Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Wyss Institute for Biologically Inspired Engineering, Boston, MA 02115, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Department of Molecular Biology, Umeå University, Umeå, 90187, Sweden; The Laboratory for Molecular Infection Medicine Sweden, Umeå, 90187, Sweden.
Curr Opin Microbiol. 2021 Oct;63:259-266. doi: 10.1016/j.mib.2021.08.006. Epub 2021 Aug 27.
Genome scale metabolic models (GEMs) offer a powerful means of integrating genome and biochemical information on an organism to make testable predictions of metabolic functions at different conditions and to systematically predict essential genes that may be targeted by drugs. This review describes how Plasmodium GEMs have become increasingly more accurate through the integration of omics and experimental genetic data. We also discuss how GEMs contribute to our increasing understanding of how Plasmodium metabolism is reprogrammed between life cycle stages.
基因组规模代谢模型（GEMs）提供了一种强大的方法，可将基因组和生物化学信息整合到生物体中，以对不同条件下的代谢功能进行可测试的预测，并系统地预测可能成为药物靶点的必需基因。本文综述了通过整合组学和实验遗传数据，如何使疟原虫 GEM 变得更加准确。我们还讨论了 GEM 如何帮助我们越来越了解疟原虫代谢在生命周期阶段之间是如何重新编程的。
