ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

Pierre Millard,Jean-Charles Portais,Patrick Kiefer,Stéphanie Heux,Julia A Vorholt,Uwe Schmitt,Kiran Raosaheb Patil

doi:10.1371/journal.pcbi.1007799

Abstract

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Highlights

Metabolic flux analysis (MFA) with stable isotope tracers, typically a 13C-labeled carbon source, allows intracellular fluxes to be quantified in a wide range of organisms and is a major tool in the fields of biotechnology [1,2,3], systems biology [4,5,6] and medicine [7,8]
Metabolic flux analysis allows the quantification of metabolic fluxes in vivo, i.e. the actual rates of biochemical conversions in biological systems, and is increasingly used to probe metabolic activity in biology, biotechnology and medicine
This network of 18 metabolites and 20 reactions includes three topological motifs classically found in metabolism: a linear pathway, a branching node and a cycle

Summary

Introduction

Metabolic flux analysis (MFA) with stable isotope tracers, typically a 13C-labeled carbon source, allows intracellular fluxes to be quantified in a wide range of organisms and is a major tool in the fields of biotechnology [1,2,3], systems biology [4,5,6] and medicine [7,8]. To ensure fluxes are identifiable, the extracellular fluxes and the labeling of upstream metabolites must be measured (as well as the intracellular metabolite concentrations for instationary 13C-MFA approaches) This is a major limitation for investigating i) pathways far downstream of the labeled nutrient(s), ii) networks with reaction gaps (e.g. an uncertain network topology), iii) incomplete datasets, iv) experiments performed in rich media, or v) situations where the isotopic transitions remain uncertain or complex (e.g. 2H tracer) [1,15]. This makes the entire experimental and computational workflow very time consuming, costly and error prone. There is a need for more robust and scalable approaches to quantify metabolic fluxes in biochemical systems

Methods

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Discussion

Conclusion