Current Opinion in Systems Biology最新文献

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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

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Current Opinion in Systems Biology

Pub Date : 2025-12-01 DOI: 10.1016/j.coisb.2025.100563

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

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.

系统生物学模型的可靠推断和预测需要知道是否可以从现有数据中估计参数，以及有多大的确定性。可识别性分析揭示了参数在原则上（结构可识别性）和在实践中（实际可识别性）是否可学习。我们介绍了使用线性模型的核心思想，强调了实验设计和输出灵敏度如何塑造可识别性。在非线性模型中，可辨识性可以随参数值而变化，这激发了全局和基于仿真的方法。我们总结了评估可识别性的计算方法，注意到弱可识别参数可能会破坏校准数据集之外的预测。提高可识别性的策略包括度量不同的输出、改进模型结构和增加先验知识。可识别性决定了推断和预测的极限，而不是技术上的事后考虑。认识到并解决这一问题，对于构建不仅能够很好地适应数据，而且能够提供具有强大的、可量化的不确定性的预测的模型至关重要。

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Editorial Board Page 编委会页面

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Current Opinion in Systems Biology

Pub Date : 2025-12-01 DOI: 10.1016/S2452-3100(25)00029-0

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Systems virology at scale 大规模的系统病毒学

IF 2.2 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-10-13 DOI: 10.1016/j.coisb.2025.100562

Cameron D. Griffiths , Andrew J. Sweatt , Kevin A. Janes

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.

今天的亚细胞和多细胞感染模型正准备解决关于病毒与宿主相互作用和易感性决定因素的更大问题。这一机会来自于增强的计算能力、改进的模型架构以及从受病毒感染的主机收集的全面数据集。在这里，我们总结了病毒建模和数据科学的最新进展，这些进展说明了系统模型如何成功地穿越了不断增加的时间长度尺度、细节水平和生物背景范围。最新的进展是令人鼓舞的，但是最近的发现只是触及了表面，考虑到有多少不同的病毒存在或将来可能出现——努力的规模应该与挑战的规模保持一致。系统病毒学对分子和细胞网络的抽象补充了广泛应用于人群的病毒传播公共卫生模型。

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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

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Current Opinion in Systems Biology

Pub Date : 2025-10-03 DOI: 10.1016/j.coisb.2025.100560

Atiyeh Ahmadi , Lena Podina , Sebastian Höpfl , Brian Ingalls

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.

机械系统生物学模型可以捕获复杂的动态相互作用，但其准确性往往依赖于具有相应难处理似然的高维噪声数据的参数推断。近似贝叶斯计算（ABC）通过低维汇总统计来比较模拟数据和观测数据，从而避免了似然评估。然而，有效地选择这些摘要仍然是一个限制。机器学习的最新进展为选择信息摘要、提高参数可识别性和降低计算成本提供了算法方法。然而，摘要的机器学习带来了新的挑战。我们调查了ABC的摘要选择技术，讨论了自动摘要如何提高参数可识别性和推理效率，讨论了算法在信息性、可追溯性和可解释性方面的权衡，并强调了确保可靠推理的策略。通过生物学案例研究，我们回顾了最近发展的选择摘要的方法。最后，我们概述了利用机器学习摘要来支持ABC作为系统生物学中参数推断的强大透明工具的挑战和未来方向。

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-09-26 DOI: 10.1016/j.coisb.2025.100561

Alejandro F. Villaverde, Matthew J. Simpson

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-09-24 DOI: 10.1016/j.coisb.2025.100559

Nicolette Meshkat , Anne Shiu

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.

本文综述了隔室模型结构可识别性理论和应用方面的最新进展。在应用方面，我们回顾了最近对流行病学、肿瘤学和其他领域的模型进行的可识别性分析；我们总结了处理不可识别模型的常用方法。我们还强调了最近关于如何重新参数化不可识别模型的理论和算法结果，以及在线性隔室模型的背景下，如何直接从模型结构预测可识别属性。最后，对未来的研究方向进行了展望。

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

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

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.

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.

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.

未知参数的估计是建立生物过程动力学模型的关键步骤。虽然定量测量通常用于模型校准，但在许多应用中，只能获得半定量或定性观测，这对参数估计提出了独特的挑战。已经开发出专门的方法来集成这些数据，在偏差、灵活性和计算效率方面提供折衷。这些方法大多涉及一个记录功能，将定量模型映射到非绝对数据；然而，这引入了额外的自由度，可能导致不可识别性。因此，可靠的校准需要结构和实际的可识别性分析，以及稳健的不确定度量化。在这项工作中，我们概述了可用方法，根据可识别性和不确定性因素对其进行批判性检查，确定方法差距，概述提高计算效率的策略，并倡导开发标准化基准框架，以支持明智的方法选择和最佳实践。

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

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

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.

不确定性对系统生物学模型的可靠性和可解释性提出了重大挑战。这篇综述的重点是由不完整的数据、测量误差或有限的生物学知识引起的可简化的认知不确定性。我们研究了这种不确定性如何影响机制模型（如动态动力学和基因组尺度代谢模型）和数据驱动模型（包括在时间序列数据上训练的神经网络）。本文回顾了量化和减轻不确定性的策略，包括轮廓似然、贝叶斯推理、集成建模、最优实验设计和主动学习。通过说明性案例研究，我们展示了数据限制、模型结构和实验设计如何影响不确定性传播和模型预测。最后，在我们的展望中，我们强调了构建更健壮模型的关键研究途径，包括将机制模型与机器学习相结合的混合框架，以提高可解释性和预测性能，推理方法和工具的进步，或者定义基准以支持可重复性和方法比较。

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-08-19 DOI: 10.1016/j.coisb.2025.100556

Mio Heinrich , Rafael Arutjunjan , Jens Timmer

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.

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.

可辨识性是任何参数估计过程的基础，在广泛的科学研究学科中起着重要作用。结构可识别性是一种定义良好的纯粹基于模型的属性，可以在没有实验测量数据的情况下用各种方法进行分析。相比之下，实际可识别性缺乏商定的简明技术定义，导致相互矛盾的评估。我们着重于系统生物学中常微分方程模型的实际可辨识性分析，并指出定义之间的差异及其含义。我们区分基于灵敏度的分类和基于置信区间的分类。我们提倡在讨论实际的可识别性分析结果时措辞准确，以便所采用的方法从术语上清楚。我们建议，如果使用基于灵敏度的方法，模型参数应称为先验或后验敏感，如果评估基于置信区间，则模型参数应被有限识别。

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-08-18 DOI: 10.1016/j.coisb.2025.100555

Christopher E. Miles

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.

单细胞基因表达测量编码可变性跨越分子噪声、细胞间异质性和技术伪影。机械随机模型提供了强大的方法来解开这些来源，但从标准快照测序数据推断潜在的动态面临基本的可识别性限制。这篇综述的重点是具有时间、空间或多模态特征的结构化数据集如何提供约束来解决这些模糊性，但它们需要更复杂的模型和推理策略，包括具有固有权衡的机器学习技术。我们强调了最近在结构化单细胞数据、随机模型开发和创新推理策略的明智整合方面取得的进展，以提取预测性的基因水平的见解。这些进展为向上扩展机制推理到调节网络和多细胞组织奠定了基础。

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