Understanding flux switching in metabolic networks through an analysis of synthetic lethals

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY NPJ Systems Biology and Applications Pub Date : 2024-09-17 DOI:10.1038/s41540-024-00426-5
Sowmya Manojna Narasimha, Tanisha Malpani, Omkar S. Mohite, J. Saketha Nath, Karthik Raman
{"title":"Understanding flux switching in metabolic networks through an analysis of synthetic lethals","authors":"Sowmya Manojna Narasimha, Tanisha Malpani, Omkar S. Mohite, J. Saketha Nath, Karthik Raman","doi":"10.1038/s41540-024-00426-5","DOIUrl":null,"url":null,"abstract":"<p>Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.</p>","PeriodicalId":19345,"journal":{"name":"NPJ Systems Biology and Applications","volume":"69 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Systems Biology and Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41540-024-00426-5","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过分析合成致死物了解代谢网络中的通量转换
生物系统是强大而冗余的。冗余可以表现为替代代谢途径。合成双致死物是一对反应,如果同时删除,就会使细胞停止生长。然而,删除一个反应后,代谢物可以通过替代途径重新定向。人们对这些途径之间的隐性联系知之甚少。在病原体的背景下了解它们对治疗创新非常有用。我们提出了一种基于约束的优化方法来识别代谢途径之间的相互依存关系。它最大限度地减少了两个反应缺失(对应于合成致死对)之间的重路由,并输出了一组对代谢重构至关重要的反应,即合成致死簇。我们描绘了不同病原体的结果,并显示这些反应跨越了不同的代谢模块,说明了新陈代谢的复杂性。最后,我们展示了两类合成致死物如何在代谢网络中发挥作用,并影响合成致死簇的不同特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
期刊最新文献
Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection. Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series. Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity. Overall biomass yield on multiple nutrient sources. Modeling critical dosing strategies for stromal-induced resistance to cancer therapy.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1