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Current Opinion in Systems Biology最新文献

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Identifiability and uncertainty for ordinary differential equation models with qualitative or semiquantitative data 具有定性或半定量数据的常微分方程模型的可辨识性和不确定性
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-09-08 DOI: 10.1016/j.coisb.2025.100558
Domagoj Dorešić , Dilan Pathirana , Daniel Weindl , Jan Hasenauer
{"title":"Identifiability and uncertainty for ordinary differential equation models with qualitative or semiquantitative data","authors":"Domagoj Dorešić ,&nbsp;Dilan Pathirana ,&nbsp;Daniel Weindl ,&nbsp;Jan Hasenauer","doi":"10.1016/j.coisb.2025.100558","DOIUrl":"10.1016/j.coisb.2025.100558","url":null,"abstract":"<div><div>The estimation of unknown parameters is a key step in the development of mechanistic dynamical models for biological processes. While quantitative measurements are typically used for model calibration, in many applications, only semiquantitative or qualitative observations are available, posing unique challenges for parameter estimation.</div><div>Specialized approaches have been developed to integrate such data, offering trade-offs in bias, flexibility, and computational efficiency. Most of these approaches involve a recording function that maps the quantitative model onto nonabsolute data; however, this introduces additional degrees of freedom that can contribute to non-identifiability. Reliable calibration therefore requires structural and practical identifiability analysis, alongside robust uncertainty quantification.</div><div>In this work, we provide an overview of available methods, critically examine them with respect to identifiability and uncertainty considerations, identify methodological gaps, outline strategies to improve computational efficiency, and advocate for the development of standardized benchmarking frameworks to support informed method selection and best practices.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100558"},"PeriodicalIF":2.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157550","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 : 2025-09-05 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":"2025-09-05","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
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 : 2025-08-19 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":"2025-08-19","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
Mechanistic inference of stochastic gene expression from structured single-cell data 从结构化单细胞数据推断随机基因表达的机制
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-08-18 DOI: 10.1016/j.coisb.2025.100555
Christopher E. Miles
{"title":"Mechanistic inference of stochastic gene expression from structured single-cell data","authors":"Christopher E. Miles","doi":"10.1016/j.coisb.2025.100555","DOIUrl":"10.1016/j.coisb.2025.100555","url":null,"abstract":"<div><div>Single-cell gene expression measurements encode variability spanning molecular noise, cell-to-cell heterogeneity, and technical artifacts. Mechanistic stochastic models provide powerful approaches to disentangle these sources, yet inferring underlying dynamics from standard snapshot sequencing data faces fundamental identifiability limitations. This review focuses on how structured datasets with temporal, spatial, or multimodal features offer constraints to resolve these ambiguities, but they demand more sophisticated models and inference strategies, including machine-learning techniques with inherent tradeoffs. We highlight recent progress in the judicious integration of structured single-cell data, stochastic model development, and innovative inference strategies to extract predictive, gene-level insights. These advances lay the foundation for scaling mechanistic inference upward to regulatory networks and multicellular tissues.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100555"},"PeriodicalIF":2.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098899","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 and practical identifiability of within-host models of virus dynamics—A review 宿主内病毒动力学模型的结构和实际可识别性综述
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-07-12 DOI: 10.1016/j.coisb.2025.100552
Necibe Tuncer , Maia Martcheva , Stanca M. Ciupe
{"title":"Structural and practical identifiability of within-host models of virus dynamics—A review","authors":"Necibe Tuncer ,&nbsp;Maia Martcheva ,&nbsp;Stanca M. Ciupe","doi":"10.1016/j.coisb.2025.100552","DOIUrl":"10.1016/j.coisb.2025.100552","url":null,"abstract":"<div><div>Within-host mechanistic mathematical models of virus dynamics described by ordinary differential equations are most useful when linked to empirical data. The main challenge in estimating parameters from typically available, noisy data arises from the intrinsic parameter correlations induced by model structure. As a result, the optimization problem, which fits parameters by minimizing the distance between the model and the data, may admit infinitely many solutions. These challenges can be elucidated through the study of structural and practical identifiability of the proposed model. In this article, we review existing methods for the structural and practical identifiability of the basic within-host model of viral dynamics and provide guidelines for improving unidentifiability. We discuss the challenges and new developments in extending these techniques to nonordinary within-host differential equation models (delay, partial, and stochastic) and stress the importance of using practical identifiability results to guide optimal experimental design.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100552"},"PeriodicalIF":2.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756836","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: Mathematical modeling of disease processes 编辑概述：疾病过程的数学建模
IF 2.2
Current Opinion in Systems Biology Pub Date : 2025-07-10 DOI: 10.1016/j.coisb.2025.100554
Kevin Thurley, Jana Wolf
{"title":"Editorial overview: Mathematical modeling of disease processes","authors":"Kevin Thurley,&nbsp;Jana Wolf","doi":"10.1016/j.coisb.2025.100554","DOIUrl":"10.1016/j.coisb.2025.100554","url":null,"abstract":"","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100554"},"PeriodicalIF":2.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756848","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 : 2025-07-08 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":"2025-07-08","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 : 2025-06-18 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":"2025-06-18","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
Editorial Board Page 编委会页面
IF 3.4
Current Opinion in Systems Biology Pub Date : 2025-06-01 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 : 2025-05-06 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":"2025-05-06","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
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