Automation on the generation of genome scale metabolic models
Universidad Pinar del Río “Hermanos Saíz Montes de Oca”（比纳尔德里奥大学“塞斯·蒙特斯·德奥卡兄弟”）；Cátedra Energesis de Tecnología Interdisciplinar, Universidad Católica de Valencia San Vicente Mártir（瓦伦西亚天主教圣文森特马蒂尔跨技术学院Energesis）；Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia（瓦伦西亚理工大学纯数学与应用数学大学研究所）
R. Reyes, D. Gamermann, A. Montagud, D. Fuente, J. Triana, J. F. Urchuegía, P. Fernández de Córdoba
备注：
24 pages, 2 figures, 2 tables
发表信息：
Journal of Computational Biology, 19(12), 1295-1306
Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider.
Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed.
Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.
Phylogenetic tree reconstruction from genome-scale metabolic models
Universidad Católica de Valencia San Vicente Mártir（瓦伦西亚天主教大学圣维森特马蒂尔）；Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia（瓦伦西亚理工大学纯数学与应用数学研究所）
Sampling from the Solution Space and Metabolic Environments of Genome-Scale Metabolic Models
KU Leuven（荷语鲁汶大学）；Université Paris-Saclay, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE)（巴黎萨克雷大学，法国国家农业食品与环境研究院）
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）
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）
Fast reconstruction of compact context-specific metabolic networks via integration of microarray data
最近，我们提出了一种用于快速重建紧凑上下文特定代谢网络（FASTCORE）的算法，该算法允许将重建时间降低到秒的时间量级（Vlavis et al.，2014年）。这种极低的计算需求为提高模型的质量开辟了新的可能性。重建高质量的模型需要几轮模型重建、根据真实实验数据测试模型的预测、输入模型和核心反应集的管理步骤以及交叉验证分析。这些半自动化的模型策展步骤在这种程度上是不可能的竞争算法，由于他们的高计算需求。为了适应FASTCORE的集成微阵列数据，因此，我们提出了一个新的工作流程：FASTCORMICS。FASTCORMICS需要输入微阵列数据和基因组规模的重建。FASTCORMICS没有启发式参数设置，并且具有低计算需求，总体构建时间大约为几分钟。FASTCORMICS用离散化工具Barcode预处理微阵列数据（Zillox等，2007）。条形码使用关于给定微阵列平台的每个探针组的强度分布的先验知识，以在表达的基因和非表达的基因之间分离。该预处理步骤允许规避设置启发式表达阈值的需要，这对于输出模型是关键的，因为响应于该阈值，可能包括或排除替代路径或子系统，从而严重改变模型的功能。一般来说，FASTCORMICS优于竞争算法，并允许以高吞吐量的方式获得高质量，鲁棒的模型。这将允许使用代谢建模作为用于分析微阵列数据的常规过程，例如在个性化医学领域。
