Marin de Mas Igor, Herand Helena, Carrasco Jorge, Nielsen Lars K, Johansson Pär I
Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark.
CAG Center for Endotheliomics, Copenhagen University Hospital, 2100 Copenhagen, Denmark.
Bioengineering (Basel). 2023 May 10;10(5):576. doi: 10.3390/bioengineering10050576.
Genome-scale metabolic models (GEMs) have emerged as a tool to understand human metabolism from a holistic perspective with high relevance in the study of many diseases and in the metabolic engineering of human cell lines. GEM building relies on either automated processes that lack manual refinement and result in inaccurate models or manual curation, which is a time-consuming process that limits the continuous update of reliable GEMs. Here, we present a novel algorithm-aided protocol that overcomes these limitations and facilitates the continuous updating of highly curated GEMs. The algorithm enables the automatic curation and/or expansion of existing GEMs or generates a highly curated metabolic network based on current information retrieved from multiple databases in real time. This tool was applied to the latest reconstruction of human metabolism (Human1), generating a series of the human GEMs that improve and expand the reference model and generating the most extensive and comprehensive general reconstruction of human metabolism to date. The tool presented here goes beyond the current state of the art and paves the way for the automatic reconstruction of a highly curated, up-to-date GEM with high potential in computational biology as well as in multiple fields of biological science where metabolism is relevant.
基因组规模代谢模型（GEMs）已成为一种从整体角度理解人类新陈代谢的工具，在许多疾病研究和人类细胞系代谢工程中具有高度相关性。GEM构建依赖于缺乏人工优化且会导致模型不准确的自动化流程，或者是人工编目，这是一个耗时的过程，限制了可靠GEMs的持续更新。在此，我们提出了一种新颖的算法辅助方案，该方案克服了这些局限性，并促进了高度编目的GEMs的持续更新。该算法能够对现有GEMs进行自动编目和/或扩展，或者根据从多个数据库实时检索到的当前信息生成一个高度编目的代谢网络。此工具应用于人类新陈代谢的最新重建（Human1），生成了一系列改进和扩展参考模型的人类GEMs，并生成了迄今为止最广泛、最全面的人类新陈代谢通用重建。这里介绍的工具超越了当前的技术水平，为自动重建一个高度编目、最新的GEM铺平了道路，该GEM在计算生物学以及与新陈代谢相关的多个生物科学领域具有很高的潜力。
