Current Opinion in Systems Biology最新文献

Editorial Board Page 编委会页面

IF 2.2

Current Opinion in Systems Biology Pub Date : 2026-03-01 Epub Date: 2026-03-23 DOI: 10.1016/S2452-3100(26)00011-9

引用次数: 0

Cell sorting models in tissue development 组织发育中的细胞分选模型

IF 2.2

Current Opinion in Systems Biology Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.1016/j.coisb.2026.100572

Martí Planasdemunt-Hospital , David Oriola

引用次数: 0

Think before you fit: Parameter identifiability, sensitivity and uncertainty in systems biology models 拟合前三思：系统生物学模型中的参数可识别性、敏感性和不确定性

IF 2.2

Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-11-04 DOI: 10.1016/j.coisb.2025.100563

Simon P. Preston , Richard D. Wilkinson , Richard H. Clayton , Mike J. Chappell , Gary R. Mirams

{"title":"Think before you fit: Parameter identifiability, sensitivity and uncertainty in systems biology models","authors":"Simon P. Preston , Richard D. Wilkinson , Richard H. Clayton , Mike J. Chappell , Gary R. Mirams","doi":"10.1016/j.coisb.2025.100563","DOIUrl":"10.1016/j.coisb.2025.100563","url":null,"abstract":"<div><div>Reliable inference and predictions from systems biology models require knowing whether parameters can be estimated from available data, and with what certainty. Identifiability analysis reveals whether parameters are learnable in principle (structural identifiability) and in practice (practical identifiability). We introduce the core ideas using linear models, highlighting how experimental design and output sensitivity shape identifiability. In nonlinear models, identifiability can vary with parameter values, motivating global and simulation-based approaches. We summarise computational methods for assessing identifiability, noting that weakly identifiable parameters can undermine predictions beyond the calibration dataset. Strategies to improve identifiability include measuring different outputs, refining model structure, and adding prior knowledge. Far from a technical afterthought, identifiability determines the limits of inference and prediction. Recognising and addressing it is essential for building models that are not only well-fitted to data, but also capable of delivering predictions with robust, quantifiable uncertainty.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100563"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614299","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 : 2025-12-01 Epub Date: 2025-07-10 DOI: 10.1016/j.coisb.2025.100554

Kevin Thurley, Jana Wolf

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 : 2025-12-01 Epub Date: 2025-08-18 DOI: 10.1016/j.coisb.2025.100555

Christopher E. Miles

引用次数: 0

Machine-learned summary statistics for Bayesian inference of systems biology–model parameters: Opportunities and challenges 系统生物学模型参数贝叶斯推理的机器学习汇总统计：机遇与挑战

IF 2.2

Current Opinion in Systems Biology Pub Date : 2025-12-01 Epub Date: 2025-10-03 DOI: 10.1016/j.coisb.2025.100560

Atiyeh Ahmadi , Lena Podina , Sebastian Höpfl , Brian Ingalls

{"title":"Machine-learned summary statistics for Bayesian inference of systems biology–model parameters: Opportunities and challenges","authors":"Atiyeh Ahmadi , Lena Podina , Sebastian Höpfl , Brian Ingalls","doi":"10.1016/j.coisb.2025.100560","DOIUrl":"10.1016/j.coisb.2025.100560","url":null,"abstract":"<div><div>Mechanistic systems biology models can capture complex dynamic interactions, but their accuracy often relies on parameter inference from high-dimensional, noisy data with corresponding intractable likelihoods. Approximate Bayesian computation (ABC) avoids likelihood evaluation by comparing simulated and observed data via low-dimensional summary statistics. However, effective selection of these summaries remains a limitation. Recent advances in machine learning offer algorithmic approaches to the selection of informative summaries, improving parameter identifiability, and reducing computational cost. Machine learning of summaries, however, introduces new challenges. We survey summary selection techniques for ABC, discuss how automated summaries can enhance parameter identifiability and inference efficiency, discuss algorithmic trade-offs in informativeness, tractability, and interpretability, and highlight strategies to ensure reliable inference. Through biological case studies, we review recently developed methods for selecting summaries. Finally, we outline challenges and future directions for leveraging machine-learned summaries to support ABC as a powerful and transparent tool for parameter inference in systems biology.</div></div>","PeriodicalId":37400,"journal":{"name":"Current Opinion in Systems Biology","volume":"42 ","pages":"Article 100560"},"PeriodicalIF":2.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362195","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 : 2025-12-01 Epub Date: 2025-07-12 DOI: 10.1016/j.coisb.2025.100552

Necibe Tuncer , Maia Martcheva , Stanca M. Ciupe

引用次数: 0

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 : 2025-12-01 Epub 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 , Dilan Pathirana , Daniel Weindl , 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":"2025-12-01","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

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

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 , 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