Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences最新文献_第2页

Cross-validation-based sequential design for stochastic models. 基于交叉验证的随机模型序贯设计。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0217

Louise M Kimpton, Michael Dunne, James M Salter, Peter Challenor

{"title":"Cross-validation-based sequential design for stochastic models.","authors":"Louise M Kimpton, Michael Dunne, James M Salter, Peter Challenor","doi":"10.1098/rsta.2024.0217","DOIUrl":"https://doi.org/10.1098/rsta.2024.0217","url":null,"abstract":"Complex numerical models are increasingly being used in healthcare and epidemiology. To represent the complex features, modellers often make the decision to include stochastic behaviour where repeated runs of the model with identical inputs produce different outputs. When computational constraints limit the number of model runs and replications, heteroscedastic Gaussian processes can be used as a fast surrogate, allowing for efficient emulation of varying noise levels across the input space. The accuracy of any emulator is greatly dependent on the design of the training data, where sequential design algorithms increase the number of design points iteratively based on predefined criteria. For stochastic models, the design problem is more challenging due to the possibility of replicates at design points. This article develops a new sequential design method for stochastic models which scales well in high-dimensional input spaces. We build upon an existing method for deterministic models using an expected squared leave-one-out error criterion that balances exploration and replication. We compare our approach with existing sequential design methods as well as applying it to an agent-based model and a COVID-19 model. Results demonstrate that the proposed method performs well in noisy environments, offering a scalable alternative to existing methods.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240217"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Practical parameter identifiability of respiratory mechanics in the extremely preterm infant. 极早产儿呼吸力学的实用参数可识别性。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0226

Richard Foster, Laura Ellwein Fix

{"title":"Practical parameter identifiability of respiratory mechanics in the extremely preterm infant.","authors":"Richard Foster, Laura Ellwein Fix","doi":"10.1098/rsta.2024.0226","DOIUrl":"https://doi.org/10.1098/rsta.2024.0226","url":null,"abstract":"The complexity of mathematical models describing respiratory mechanics has grown in recent years, however, parameter identifiability of such models has only been studied in the last decade in the context of observable data. This study investigates parameter identifiability of a nonlinear respiratory mechanics model tuned to the physiology of an extremely preterm infant, using global Morris screening, local deterministic sensitivity analysis and singular value decomposition-based subset selection. The model predicts airflow and dynamic pulmonary volumes and pressures under varying levels of continuous positive airway pressure, and a range of parameters characterizing both surfactant-treated and surfactant-deficient lung. Sensitivity analyses indicated 11 parameters influence model outputs over the range of continuous positive airway pressure and lung health scenarios. The model was adapted to data from a spontaneously breathing 1 kg infant using gradient-based optimization to estimate the parameter subset characterizing the patient's state of health.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240226"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation-based inference of the time-dependent reproduction number from temporally aggregated and under-reported disease incidence time series data. 基于模拟的从时间聚合和低报的疾病发病率时间序列数据中推断时间相关的复制数。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-04-02 DOI: 10.1098/rsta.2024.0412

Isaac Ogi-Gittins, Nicholas Steyn, Jonathan Polonsky, William S Hart, Mory Keita, Steve Ahuka-Mundeke, Edward M Hill, Robin N Thompson

