Current Opinion in Systems Biology最新文献

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-07-12 DOI: 10.1016/j.coisb.2025.100552

Necibe Tuncer , Maia Martcheva , Stanca M. Ciupe

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.

用常微分方程描述的宿主内病毒动力学机制数学模型在与经验数据联系起来时最有用。从典型可用的噪声数据中估计参数的主要挑战来自模型结构引起的内在参数相关性。因此，通过最小化模型与数据之间的距离来拟合参数的优化问题可能有无限多个解。这些挑战可以通过研究所提出的模型的结构和实际可识别性来阐明。在本文中，我们回顾了现有的病毒动力学基本宿主内模型的结构和实际可识别性的方法，并提供了改进不可识别性的指导方针。我们讨论了将这些技术扩展到非常主机内微分方程模型（延迟，部分和随机）的挑战和新发展，并强调了使用实际可识别性结果来指导最佳实验设计的重要性。

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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-07-10 DOI: 10.1016/j.coisb.2025.100554

Kevin Thurley, Jana Wolf

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-07-08 DOI: 10.1016/j.coisb.2025.100553

Julio R. Banga , Alejandro F. Villaverde

Mathematical modelling is one of the pillars of systems biology. In this review, we focus on models that are mechanistic, i.e., they explain the mechanism by which a phenomenon takes place, and dynamic, 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.

数学建模是系统生物学的支柱之一。在这篇综述中，我们关注的是机械性的模型，即，它们解释了现象发生的机制，以及动态的模型，即，它们由模拟系统时间过程的微分方程组成。我们的目标是提供系统生物学中机械动态建模技术的最新状态。这些基于第一性原理的模型对于了解复杂的生理过程至关重要。它们可用于测试假设，预测系统行为，探索和优化干预策略。由于生物过程通常是非线性的、多尺度的，并且受到各种不确定性的影响，因此建立和分析稳健可靠的机制模型的任务充满了困难。在本文中，我们概述了模型发现和结构选择、可识别性分析、参数估计、不确定性量化和模型可靠性等关键主题的最新发展。我们讨论了这些领域的挑战和悬而未决的问题，并概述了未来工作的前景。

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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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

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

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.

在应用前识别相互作用的药物是必要的，以减轻药物-药物相互作用对患者健康所代表的严重风险。基于机器学习的方法越来越被监管机构认可为在药物开发中发挥核心作用的工具，包括识别新的相互作用。近年来，与非基于图的方法相比，图和超图神经网络在该领域提供了有希望的性能改进。在这篇入门文章中，我们讨论了图和超图神经网络的最新发展，并强调了将蛋白质和代谢物数据纳入识别任务的潜力，以提供一个新的、更全面的、基于系统生物学的药物-药物相互作用的视角。

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Editorial Board Page 编委会页面

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-06-01 DOI: 10.1016/S2452-3100(25)00007-1

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 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

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

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.

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 StrucID, 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.

动态系统参数的可辨识性是系统生物学和系统医学中数学建模的一个基本概念。模型中参数的结构固有可识别性和由于可用数据不足而产生的参数的实际可识别性在开发有用的模型中起着至关重要的作用。在这里，我们概述了基于常微分方程的模型结构可识别性分析领域的最新发展，强调了其对准确参数估计的重要性。通过将现有的结构可识别性分析方法与最近开发的结构可识别性分析方法进行比较，我们扩展了已有的基准研究，表明它是一种快速、高效和直观的算法。此外，本综述强调了实际可识别性分析中的挑战，以及使用实验数据与现实世界模型进行基准测试的必要性。强调了对具有实验数据和实际不可识别性的基准模型进行标准化文档的潜在好处。

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引用次数: 0

Editorial Board Page 编委会页面

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-03-01 DOI: 10.1016/S2452-3100(25)00002-2

引用次数: 0

From shallow to deep: The evolution of machine learning and mechanistic model integration in cancer research 从浅到深：癌症研究中机器学习和机制模型集成的演变

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-03-01 DOI: 10.1016/j.coisb.2025.100541

Yunduo Lan , Sung-Young Shin , Lan K. Nguyen

This review explores the integration of machine learning (ML) and mechanistic modelling to address challenges in computational biology, particularly in cancer research. While ML excels in processing large datasets and identifying complex, nonlinear relationships, mechanistic models provide causal insights grounded in biological principles. We classify the integration into shallow and deep categories. Shallow integration methods—such as sensitivity analysis, surrogate modelling, and data augmentation—have demonstrated improved computational efficiency and prediction accuracy. Deep integration goes further by embedding biological mechanisms directly into ML models, enhancing both explainability and performance in biological systems. Applications across cancer signalling, pharmacokinetics, and cell signalling illustrate the effectiveness of these integrated strategies. However, challenges including computational scalability and data quality must be addressed to fully realize their potential. We highlight key advancements in the integration of ML and mechanistic models and suggest that their continued evolution will drive future innovations in computational biology and systems medicine.

