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Identifiability of Nonlinear Biological Models

非线性生物模型的可识别性

基本信息

批准号：
7405316
负责人：
DAVID M FOSTER
金额：
$ 20.03万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-03-01 至 2009-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7405316
关键词：
Address Adoption Algorithms Animals Architecture Biological Biological Models Biology Biomedical Engineering Chemicals Childhood Class Client Clinical Medicine Clinical Research Clinical Trials Communities Complex Computer software Computers Computing Methodologies Data Development Diagnostic Dose Drug Kinetics Effect Modifiers (Epidemiology)End Time Enzyme Kinetics Enzymes Equation Experimental Designs Experimental Models Feedback Fostering Goals Health Hormones Human Institutes Internet Invasive Italy Kinetics Knowledge Label Laws Least-Squares Analysis Life Manuals Markov Chains Mathematics Measurement Meta-Analysis Methods Modeling Modification Nature Noise Non-linear Models Nonlinear Dynamics Numbers Numerical value Oral Organ Organism Output Patients Peer Review Personal Satisfaction Pharmaceutical Preparations Phase Physicians Physiological Physiological Processes Physiology Placement Population Population Analysis Principal Investigator Problem Solving Procedures Process Programming Languages Publishing Purpose Qualifying Rate Research Research Personnel Resources Running Sampling Schedule Site Software Tools Solutions Space Models Specific qualifier value Standards of Weights and Measures Structure Study models System Systems Analysis Systems Theory Technology Testing Theoretical Studies Theoretical model Thinking Time Today Translating United States National Institutes of Health Universities Washington Weight Work absorption base bioimaging court density design driving force drug metabolism graphical user interface interest mathematical model neglect open source pharmacodynamic model pharmacokinetic model platform-independent programs radiotracer research study simulation software development software systems theories tool user friendly software user-friendly

项目摘要

DESCRIPTION (provided by applicant): Mathematical models of biological systems are essential to the quantitative understanding of physiological and pathophysiological mechanisms in humans and animals. Physiologically plausible models, the structure of which reflect available knowledge and assumptions about the systems, are usually nonlinear and characterized by a large number of unknown parameters. Examples of such models are enzyme kinetics and pharmacokinetic-pharmacodynamic models. Before performing an experiment to estimate these unknown parameters from the data, the following question arises: will the data we are about to collect (usually at a substantial expense) contain enough information to precisely and unequivocally estimate (for example, via least squares or maximum likelihood) all the unknown parameters of the postulated model? This question, set in the (theoretical) context of an error-free model structure and noise-free data, is usually referred to as the a priori global identifiability problem. Despite its theoretical nature, it is an essential, but often overlooked, prerequisite for model parameter estimation from real data. The solution of the identifiability problem is however in general very difficult, since one needs to solve a system of nonlinear algebraic equations which is increasing in number of terms and nonlinearity degree with the model order. The specific aims of this application focus on the development of an algorithm and a software tool to test a priori global identifiability of nonlinear compartmental models, a very inclusive class of ordinary nonlinear differential equation models based on conservation of mass. These models are widely used to study the kinetics of endogenous (e.g. substrates, hormones, enzymes) and exogenous (e.g., drugs, radiotracers) substances in living systems. The problem has been solved for a very limited set of models, but no solution exists in the general case. We will develop an algorithm based on computer algebra which allows to decrease the system complexity, thus providing the number of solutions for each parameter of the model. The software we propose to develop will be based on the client-server architecture paradigm, and will be open source, user-friendly and platform-independent. Such a tool would be very useful in experiment design. The software will also help in defining minimal input-output experimental configurations to assure a priori global identifiability: this is particularly important in clinical studies where severe constraints exist on experiment design, i.e. the number of inputs and outputs is limited for ethical and practical reasons.

描述(申请人提供)：生物系统的数学模型对于定量了解人类和动物的生理和病理生理学机制是必不可少的。生理上看似合理的模型，其结构反映了关于系统的现有知识和假设，通常是非线性的，并且具有大量未知参数的特征。这类模型的例子是酶动力学和药物动力学-药效学模型。在进行实验以从数据中估计这些未知参数之前，出现了以下问题：我们即将收集的数据(通常花费很大)是否包含足够的信息来精确和明确地估计(例如，通过最小二乘法或最大似然法)假设模型的所有未知参数？这个问题设置在无误差模型结构和无噪声数据的(理论)背景下，通常被称为先验全局可辨识性问题。尽管它的理论性质，它是一个基本的，但往往被忽视的先决条件，模型参数估计从实际数据。然而，可辨识性问题的求解通常是非常困难的，因为人们需要求解一个非线性代数方程组，该方程组的项数和非线性度随着模型阶数的增加而增加。这项应用的具体目标集中在开发一种算法和软件工具，以测试非线性分区模型的先验全局可辨识性，非线性分区模型是一类非常包容的基于质量守恒的普通非线性微分方程模型。这些模型被广泛用于研究生物系统中内源物质(如底物、激素、酶)和外源物质(如药物、放射性示踪剂)的动力学。对于非常有限的一组模型，这个问题已经解决了，但在一般情况下没有解决方案。我们将开发一种基于计算机代数的算法，它可以降低系统的复杂性，从而为模型的每个参数提供解的数量。我们建议开发的软件将基于客户端-服务器架构范例，并且将是开源的、用户友好的和独立于平台的。这样的工具在实验设计中将非常有用。该软件还将帮助定义最小输入输出实验配置，以确保先验的全局可识别性：这在临床研究中尤其重要，因为在临床研究中，实验设计存在严格的限制，即出于伦理和实际原因，输入和输出的数量受到限制。