{"title":"Simulation-based inference of the time-dependent reproduction number from temporally aggregated and under-reported disease incidence time series data.","authors":"Isaac Ogi-Gittins, Nicholas Steyn, Jonathan Polonsky, William S Hart, Mory Keita, Steve Ahuka-Mundeke, Edward M Hill, Robin N Thompson","doi":"10.1098/rsta.2024.0412","DOIUrl":"https://doi.org/10.1098/rsta.2024.0412","url":null,"abstract":"During infectious disease outbreaks, the time-dependent reproduction number ([Formula: see text]) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating [Formula: see text] from temporally aggregated disease incidence data (e.g. weekly case reports). While that approach is straightforward to use, it assumes implicitly that all cases are reported and the computation can be slow when applied to large datasets. In this article, we extend our previous approach and develop a computationally efficient simulation-based method for estimating [Formula: see text] in real-time accounting for both temporal aggregation of incidence data and under-reporting (with a fixed reporting probability per case). Using simulated data, we show that failing to consider stochastic under-reporting can lead to inappropriately precise estimates, including scenarios in which the true [Formula: see text] value lies outside inferred credible intervals more often than expected. We then apply our approach to data from the 2018 to 2020 Ebola outbreak in the Democratic Republic of the Congo (DRC), again exploring the effects of case under-reporting. Finally, we show how our method can be extended to account for temporal variations in reporting. Given information about the level of case reporting, our framework can be used to estimate [Formula: see text] during future outbreaks with under-reported and temporally aggregated case data.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2293","pages":"20240412"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing experimental designs for model selection of ion channel drug-binding mechanisms. 离子通道药物结合机制模型选择的优化实验设计。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0227

Frankie Patten-Elliott, Chon Lok Lei, Simon P Preston, Richard D Wilkinson, Gary R Mirams

{"title":"Optimizing experimental designs for model selection of ion channel drug-binding mechanisms.","authors":"Frankie Patten-Elliott, Chon Lok Lei, Simon P Preston, Richard D Wilkinson, Gary R Mirams","doi":"10.1098/rsta.2024.0227","DOIUrl":"10.1098/rsta.2024.0227","url":null,"abstract":"The rapid delayed rectifier current carried by the human Ether-à-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction, which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug compound binds to hERG can help reduce uncertainty when quantifying pro-arrhythmic risk. In this study, we introduce a methodology for optimizing experimental voltage protocols to produce data that enable different proposed models for the drug-binding mechanism to be distinguished. We demonstrate the performance of this methodology via a synthetic data study. If the underlying model of hERG current is known exactly, then the optimized protocols generated show noticeable improvements in our ability to select the true model when compared with a simple protocol used in previous studies. However, if the model is not known exactly, and we assume a discrepancy between the data-generating hERG model and the hERG model used in fitting the models, then the optimized protocols become less effective in determining the 'true' binding dynamics. While the introduced methodology shows promise, we must be careful to ensure that, if applied to a real data study, we have a well-calibrated model of hERG current gating.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240227"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Windkessel calibration using optimized zero-dimensional surrogate models. 使用优化的零维代理模型的贝叶斯Windkessel校准。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0223

Jakob Richter, Jonas Nitzler, Luca Pegolotti, Karthik Menon, Jonas Biehler, Wolfgang A Wall, Daniele E Schiavazzi, Alison L Marsden, Martin R Pfaller

{"title":"Bayesian Windkessel calibration using optimized zero-dimensional surrogate models.","authors":"Jakob Richter, Jonas Nitzler, Luca Pegolotti, Karthik Menon, Jonas Biehler, Wolfgang A Wall, Daniele E Schiavazzi, Alison L Marsden, Martin R Pfaller","doi":"10.1098/rsta.2024.0223","DOIUrl":"https://doi.org/10.1098/rsta.2024.0223","url":null,"abstract":"Bayesian boundary condition (BC) calibration approaches from clinical measurements have successfully quantified inherent uncertainties in cardiovascular fluid dynamics simulations. However, estimating the posterior distribution for all BC parameters in three-dimensional (3D) simulations has been unattainable due to infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors: We only evaluate the 3D model once for an initial choice of BCs and use the result to create a highly accurate zero-dimensional (0D) surrogate. We then perform Sequential Monte Carlo (SMC) using the optimized 0D model to derive the high-dimensional Windkessel BC posterior distribution. Optimizing 0D models to match 3D data a priori lowered their median approximation error by nearly one order of magnitude in 72 publicly available vascular models. The optimized 0D models generalized well to a wide range of BCs. Using SMC, we evaluated the high-dimensional Windkessel parameter posterior for different measured signal-to-noise ratios in a vascular model, which we validated against a 3D posterior. The minimal computational demand of our method using a single 3D simulation, combined with the open-source nature of all software and data used in this work, will increase access and efficiency of Bayesian Windkessel calibration in cardiovascular fluid dynamics simulations.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240223"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A renewal-equation approach to estimating R_t and infectious disease case counts in the presence of reporting delays. 在报告延迟的情况下估计Rt和传染病病例数的更新方程方法。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0357

