High-throughput metabolic flux analysis based on gas chromatography–mass spectrometry derived 13 C constraints Eliane Fischer, Nicola Zamboni, and Uwe Sauer * Institute of Biotechnology, ETH Z€ urich CH-8093, Z€ urich, Switzerland Received 15 August 2003 Abstract 13 C-constrained flux balancing analysis based on gas chromatography–mass spectrometry data is presented here as a simple and robust method for the estimation of intracellular carbon fluxes. In this approach, the underdetermined system of metabolite bal- ances deduced from stoichiometric relations and measured extracellular rates is complemented with 13 C constraints from metabolic flux ratio analysis. Fluxes in central carbon metabolism of exponentially growing Escherichia coli were estimated by 13 C-constrained flux balancing from three different 13 C-labeled glucose experiments. The best resolution of the network was achieved using 13 C constraints derived from [U- 13 C]glucose and [1- 13 C]glucose experiments. The corresponding flux estimate was in excellent agreement with a solution that was independently obtained with a comprehensive isotopomer model. This new methodology was also dem- onstrated to faithfully capture the intracellular flux distribution in E. coli shake flasks and 1-ml deep-well microtiter plates. Due to its simplicity, speed, and robustness, 13 C-constrained metabolic flux balancing is promising for routine and high-throughput analysis on a miniaturized scale. Ó 2003 Elsevier Inc. All rights reserved. Quantification of metabolic material flow is impor- tant for the characterization of cellular phenotypes in metabolic engineering and functional genomics. While extracellular uptake and secretion rates are readily ob- tained with standard physiological methodology, esti- mation of intracellular reaction rates is more challenging because networks are generally redundant, with multiple combinations of metabolic pathways that potentially lead to the formation of identical products [1]. Hence, intracellular fluxes are mainly assessed from 13 C tracer experiments [2,3]. In these approaches, 13 C-labeled substrates are administered and metabolic products are analyzed by methods that distinguish between different isotope labeling patterns, i.e., nuclear magnetic reso- nance (NMR) and mass spectrometry (MS) [3,4]. Direct analytical interpretation of 13 C labeling pat- terns from either method has been used for a long time in biochemical research to estimate individual pathways or reactions [3,5,6]. More recently, analytical interpre- tation of the 13 C labeling pattern in proteinogenic amino acids was developed such that several flux partitioning ratios can be quantified from a single experiment [7,8]. The individually quantified flux partitioning ratios are largely independent of each other and no further phys- iological information is required. This metabolic flux ratio (METAFoR) 1 analysis allows diagnosis of active or absent pathways or reactions in a metabolic network by quantifying relative contributions of converging pathways to the formation of a target metabolite. Beyond flux ratios, net fluxes through metabolic net- works may be deduced from 13 C labeling information when combined with material balances within a stoichi- ometric model. This requires additional measurements such as extracellular rates and biomass composition. In the most generalized methodology, several hundred isotope isomer (isotopomer) balances are included in a comprehensive network model to map metabolic fluxes to all available 13 C labeling data [2,9–11] but subsets of 13 C labeling data such as summed fractional labels [12] or * Corresponding author. Fax: +41-1-633-10-51. E-mail address: sauer@biotech.biol.ethz.ch (U. Sauer). 1 Abbreviations used: METAFoR, metabolic flux ratio; PEP, phosphoenolpyruvate; ED, Entner–Doudoroff; OAA, oxaloacetate; PP, pentose phosphate; TCA, tricarboxylic acid. 0003-2697/$ - see front matter Ó 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.ab.2003.10.036 Analytical Biochemistry 325 (2004) 308–316 ANALYTICAL BIOCHEMISTRY www.elsevier.com/locate/yabio