T 辅助细胞分化和可塑性的动力学:计算模型如何增进我们的理解?

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Current Opinion in Systems Biology Pub Date : 2024-02-16 DOI:10.1016/j.coisb.2024.100508
Pradyumna Harlapur, Atchuta Srinivas Duddu, Mohit Kumar Jolly
{"title":"T 辅助细胞分化和可塑性的动力学:计算模型如何增进我们的理解?","authors":"Pradyumna Harlapur,&nbsp;Atchuta Srinivas Duddu,&nbsp;Mohit Kumar Jolly","doi":"10.1016/j.coisb.2024.100508","DOIUrl":null,"url":null,"abstract":"<div><p>Naïve CD4+ T cells can polarize into diverse functionally distinct effector cell types such as Th1, Th2, Th17 and Treg. These cell types can also interconvert among one another. The dynamics of T-cell differentiation and plasticity is driven by complex interactions involving many feedback loops among cytokines, intracellular signalling and lineage-determining transcription factors. In the past two decades, mechanistic computational models have played an instrumental role in understanding the underlying emergent dynamics. Here, we highlight the key concepts elucidated from such modelling efforts – a) multistability in underlying gene regulatory networks, b) the (co-) existence of stable hybrid cell states (Th1/Th2, Th1/Th17, Th2/Th17), and c) population-level dynamics of T-cell differentiation. These models, in close integration with experimental data, have improved our understanding of cell-state transitions and trajectories implicated in intracellular and population dynamics of T-cell plasticity.</p></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamics of T-helper cell differentiation and plasticity: How have computational models improved our understanding?\",\"authors\":\"Pradyumna Harlapur,&nbsp;Atchuta Srinivas Duddu,&nbsp;Mohit Kumar Jolly\",\"doi\":\"10.1016/j.coisb.2024.100508\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Naïve CD4+ T cells can polarize into diverse functionally distinct effector cell types such as Th1, Th2, Th17 and Treg. These cell types can also interconvert among one another. The dynamics of T-cell differentiation and plasticity is driven by complex interactions involving many feedback loops among cytokines, intracellular signalling and lineage-determining transcription factors. In the past two decades, mechanistic computational models have played an instrumental role in understanding the underlying emergent dynamics. Here, we highlight the key concepts elucidated from such modelling efforts – a) multistability in underlying gene regulatory networks, b) the (co-) existence of stable hybrid cell states (Th1/Th2, Th1/Th17, Th2/Th17), and c) population-level dynamics of T-cell differentiation. These models, in close integration with experimental data, have improved our understanding of cell-state transitions and trajectories implicated in intracellular and population dynamics of T-cell plasticity.</p></div>\",\"PeriodicalId\":37400,\"journal\":{\"name\":\"Current Opinion in Systems Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452310024000040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452310024000040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

新生的 CD4+ T 细胞可极化为各种功能不同的效应细胞类型,如 Th1、Th2、Th17 和 Treg。这些细胞类型还可以相互转化。T 细胞分化和可塑性的动态是由细胞因子、细胞内信号传导和决定细胞系的转录因子之间涉及许多反馈回路的复杂相互作用驱动的。在过去二十年中,机理计算模型在理解潜在的突发性动力学方面发挥了重要作用。在此,我们重点介绍从这些建模工作中阐明的关键概念--a) 基本基因调控网络的多稳定性;b) 稳定混合细胞状态(Th1/Th2、Th1/Th17、Th2/Th17)的(共同)存在;c) T 细胞分化的群体级动态。这些模型与实验数据紧密结合,提高了我们对细胞状态转换以及细胞内和群体动态 T 细胞可塑性相关轨迹的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Dynamics of T-helper cell differentiation and plasticity: How have computational models improved our understanding?

Naïve CD4+ T cells can polarize into diverse functionally distinct effector cell types such as Th1, Th2, Th17 and Treg. These cell types can also interconvert among one another. The dynamics of T-cell differentiation and plasticity is driven by complex interactions involving many feedback loops among cytokines, intracellular signalling and lineage-determining transcription factors. In the past two decades, mechanistic computational models have played an instrumental role in understanding the underlying emergent dynamics. Here, we highlight the key concepts elucidated from such modelling efforts – a) multistability in underlying gene regulatory networks, b) the (co-) existence of stable hybrid cell states (Th1/Th2, Th1/Th17, Th2/Th17), and c) population-level dynamics of T-cell differentiation. These models, in close integration with experimental data, have improved our understanding of cell-state transitions and trajectories implicated in intracellular and population dynamics of T-cell plasticity.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Current Opinion in Systems Biology
Current Opinion in Systems Biology Mathematics-Applied Mathematics
CiteScore
7.10
自引率
2.70%
发文量
20
期刊介绍: Current Opinion in Systems Biology is a new systematic review journal that aims to provide specialists with a unique and educational platform to keep up-to-date with the expanding volume of information published in the field of Systems Biology. It publishes polished, concise and timely systematic reviews and opinion articles. In addition to describing recent trends, the authors are encouraged to give their subjective opinion on the topics discussed. As this is such a broad discipline, we have determined themed sections each of which is reviewed once a year. The following areas will be covered by Current Opinion in Systems Biology: -Genomics and Epigenomics -Gene Regulation -Metabolic Networks -Cancer and Systemic Diseases -Mathematical Modelling -Big Data Acquisition and Analysis -Systems Pharmacology and Physiology -Synthetic Biology -Stem Cells, Development, and Differentiation -Systems Biology of Mold Organisms -Systems Immunology and Host-Pathogen Interaction -Systems Ecology and Evolution
期刊最新文献
From regulation of cell fate decisions towards patient-specific treatments, insights from mechanistic models of signalling pathways Editorial overview: Systems biology of ecological interactions across scales A critical review of multiscale modeling for predictive understanding of cancer cell metabolism Network modeling approaches for metabolic diseases and diabetes Contents
×
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