Sumali Bajaj, Robin Thompson, Ben Lambert

{"title":"A renewal-equation approach to estimating Rt and infectious disease case counts in the presence of reporting delays.","authors":"Sumali Bajaj, Robin Thompson, Ben Lambert","doi":"10.1098/rsta.2024.0357","DOIUrl":"10.1098/rsta.2024.0357","url":null,"abstract":"During infectious disease outbreaks, delays in case reporting mean that the time series of cases is unreliable, particularly for those cases occurring most recently. This means that real-time estimates of the time-varying reproduction number, [Formula: see text], are often made using a time series of cases only up until a time period sufficiently far in the past that there is some confidence in the case counts. This means that the most recent [Formula: see text] estimates are usually out of date, inducing lags in the response of public health authorities. Here, we introduce an [Formula: see text] estimation method, which makes use of the retrospective updates to case time series which happen as more cases that occurred historically enter the health system; these data encode within them information about the reporting delays, which our method also estimates. These estimates, in turn, allow us to estimate the true count of cases occurring most recently allowing up-to-date estimates of [Formula: see text]. Our method simultaneously estimates the reporting delays, true historical case counts and [Formula: see text] in a single Bayesian framework, allowing the uncertainty in each of these quantities to be accounted for. We apply our method to both simulated and real outbreak data, which shows that the method substantially improves upon naive estimates of [Formula: see text] which do not account for reporting delays. Our method is available in an open-source fully tested R package, incidenceinflation. Our research highlights the value of keeping historical time series of cases since changes to these data can help to characterize nuisance processes, such as reporting delays, which allow these to be accounted for when estimating key epidemic quantities.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240357"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emulating computer models with high-dimensional count output. 模拟具有高维计数输出的计算机模型。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0216

James M Salter, Trevelyan J McKinley, Xiaoyu Xiong, Daniel B Williamson

{"title":"Emulating computer models with high-dimensional count output.","authors":"James M Salter, Trevelyan J McKinley, Xiaoyu Xiong, Daniel B Williamson","doi":"10.1098/rsta.2024.0216","DOIUrl":"10.1098/rsta.2024.0216","url":null,"abstract":"Computer models are used to study the real world, and often contain a large number of uncertain input parameters, produce a large number of outputs, may be expensive to run and need calibrating to real-world observations to be useful for decision-making. Emulators are often used as cheap surrogates for the expensive simulator, trained on a small number of simulations to provide predictions with uncertainty at unseen inputs. In epidemiological applications, for example compartmental or agent-based models for modelling the spread of infectious diseases, the output is usually spatially and temporally indexed, stochastic and consists of counts rather than continuous variables. Here, we consider emulating high-dimensional count output from a complex computer model using a Poisson lognormal PCA (PLNPCA) emulator. We apply the PLNPCA emulator to output fields from a COVID-19 model for England and Wales and compare this to fitting emulators to aggregations of the full output. We show that performance is generally comparable, while the PLNPCA emulator inherits desirable properties, including allowing the full output to be predicted while capturing correlations between outputs, providing high-dimensional samples of counts that are representative of the true model output.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240216"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges and opportunities in uncertainty quantification for healthcare and biological systems. 医疗保健和生物系统不确定性量化的挑战和机遇。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0232

