Smith Anthony C, Eyassu Filmon, Mazat Jean-Pierre, Robinson Alan J
Medical Research Council Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0XY, UK.
Institute of Biochemistry and Genetics of the Cell, CNRS-UMR5095, 1 Rue Camille Saint Saëns, 33077, Bordeaux cedex, France.
BMC Syst Biol. 2017 Nov 25;11(1):114. doi: 10.1186/s12918-017-0500-7.
The complexity of metabolic networks can make the origin and impact of changes in central metabolism occurring during diseases difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localisation 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, making them difficult to parameterise and simulation results hard to interpret. Therefore, it remains important to develop smaller, manually curated models.
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 physiological 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 interpretation of results. Its key innovations include a more accurate partitioning of metabolism between cytosol and mitochondrial matrix; better modelling of connecting transport steps; 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 necessary to model central metabolism more accurately.
We anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret their data and test 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.
代谢网络的复杂性使得疾病期间发生的中心代谢变化的起源和影响难以理解。计算机模拟有助于揭示这种复杂性,并且基因组规模代谢模型已经取得了进展。然而,许多模型在模拟异常代谢时会产生不切实际的结果,因为它们包括反应和运输步骤的错误指定和定位、错误的反应参数以及反应中辅基和游离代谢物的混淆。其他常见缺点归因于它们的规模,使其难以参数化且模拟结果难以解释。因此,开发更小的、人工整理的模型仍然很重要。
我们展示了MitoCore,这是一个人工整理的基于约束的人类代谢计算机模型,它整合了中心代谢的复杂性,并在正常和异常生理条件下(包括缺氧和线粒体疾病)成功模拟了这种代谢。MitoCore描述了324个代谢反应、线粒体与细胞质之间的83个运输步骤以及通过质膜的74种代谢物输入和输出,以产生一个规模易于管理、便于结果解释的模型。其关键创新包括更准确地划分细胞质和线粒体基质之间的代谢;更好地模拟连接运输步骤;区分反应中的辅基和游离辅因子;以及呼吸链和质子动力的新表示。MitoCore的默认参数模拟正常心肌细胞代谢,为提高可用性并允许与其他模型和分析类型进行比较,其反应和代谢物具有广泛的注释,并交叉引用了虚拟代谢人类数据库和KEGG中的标识符。这些创新——包括Recon 2中不存在或修改的100多个反应——对于更准确地模拟中心代谢是必要的。
我们预计MitoCore将成为科学家的研究工具,从希望解释数据和检验假设的实验人员,到预测疾病后果或使用大型模型不可行的计算密集型方法的经验丰富的建模人员,以及对于那些刚开始建模且需要一个小型、易于管理的模型来学习和实验的人来说,它也是一种教学工具。