Abstract
13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.
Highlights
Systems Biology combines high-throughput experimentation with quantitative analysis and computational modeling to approach an understanding on how cellular phenotypes emerge from molecular interactions (Wolkenhauer, 2001; Westerhoff and Hofmeyr, 2005)
The collection is illustrated with typical 13C metabolic flux analysis (MFA) examples at hand: First, we demonstrate that the Flux Markup Language (FluxML) model format unlocks the comparability of state-of-the-art simulators, an aspect that is dearly missing, even 20 years after advent of the first simulators
Two decades of experiences with planning, modeling and analyzing isotope labeling experiment (ILE) and the continuous exchange with the 13CFLUX(2) user community have led to the specification of the updated FluxML format, which we present in this work
Summary
Systems Biology combines high-throughput experimentation with quantitative analysis and computational modeling to approach an understanding on how cellular phenotypes emerge from molecular interactions (Wolkenhauer, 2001; Westerhoff and Hofmeyr, 2005). Since the flux estimation procedure with such networks is computationally demanding, a number of algorithms have been proposed over the last two decades to speed up the core computation steps (Wiechert et al, 1999; Zamboni et al, 2005; Antoniewicz et al, 2007; Weitzel et al, 2007; Tepper and Shlomi, 2015) These developments have led to the emergence of a variety of software tools that are almost as diverse as the experimental scenarios of 13C MFA (see Supplementary S1 Table 1.1). A 13C MFA modeling language empowers modelers to concentrate on the specification of the underlying network model, independent of the specific implementation in a software tool (cf Figure 1) Such an “Esperanto” format is, the central component for serving the FAIR Data Principles (Wilkinson et al, 2016). We illustrate how easy the configuration task of parallel labeling experiments is with FluxML
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