Naama Tepper1, Tomer Shlomi. 1. Department of Computer Science, Technion-Israel Institute of Technology, Haifa 32000, Israel and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
Abstract
MOTIVATION: Metabolic flux analysis (MFA) is a commonly used approach for quantifying metabolic fluxes based on tracking isotope labeling of metabolite within cells. Tandem mass-spectrometry (MS/MS) has been recently shown to be especially useful for MFA by providing rich information on metabolite positional labeling, measuring isotopic labeling patterns of collisional fragments. However, a major limitation in this approach is the requirement that the positional origin of atoms in a collisional fragment would be known a priori, which in many cases is difficult to determine. RESULTS: Here we show that MS/MS data could also be used to improve flux inference even when the positional origin of fragments is unknown. We develop a novel method, metabolic flux analysis/unknown fragments, that extends on standard MFA and jointly searches for the most likely metabolic fluxes together with the most plausible position of collisional fragments that would optimally match measured MS/MS data. MFA/UF is shown to markedly improve flux prediction accuracy in a simulation model of gluconeogenesis and using experimental MS/MS data in Bacillus subtilis.
MOTIVATION: Metabolic flux analysis (MFA) is a commonly used approach for quantifying metabolic fluxes based on tracking isotope labeling of metabolite within cells. Tandem mass-spectrometry (MS/MS) has been recently shown to be especially useful for MFA by providing rich information on metabolite positional labeling, measuring isotopic labeling patterns of collisional fragments. However, a major limitation in this approach is the requirement that the positional origin of atoms in a collisional fragment would be known a priori, which in many cases is difficult to determine. RESULTS: Here we show that MS/MS data could also be used to improve flux inference even when the positional origin of fragments is unknown. We develop a novel method, metabolic flux analysis/unknown fragments, that extends on standard MFA and jointly searches for the most likely metabolic fluxes together with the most plausible position of collisional fragments that would optimally match measured MS/MS data. MFA/UF is shown to markedly improve flux prediction accuracy in a simulation model of gluconeogenesis and using experimental MS/MS data in Bacillus subtilis.