Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms.
Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms.
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DOI：
10.3390/metabo11090606
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发表时间：
2021-09-07
期刊：
影响因子：
4.1
通讯作者：
Chandrasekaran S
中科院分区：
文献类型：
--
作者：
Chung CH;Lin DW;Eames A;Chandrasekaran S
基因组尺度代谢模型（GEMs）是从系统层面理解代谢的有力工具。然而，GEMs最基本的形式无法解释细胞调节。多种机制调节细胞代谢，使生物体能够对各种条件作出反应。GEMs的这一局限性促使人们开发新的方法来整合监管机制，从而提高了GEMs的预测能力，扩大了GEMs的范围。在这里，我们涵盖了包括六种调节机制的综合模型：转录调节网络（trn），翻译后修饰（PTMs），表观遗传学，蛋白质-蛋白质相互作用和蛋白质稳定性（PPIs/PS），变质和信号网络。我们讨论了22种综合GEM建模方法，以及这些方法如何用于模拟正常和病理条件下的代谢调节。虽然这些进展是显著的，但仍然需要全面和广泛地将监管限制纳入GEMs。最后，我们讨论了构建具有调控的gem所面临的挑战，并强调了成功建模代谢调控需要解决的问题。结合多种调控机制及其相互作用的下一代综合GEMs对于发现细胞类型和疾病特异性代谢控制机制将是非常宝贵的。
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein–protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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DOI：
10.1126/science.1234012
发表时间：
2013-06-07
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Chang RL;Andrews K;Kim D;Li Z;Godzik A;Palsson BO
通讯作者：
Palsson BO
影响因子：
9.9
作者：
Chubukov, Victor;Uhr, Markus;Le Chat, Ludovic;Kleijn, Roelco J.;Jules, Matthieu;Link, Hannes;Aymerich, Stephane;Stelling, Joerg;Sauer, Uwe
通讯作者：
Sauer, Uwe
DOI：
10.1073/pnas.1811971115
发表时间：
2018-10-23
影响因子：
11.1
作者：
Brunk, Elizabeth;Chang, Roger L.;Lewis, Nathan E.
通讯作者：
Lewis, Nathan E.
影响因子：
2.2
作者：
Chandrasekaran, Sriram
通讯作者：
Chandrasekaran, Sriram
影响因子：
--
作者：
Banos, Daniel Trejo;Trebulle, Pauline;Elati, Mohamed
通讯作者：
Elati, Mohamed