classification: 10610
Keywords:

Abstract

Investigating complex biological systems, such as cellular metabolism, is a long-standing field of research spanning and combining areas such as cellular physiology, biochemistry and biotechnology. While the metabolic phenotype in human cells can be characterised by many different parameters, the most important parameters, used to understand the inner workings of the metabolism, are the intracellular metabolic conversion rates, also called metabolic fluxes. These fluxes describe how metabolite conversions occur throughout the system and are very difficult to measure in living tissue. Currently, the approach of metabolic flux analysis (MFA) provides the best solution for quantitatively determining the metabolic fluxes. Metabolic flux analysis uses mathematical modelling to determine the metabolic fluxes based on the distribution of isotopically labelled metabolite data. In the past the MFA approach have mainly been used for mapping the metabolism of simpler organism such as E-coli and have only to a limited extent been used to evaluate more complex systems, such as the human metabolism. While MFA can accurately determine the flux configuration of complex systems, the modelling part of the methodology needs to be further developed. Thus, the aim for this project is to expand on the existing modelling framework in order to develop a robust, reliable and realistic methodology for modelling metabolic fluxes in human systems. Further, the knowledge acquired from these metabolic models will be combined with a mechanistic understanding for the cerebral activity and blood flow, also gained through mathematical modelling, to gain a wholistic understanding of how the human cerebral metabolism works.