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MitoCore: A curated constraint-based model for simulating human central metabolism

View ORCID ProfileAnthony C. Smith, Filmon Eyassu, Jean-Pierre Mazat, View ORCID ProfileAlan J. Robinson
doi: https://doi.org/10.1101/141101
Anthony C. Smith
1Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
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Filmon Eyassu
1Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
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Jean-Pierre Mazat
2Institute of Biochemistry and Genetics of the Cell, CNRS-UMR5095, 1 Rue Camille Saint Saёns, 33077 Bordeaux cedex, France
3University Bordeaux Segalen, 146 rue Léo Saignat, 33076 Bordeaux cedex, France
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Alan J. Robinson
1Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK
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  • For correspondence: alan.robinson@mrc-mbu.cam.ac.uk
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Abstract

Background The complexity of metabolic networks can make the origin and impact of profound changes in central metabolism occurring during disease difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many current models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localization of reactions and transport steps, incorrect reaction parameters, and confounding of prosthetic groups and free metabolites in reactions. Other common drawbacks are due to their scale, such as being difficult to parameterise and simulation results being hard to interpret. Therefore, it remains important to develop smaller, manually curated models to represent central metabolism accurately.

Results We present MitoCore, a manually curated constraint-based computer model of human metabolism that incorporates the complexity of central metabolism and simulates this metabolism successfully under normal and abnormal conditions, including hypoxia and mitochondrial diseases. MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy data interpretation. Its key innovations include accurate partitioning of metabolism between cytosol and mitochondrial matrix; correct modelling of connecting transport steps; proper differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCore’s default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG. These innovations—including over 100 reactions absent or modified from Recon 2—are essential to model central metabolism accurately.

Conclusion We anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret data and generate further hypotheses, to experienced modellers predicting the consequences of disease or using computationally intensive methods that are infeasible with larger models, as well as a teaching tool for those new to modelling and needing a small manageable model on which to learn and experiment.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 24, 2017.
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MitoCore: A curated constraint-based model for simulating human central metabolism
Anthony C. Smith, Filmon Eyassu, Jean-Pierre Mazat, Alan J. Robinson
bioRxiv 141101; doi: https://doi.org/10.1101/141101
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MitoCore: A curated constraint-based model for simulating human central metabolism
Anthony C. Smith, Filmon Eyassu, Jean-Pierre Mazat, Alan J. Robinson
bioRxiv 141101; doi: https://doi.org/10.1101/141101

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