This is the first report, to our knowledge, on a systematic method for constructing a large scale kinetic metabolic model with incomplete information on kinetic parametersr, and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium, with all necessary constraints. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. The success of our approach with incompletely input information is guaranteed by two known principles in biology, the robustness of the system and the cooperation among its various parts. (Will be pleased to be informed on other methodologies dealing with same type of problems: aoping@u.washington.edu)
Uncertainty-aware integration of deuterium metabolic imaging with genome-scale metabolic modeling reveals diet-associated feasible-space remodeling in mouse skeletal muscle
Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；German Diabetes Center (DDZ), Leibniz Institute for Diabetes Research, Germany（德国糖尿病中心，莱布尼茨糖尿病研究所）；Institute for Molecular Cardiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Germany（杜塞尔多夫大学医学院及附属医院分子心脏病学研究所）；Cardiovascular Research Institute Düsseldorf (CARID), Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Germany（杜塞尔多夫心血管研究所）；German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany（德国糖尿病研究中心）
Reply to ''Comment on 'Regularizing Capacity of Metabolic Networks' ''
在最近的一篇论文[C。Marr，M. Mueller-Linow和M. T. Huett，Phys.Rev.E75，041917（2007）]，我们讨论了与随机对应物相比，真实代谢网络拓扑结构正则化复杂二元动力学的显著潜力。在他们的评论[P.霍姆和M。Huss，arXiv：0705.4084v1]，Holme和Huss批评了我们的方法，并用更现实的动力学重复了我们的研究，其中程式化的反应动力学是在成对反应集上实现的。作者发现从代谢网络和随机化对应物重建的反应集之间没有动态差异。我们重现作者的观察，并发现他们的算法导致一个动态的碎片，从而消除了包含在图中的拓扑信息。因此，他们的方法不能排除代谢网络的拓扑结构和稳态的普遍存在之间的联系。
A generalizable framework for unlocking missing reactions in genome-scale metabolic networks using deep learning
University of South Carolina（南卡罗来纳大学）；Central South University（中南大学）；Peking University（北京大学）；Yangtze Delta Region Institute, University of Electronic Science and Technology of China（电子科技大学长三角研究院）；National Technology Innovation Center of Synthetic Biology（国家合成生物技术创新中心）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
