发布求助
文献互助 智能选刊 最新文献

Current Opinion in Systems Biology最新文献

筛选
英文 中文
Systems virology at scale 大规模的系统病毒学
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.coisb.2025.100562
Cameron D. Griffiths , Andrew J. Sweatt , Kevin A. Janes
{"title":"Systems virology at scale","authors":"Cameron D. Griffiths ,&nbsp;Andrew J. Sweatt ,&nbsp;Kevin A. Janes","doi":"10.1016/j.coisb.2025.100562","DOIUrl":"10.1016/j.coisb.2025.100562","url":null,"abstract":"<div><div>Today's subcellular and multicellular models of infection are poised to tackle bigger questions about virus–host interactions and the determinants of susceptibility. This opportunity comes from increased computing power, improved model architectures, and comprehensive datasets collected from virus-infected hosts. Here we summarize recent advances in viral modeling and data science that illustrate how systems models have successfully traversed increasing time–length scales, levels of detail, and ranges of biological context. The latest progress is encouraging, but recent findings just scratch the surface given how many different viruses exist or could someday emerge–the scale of the effort should align with the scale of the challenge. Abstraction of molecular and cellular networks by systems virology complements public-health models of viral transmission that are widely applied to human populations.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100562"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mechanistic dynamic modelling of biological systems: The road ahead 生物系统的机械动力学建模：未来的道路
IF 3.4
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-07-08 DOI: 10.1016/j.coisb.2025.100553
Julio R. Banga , Alejandro F. Villaverde
{"title":"Mechanistic dynamic modelling of biological systems: The road ahead","authors":"Julio R. Banga ,&nbsp;Alejandro F. Villaverde","doi":"10.1016/j.coisb.2025.100553","DOIUrl":"10.1016/j.coisb.2025.100553","url":null,"abstract":"<div><div>Mathematical modelling is one of the pillars of systems biology. In this review, we focus on models that are <em>mechanistic</em>, i.e., they explain the mechanism by which a phenomenon takes place, and <em>dynamic</em>, i.e., they consist of differential equations that simulate the time course of a system. Our aim is to provide an updated state of the art of mechanistic dynamic modelling in systems biology. These models, which are based on first principles, are crucial for obtaining insights about complex physiological processes. They can be used to test hypotheses, predict system behaviour, and explore and optimize intervention strategies. Since biological processes are typically nonlinear, multiscale, and subject to various sources of uncertainty, the task of building and analysing robust and reliable mechanistic models is fraught with difficulties. In this paper, we provide an overview of recent developments in key topics such as model discovery and structure selection, identifiability analysis, parameter estimation, uncertainty quantification, and model reliability. We discuss the challenges and open questions in these areas and outline perspectives for future work.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100553"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine learning for predicting drug–drug interactions: Graph neural networks and beyond 预测药物-药物相互作用的机器学习：图神经网络及其他
IF 3.4
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-06-18 DOI: 10.1016/j.coisb.2025.100551
Peter Petschner , Anh Duc Nguyen , Canh Hao Nguyen , Hiroshi Mamitsuka
{"title":"Machine learning for predicting drug–drug interactions: Graph neural networks and beyond","authors":"Peter Petschner ,&nbsp;Anh Duc Nguyen ,&nbsp;Canh Hao Nguyen ,&nbsp;Hiroshi Mamitsuka","doi":"10.1016/j.coisb.2025.100551","DOIUrl":"10.1016/j.coisb.2025.100551","url":null,"abstract":"<div><div>Identification of interacting drugs before application would be imperative to mitigate the serious risk represented by drug–drug interactions for patient health. Machine learning–based methods are increasingly recognized by regulatory agencies as tools with a central role in drug development, including the identification of novel interactions. In recent years, graph and hypergraph neural networks delivered promising performance improvements compared to non–graph-based methods on the field. In this primer, we discuss recent developments of graph and hypergraph neural networks and highlight the potential of incorporating protein and metabolite data into the identification task to provide a new, more comprehensive, systems biology–based perspective on drug–drug interactions.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100551"},"PeriodicalIF":3.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantifying and managing uncertainty in systems biology: Mechanistic and data-driven models 量化和管理系统生物学中的不确定性：机械和数据驱动模型
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-09-05 DOI: 10.1016/j.coisb.2025.100557
Eva Balsa-Canto , Nùria Campo-Manzanares , Artai R. Moimenta , Geoffrey Roudaut , Diego Troitiño-Jordedo
{"title":"Quantifying and managing uncertainty in systems biology: Mechanistic and data-driven models","authors":"Eva Balsa-Canto ,&nbsp;Nùria Campo-Manzanares ,&nbsp;Artai R. Moimenta ,&nbsp;Geoffrey Roudaut ,&nbsp;Diego Troitiño-Jordedo","doi":"10.1016/j.coisb.2025.100557","DOIUrl":"10.1016/j.coisb.2025.100557","url":null,"abstract":"<div><div>Uncertainty poses a significant challenge to the reliability and interpretability of systems biology models. This review focuses on reducible epistemic uncertainty arising from incomplete data, measurement errors, or limited biological knowledge. We examine how this uncertainty affects both mechanistic models —such as dynamic kinetic and genome-scale metabolic models— and data-driven models, including neural networks trained on time-series data. Strategies for quantifying and mitigating uncertainty are reviewed, including profile likelihoods, Bayesian inference, ensemble modelling, optimal experimental design and active learning. Through illustrative case studies, we show how data limitations, model structure, and experimental design influence uncertainty propagation and model predictions. Finally, in our outlook, we highlight key research avenues to build more robust models, including hybrid frameworks combining mechanistic models with machine learning to improve interpretability and predictive performance, advances in inference methods and tools, or the definition of benchmarks to support reproducibility and method comparison.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100557"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial Board Page 编委会页面
