MetClassNet: new approaches to bridge the gap between genome-scale metabolic networks and untargeted metabolomics

代谢是一个关键的生物过程，在生物体中受到环境暴露、遗传变异和饮食的调节。了解新陈代谢对提高植物性能、营养成分和了解人类健康和福祉至关重要。代谢反应可能是复杂的，涉及成百上千的小分子（代谢物），由成千上万的生化反应连接起来。它们共同构成了一个密集的网络，其整体通常被称为基因组规模代谢网络（GSMN）。在这种背景下，代谢组学是实验观察代谢组（一组代谢物）变化的基础方法。测量代谢组的主要分析平台之一是质谱（MS），它通常与分离方法（例如液相色谱，LC-MS）相结合。尽管这项技术正在迅速发展，但代谢组学的广泛应用仍然面临着一些挑战。代谢物鉴定仍然是这些挑战之一。然而，实验获得的数据和计算机生成的GSMN只有部分重叠，通常不会同时进行研究。在MetClassNet中，我们假设这些困难可以通过设计新的数据结构和算法来克服，这些数据结构和算法将利用分子之间的连接（网络）。这种综合方法将通过统一gsmn和从实验数据中获得的网络来提高数据分析的能力。因此，MetClassNet将提出一个新的计算框架和新方法，以帮助解决数据分析和数据解释中的主要代谢组学挑战。该框架将通过使用直接映射、本体和化学类信息将实验获得的信息和gsmn连接起来，从而集成这些信息。在项目结束时，MetClassNet将为社区提供一个创新的工具集，通过将代谢组学数据集成（而不仅仅是导出）到一个网络系统中，它将有可能超越基于表的分析。为此，MetClassNet将创建新的算法和工具来挖掘这些网络，从而增加我们对代谢组的了解。开发的框架还将简化代谢组学和gsmn之间的连接，从而填补当前代谢网络数据库的空白。在MetClassNet项目中，我们将展示计算框架的好处，以解决与衰老、毒理学、癌症和营养相关的代谢调节研究。最后，MetClassNet联盟将把向社区开放数据、协议和软件的必要性放在其议程的首位。

Metabolism is a key biological process which is modulated in living organisms in response to environmental exposure, genetic variations and diet. Understanding metabolism is essential to improve plant performance, nutritional content, and to understand Human health and well-being. The metabolic response can be complex, involving hundreds to thousands of small molecules (metabolites) connected by thousands of biochemical reactions. Together, they constitute a dense network, in its entirety often called Genome Scale Metabolic Network (GSMN). Within this context, metabolomics is a cornerstone approach to experimentally observe changes in the metabolome (set of metabolites). One of the main analytical platforms to measure the metabolome is Mass Spectrometry (MS) which is often coupled to separation methods (e.g. Liquid Chromatography, LC-MS). Even though the technology is advancing rapidly, several challenges remain for widespread adoption of metabolomics. Metabolite identification remains one of these challenges. Nevertheless, experimentally obtained data and in silico generated GSMN overlap only partially and are generally not studied simultaneously. In MetClassNet, we hypothesis that these difficulties could be overcome by designing new data structures and algorithms which will exploit the connectivity (network) between molecules. This integrative approach will boost the power of data analysis by unifying GSMNs and networks obtained from experimental data. Hence, MetClassNet will propose a new computational framework and novel methods to help with tackling main metabolomics challenges in data analysis and data interpretation. This framework will integrate information from experimentally derived information and GSMNs by bridging them using direct mapping, ontologies and chemical class information.At the end of the project, MetClassNet will offer the community an innovative tool set where it will be possible to go beyond table based analysis of metabolomics data by integrating (and not just exporting) them into a network system. To this end, MetClassNet will create novel algorithms and tools to mine these networks allowing to increase our knowledge of the metabolome. The developed framework will also ease the connection between metabolomics and GSMNs, hence allowing to fill the gaps in current databases of metabolic networks. Within MetClassNet project, we will showcase the benefit of the computational framework to address the study of metabolic modulations related to ageing, toxicology, cancer and nutrition. Finally, MetClassNet consortium will put the necessity of opening data, protocols and software to the community high in its agenda.