这篇综述探讨了机器学习（ML）和机械建模的集成，以解决计算生物学中的挑战，特别是在癌症研究中。虽然机器学习擅长处理大型数据集和识别复杂的非线性关系，但机制模型提供了基于生物学原理的因果见解。我们将集成分为浅类和深类。浅层整合方法——如敏感性分析、代理建模和数据增强——已经证明了计算效率和预测精度的提高。通过将生物机制直接嵌入到机器学习模型中，深度集成进一步增强了生物系统的可解释性和性能。在癌症信号传导、药代动力学和细胞信号传导中的应用说明了这些综合策略的有效性。然而，必须解决包括计算可伸缩性和数据质量在内的挑战，以充分实现其潜力。我们强调了机器学习和机械模型集成方面的关键进展，并建议它们的持续发展将推动计算生物学和系统医学的未来创新。

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引用次数: 0

Leveraging mathematical models to improve the statistical robustness of cancer immunotherapy trials 利用数学模型提高癌症免疫治疗试验的统计稳健性

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-01-11 DOI: 10.1016/j.coisb.2024.100540

Jeroen H.A. Creemers , Johannes Textor

Cancer immunotherapy is an important application area for mathematical modeling. Current modeling studies have a range of ambitious goals from dose optimization to creating “digital twins” of individual cancer patients for treatment response prediction. Here we focus on a humbler, but nonetheless important, goal: aiding with the planning and design of clinical trials. Cancer immunotherapy trials can be hard to design due to heterogeneous and time-varying treatment effects. While clinical statisticians already use computer simulations, these rarely integrate explicit pathophysiological mechanisms, such as cancer-immune interactions, to specifically adapt the design to the treatment. Encouraged by rapid progress in mathematical modeling, we here propose an “in-silico-first” approach–already common in industry–where doctors, statisticians, and modelers build knowledge-based mathematical models to examine and refine the statistical design of clinical trials. Ultimately, we hope that this collaborative effort will lead to more robust designs of future clinical trials, resulting in improved success rates.

肿瘤免疫治疗是数学建模的一个重要应用领域。目前的建模研究有一系列雄心勃勃的目标，从剂量优化到为个体癌症患者创建“数字双胞胎”以预测治疗反应。在这里，我们关注的是一个更不起眼但却很重要的目标：帮助临床试验的规划和设计。由于治疗效果的异质性和时变性，癌症免疫治疗试验很难设计。虽然临床统计学家已经使用计算机模拟，但这些模拟很少整合明确的病理生理机制，如癌症免疫相互作用，以专门适应治疗的设计。受数学建模快速发展的鼓舞，我们在此提出了一种“硅芯片优先”的方法——在工业界已经很常见——医生、统计学家和建模者建立基于知识的数学模型来检查和完善临床试验的统计设计。最终，我们希望这种合作的努力将导致未来临床试验更稳健的设计，从而提高成功率。

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引用次数: 0

Calcium-mediated mitochondrial energy deficiency in Parkinson's and Alzheimer's diseases: Insights from computational modelling 帕金森病和阿尔茨海默病中钙介导的线粒体能量缺乏：来自计算模型的见解

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Current Opinion in Systems Biology

Pub Date : 2025-01-10 DOI: 10.1016/j.coisb.2024.100539

Valérie Voorsluijs , Alexander Skupin

Alzheimer's and Parkinson's diseases are the most prevalent neurodegenerative disorders worldwide and are characterised by progressive cognitive and functional impairments caused by neuronal loss. Energy deficiency is a predominant hallmark of their pathophysiology and plays a central role in the development of the disease, notably by mitochondrial dysfunction enhancing protein aggregation and oxidative stress which trigger subsequently immune responses and neuronal loss. Quantifying this energetic deficiency and identifying specific causative mechanisms from the complex network of interacting metabolic and regulatory pathways at play is rather challenging, where integrative mathematical modelling represents a powerful tool to support these investigations. Here, we review the latest developments in integrative modelling in brain bioenergetics in relation to Alzheimer's and Parkinson's diseases where we focus on the regulatory role of Ca²⁺ signalling. Finally, we discuss recent challenges and future directions to improve the current understanding of the energy-deficiency theory of neurodegeneration.

阿尔茨海默病和帕金森病是世界上最常见的神经退行性疾病，其特征是由神经元丧失引起的进行性认知和功能障碍。能量缺乏是其病理生理学的主要特征，在疾病的发展中起着核心作用，特别是通过线粒体功能障碍增强蛋白质聚集和氧化应激，从而引发随后的免疫反应和神经元损失。量化这种能量不足，并从相互作用的代谢和调节途径的复杂网络中确定特定的致病机制是相当具有挑战性的，其中综合数学模型代表了支持这些研究的强大工具。在这里，我们回顾了与阿尔茨海默病和帕金森病相关的脑生物能量学综合建模的最新进展，其中我们关注Ca2+信号的调节作用。最后，我们讨论了最近的挑战和未来的方向，以提高目前对神经变性的能量缺乏理论的理解。

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