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-12-11 DOI: 10.1016/S2452-3100(25)00029-0
{"title":"Editorial Board Page","authors":"","doi":"10.1016/S2452-3100(25)00029-0","DOIUrl":"10.1016/S2452-3100(25)00029-0","url":null,"abstract":"","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100569"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Structural identifiability of compartmental models: Recent progress and future directions 隔室模型的结构可识别性：最新进展和未来方向
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-09-24 DOI: 10.1016/j.coisb.2025.100559
Nicolette Meshkat , Anne Shiu
{"title":"Structural identifiability of compartmental models: Recent progress and future directions","authors":"Nicolette Meshkat ,&nbsp;Anne Shiu","doi":"10.1016/j.coisb.2025.100559","DOIUrl":"10.1016/j.coisb.2025.100559","url":null,"abstract":"<div><div>We summarize recent progress on the theory and applications of structural identifiability of compartmental models. On the applications side, we review identifiability analyses undertaken recently for models arising in epidemiology, oncology, and other areas; and we summarize common approaches for handling models that are unidentifiable. We also highlight recent theoretical and algorithmic results on how to reparametrize unidentifiable models and, in the context of linear compartmental models, how to predict identifiability properties directly from the model structure. Finally, we highlight future research directions.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100559"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial overview: ‘Identifiability, estimation, and uncertainty in mathematical modeling’ 编辑概述：“数学建模中的可识别性、估计和不确定性”
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-09-26 DOI: 10.1016/j.coisb.2025.100561
Alejandro F. Villaverde, Matthew J. Simpson
{"title":"Editorial overview: ‘Identifiability, estimation, and uncertainty in mathematical modeling’","authors":"Alejandro F. Villaverde,&nbsp;Matthew J. Simpson","doi":"10.1016/j.coisb.2025.100561","DOIUrl":"10.1016/j.coisb.2025.100561","url":null,"abstract":"","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100561"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On the different flavours of practical identifiability 关于实际可识别性的不同口味
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-08-19 DOI: 10.1016/j.coisb.2025.100556
Mio Heinrich , Rafael Arutjunjan , Jens Timmer
{"title":"On the different flavours of practical identifiability","authors":"Mio Heinrich ,&nbsp;Rafael Arutjunjan ,&nbsp;Jens Timmer","doi":"10.1016/j.coisb.2025.100556","DOIUrl":"10.1016/j.coisb.2025.100556","url":null,"abstract":"<div><div>Identifiability is fundamental to any parameter estimation process and plays a role in a wide range of scientific research disciplines. Structural identifiability is a well-defined and purely model-based property that can be analysed in the absence of experimentally measured data with various methods. In contrast, practical identifiability lacks a concise technical definition that is agreed upon, leading to conflicting assessments. We focus on the practical identifiability analysis of ordinary differential equation models in systems biology and point out the differences between definitions and their implications. We differentiate between classifications based on sensitivity and classifications based on confidence intervals. We advocate for precise wording in discussions of practical identifiability analysis results so that the employed method is clear from the terminology.</div><div>We propose that model parameters should be termed a priori or a posteriori sensitive if sensitivity-based methods are used and finitely identified if the assessment is based on confidence intervals.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100556"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Editorial Board Page 编委会页面
IF 3.4
Current Opinion in Systems Biology Pub Date : 2025-06-01 Epub Date: 2025-06-09 DOI: 10.1016/S2452-3100(25)00007-1
{"title":"Editorial Board Page","authors":"","doi":"10.1016/S2452-3100(25)00007-1","DOIUrl":"10.1016/S2452-3100(25)00007-1","url":null,"abstract":"","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"41 ","pages":"Article 100547"},"PeriodicalIF":3.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On structural and practical identifiability: Current status and update of results 关于结构和实际可识别性：现状和结果的更新
IF 3.4
Current Opinion in Systems Biology Pub Date : 2025-06-01 Epub Date: 2025-05-06 DOI: 10.1016/j.coisb.2025.100546
Mio Heinrich , Marcus Rosenblatt , Franz-Georg Wieland , Hans Stigter , Jens Timmer
{"title":"On structural and practical identifiability: Current status and update of results","authors":"Mio Heinrich ,&nbsp;Marcus Rosenblatt ,&nbsp;Franz-Georg Wieland ,&nbsp;Hans Stigter ,&nbsp;Jens Timmer","doi":"10.1016/j.coisb.2025.100546","DOIUrl":"10.1016/j.coisb.2025.100546","url":null,"abstract":"<div><div>Identifiability of parameters in dynamical systems is a fundamental concept of mathematical modelling in systems biology and systems medicine. Both the structurally inherent identifiability of parameters in models and the practical identifiability of parameters, which arises from insufficient available data, play crucial roles in the development of useful models.</div><div>Here, we provide an overview of recent developments in the field of structural identifiability analysis of models based on ordinary differential equations, emphasising its importance for accurate parameter estimation. We extend an existing benchmark study by comparing the methods for structural identifiability analysis with the recently developed <em>StrucID</em>, showing it to be a fast, efficient and intuitive algorithm. Furthermore, this review highlights the challenges in practical identifiability analysis and the need for benchmarking with real-world models using experimental data. The potential benefits of standardising documentation for benchmarking models with experimental data and practical non-identifiabilities are stressed.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"41 ","pages":"Article 100546"},"PeriodicalIF":3.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144114935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
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
请完成安全验证×
相关产品
×
本文献相关产品
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书