Abstract
Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.
Highlights
Cell metabolism is known to play a key role in the pathogenesis of various diseases [1] such as Parkinson’s disease [2] and cancer [3]
To maximize the predictive power of a metabolic model when conditioning on a specific context, for instance the energy metabolism of a neuron or the metabolism of liver, recent efforts go into the development of context-specific metabolic models [8,9,10,11,12,13]
We present FASTCORE, a fast algorithm for the reconstruction of compact context-specific metabolic network models
Summary
Cell metabolism is known to play a key role in the pathogenesis of various diseases [1] such as Parkinson’s disease [2] and cancer [3]. The study of human metabolism has been greatly advanced by the development of computational models of metabolism, such as Recon 1 [4], the Edinburgh human metabolic network [5], and Recon 2 [6] These are genome-scale metabolic network models that have been reconstructed by combining various sources of ‘omics’ and literature data, and they involve a large set of biochemical reactions that can be active in different contexts, e.g., different cell types or tissues [7]. To maximize the predictive power of a metabolic model when conditioning on a specific context, for instance the energy metabolism of a neuron or the metabolism of liver, recent efforts go into the development of context-specific metabolic models [8,9,10,11,12,13] These are network models that are derived from global models like Recon 1, but they only contain a subset of reactions, namely, those reactions that are active in the given context. Such contextspecific metabolic models are known to exhibit superior explanatory and predictive power than their global counterparts [10,14,15]
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