Louise M Kimpton, L Mihaela Paun, Mitchel J Colebank, Victoria Volodina

{"title":"Challenges and opportunities in uncertainty quantification for healthcare and biological systems.","authors":"Louise M Kimpton, L Mihaela Paun, Mitchel J Colebank, Victoria Volodina","doi":"10.1098/rsta.2024.0232","DOIUrl":"10.1098/rsta.2024.0232","url":null,"abstract":"Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science and aerospace engineering, incorporate UQ in the development and translation of new technologies. In contrast, the application of UQ to biological and healthcare models is understudied and suffers from several critical knowledge gaps. In an era of personalized medicine, patient-specific modelling, and digital twins, a lack of UQ understanding and appropriate implementation of UQ methodology limits the success of modelling and simulation in a clinical setting. The main contribution of our review article is to emphasize the importance and current deficiencies of UQ in the development of computational frameworks for healthcare and biological systems. As the introduction to the special issue on this topic, we provide an overview of UQ methodologies, their applications in non-biological and biological systems and the current gaps and opportunities for UQ development, as later highlighted by authors publishing in the special issue.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240232"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparison of Gaussian processes and polynomial chaos emulators in the context of haemodynamic pulse-wave propagation modelling. 血流动力学脉冲波传播建模中高斯过程与多项式混沌仿真器的比较。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0222

L Mihaela Paun, Mitchel J Colebank, Dirk Husmeier

{"title":"A comparison of Gaussian processes and polynomial chaos emulators in the context of haemodynamic pulse-wave propagation modelling.","authors":"L Mihaela Paun, Mitchel J Colebank, Dirk Husmeier","doi":"10.1098/rsta.2024.0222","DOIUrl":"10.1098/rsta.2024.0222","url":null,"abstract":"Computational modelling of the cardiovascular system is a promising future direction for patient-specific healthcare. However, the computational cost of these simulators is a bottleneck for their practical use in clinic for real-time digital twins. Emulation can overcome this, yet an extensive investigation into cardiovascular emulators is warranted. In this study, we emulate two one-dimensional haemodynamics models of the pulmonary circulation and compare two common emulation strategies: Gaussian processes (GPs) and polynomial chaos expansions (PCEs). We start by reducing the parameter space of the models through global sensitivity analysis, and then compare both emulation strategies using a multivariate, time-series output quantity of interest and a reduced representation using principal component analysis. We compare the emulators in both forward emulation on test data, as well as in their ability to infer parameters in the inverse problem. Our results indicate that GPs slightly outperform PCEs consistently across every comparison, and that a similar performance is obtained for the emulators of the time-dependent output and reduced output.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240222"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian inference informed by parameter subset selection for a minimal PBPK brain model. 基于参数子集选择的最小PBPK脑模型贝叶斯推理。

IF 4.3 3区综合性期刊

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Pub Date : 2025-03-13 DOI: 10.1098/rsta.2024.0219

Kamala Dadashova, Ralph C Smith, Mansoor A Haider, Brian J Reich

{"title":"Bayesian inference informed by parameter subset selection for a minimal PBPK brain model.","authors":"Kamala Dadashova, Ralph C Smith, Mansoor A Haider, Brian J Reich","doi":"10.1098/rsta.2024.0219","DOIUrl":"10.1098/rsta.2024.0219","url":null,"abstract":"Physiologically based pharmacokinetic (PBPK) models use a mechanistic approach to delineate the processes of the absorption, distribution, metabolism and excretion of biological substances in various species. These models generally comprise coupled systems of ordinary differential equations involving multiple states and a moderate to a large number of parameters. Such models contain compartments corresponding to various organs or tissues in the body. Before employing the models for treatment, the quantification of uncertainties for the parameters, based on a priori information or data for a specific response, is necessary. This requires the determination of identifiable parameters, which are uniquely determined by data, and uncertainty analysis based on frequentist or Bayesian inference. We introduce a strategy to integrate parameter subset selection, based on identifiability analysis, with Bayesian inference. This approach further refines the subset of identifiable parameters, quantifies parameter and response uncertainties, enhances model prediction and reduces computational cost.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.","PeriodicalId":19879,"journal":{"name":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","volume":"383 2292","pages":"20240219"},"PeriodicalIF":4.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143616731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0