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Fast Reconstruction of Compact Context-Specific Metabolic Network Models

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  • Nikos Vlassis
  • Maria Pires Pacheco
  • Thomas Sauter

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.Author Summary: Metabolism comprises all life-sustaining biochemical processes. It plays an essential role in various aspects of biology, including the development and progression of many diseases. As the metabolism of a living cell involves several thousands of small molecules and their conversion, a full analysis of such a metabolic network is only feasible using computational approaches. In addition, metabolism differs significantly from cell to cell and over different contexts. Therefore, the efficient generation of context-specific mathematical models is of high interest. We present fastcore, a fast algorithm for the reconstruction of compact context-specific metabolic network models. The algorithm takes as input a global metabolic model and a set of reactions that are known to be active in a given context, and it produces a context-specific model. fastcore is significantly faster than other algorithms, typically obtaining a genome-wide reconstruction in a few seconds. High-throughput model building will soon become a common procedure for the integration and analysis of omics data, and we foresee many future applications of fastcore in disease and patient specific metabolic modeling.

Suggested Citation

  • Nikos Vlassis & Maria Pires Pacheco & Thomas Sauter, 2014. "Fast Reconstruction of Compact Context-Specific Metabolic Network Models," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-10, January.
  • Handle: RePEc:plo:pcbi00:1003424
    DOI: 10.1371/journal.pcbi.1003424
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    1. Mohammad H. Mirhakkak & Xiuqiang Chen & Yueqiong Ni & Thorsten Heinekamp & Tongta Sae-Ong & Lin-Lin Xu & Oliver Kurzai & Amelia E. Barber & Axel A. Brakhage & Sebastien Boutin & Sascha Schäuble & Gian, 2023. "Genome-scale metabolic modeling of Aspergillus fumigatus strains reveals growth dependencies on the lung microbiome," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Oveis Jamialahmadi & Sameereh Hashemi-Najafabadi & Ehsan Motamedian & Stefano Romeo & Fatemeh Bagheri, 2019. "A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-29, April.
    3. André Schultz & Amina A Qutub, 2016. "Reconstruction of Tissue-Specific Metabolic Networks Using CORDA," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-33, March.
    4. Yuefan Huang & Vakul Mohanty & Merve Dede & Kyle Tsai & May Daher & Li Li & Katayoun Rezvani & Ken Chen, 2023. "Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    5. Zhepu Ruan & Kai Chen & Weimiao Cao & Lei Meng & Bingang Yang & Mengjun Xu & Youwen Xing & Pengfa Li & Shiri Freilich & Chen Chen & Yanzheng Gao & Jiandong Jiang & Xihui Xu, 2024. "Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. Hulda S Haraldsdóttir & Ronan M T Fleming, 2016. "Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-30, November.

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