Bayesian Validation of Dynamic Systems for Biological Networks.

IF 1.6 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Donghui Son, Jaejik Kim
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引用次数: 0

Abstract

Dynamic systems encompass a broad class of mathematical models used to describe the behavior of complex networks or systems over time. One of the most common approaches to modeling such dynamics is through a set of ordinary differential equations (ODEs), typically constructed based on hypotheses, known interactions, or observed trajectories. However, ODEs are deterministic and inflexible, while biological data are typically noisy. Thus, the model fit might not account for all possible data variations, and there might be a discrepancy between the actual biological process and the assumed model. This discrepancy could lead to inaccuracies in the prediction and interpretation of the biological networks. Therefore, it is required to validate ODE models in terms of observed data. Given that biological networks typically involve multiple sources of errors and uncertainties, the validation process should account for these factors. The Bayesian approaches offer a robust framework for quantifying errors and uncertainties. Thus, in this study, we propose a Bayesian validation method for ODE models that addresses model inadequacy, presented as bias. Since the proposed method estimates bias as a function of time, it can provide prediction bounds for the entire observed time interval. Consequently, it allows for a direct evaluation of the model's validity across the whole time interval, and it can lead to better prediction by correcting the bias.

生物网络动态系统的贝叶斯验证。
动态系统包含了广泛的数学模型,用于描述复杂网络或系统随时间的行为。建模这种动力学的最常用方法之一是通过一组常微分方程(ode),通常是基于假设、已知的相互作用或观察到的轨迹构建的。然而,ode是确定性的和不灵活的,而生物数据通常是嘈杂的。因此,模型拟合可能无法解释所有可能的数据变化,并且实际的生物过程与假设的模型之间可能存在差异。这种差异可能导致对生物网络的预测和解释不准确。因此,需要根据观察到的数据来验证ODE模型。鉴于生物网络通常涉及多个错误和不确定性来源,验证过程应考虑到这些因素。贝叶斯方法为量化误差和不确定性提供了一个强大的框架。因此,在本研究中,我们提出了一种用于ODE模型的贝叶斯验证方法,以解决模型的不足,即偏差。由于提出的方法估计偏差作为时间的函数,它可以提供整个观测时间区间的预测界限。因此,它允许在整个时间间隔内对模型的有效性进行直接评估,并且可以通过纠正偏差来进行更好的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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