贝叶斯时间序列数据分析（BayModTS）——一种用于处理稀疏和高度变化数据的 FAIR 工作流程。

Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data.

机构信息

Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany.

Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany.

出版信息

Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae312.

DOI:10.1093/bioinformatics/btae312

PMID:38741151

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11128094/

Abstract

MOTIVATION

Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties.

RESULTS

We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection.

AVAILABILITY AND IMPLEMENTATION

The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.

摘要

动机

系统生物学旨在通过对实验和临床数据的数学建模来更好地理解生命系统。在定量动力学建模中，普遍存在的挑战是整合时间序列测量，这些测量通常具有高度可变性和低采样分辨率。需要采用一些方法来利用这些信息，同时始终处理不确定性。

结果

我们提出了 BayModTS（时间序列数据的贝叶斯建模），这是一种新的 FAIR（可发现、可访问、可互操作和可重复使用）工作流程，用于处理和分析稀疏且高度可变的时间序列数据。BayModTS 始终将不确定性从数据传递到模型预测，包括通过参数化模型传递过程知识。此外，通过过滤噪声，可以识别不同条件下动力学的可信差异。为了展示 BayModTS 的强大功能和多功能性，我们将其应用于三个来自三个不同物种和不同测量技术的肝数据集：（i）门静脉结扎后大鼠肝脏磁共振成像的血液灌注测量，（ii）正常和脂肪变性小鼠中不同药物的药代动力学时间序列，以及（iii）临床肝切除后基于 CT 的人类肝残余体积评估。

可用性和实现

BayModTS 代码库可在 GitHub 上获得，网址为 https://github.com/Systems-Theory-in-Systems-Biology/BayModTS。该存储库包含一个用于可执行 BayModTS 工作流程的 Python 脚本和一个广泛适用的延迟瞬态函数的 SBML（系统生物学标记语言）模型。此外，本文中的所有示例都包含在存储库中。应用示例的数据和代码存储在 DaRUS 上：https://doi.org/10.18419/darus-3876。原始 MRI ROI 体素数据已上传到 DaRUS：https://doi.org/10.18419/darus-3878。脂肪肝代谢物数据发布在 FairdomHub 上：10.15490/fairdomhub.1.study.1070.1。