A computational model of Alzheimer's disease at the nano, micro, and macroscales

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics Pub Date : 2024-03-22 DOI:10.3389/fninf.2024.1348113
Éléonore Chamberland, Seyedadel Moravveji, Nicolas Doyon, Simon Duchesne
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Abstract

IntroductionMathematical models play a crucial role in investigating complex biological systems, enabling a comprehensive understanding of interactions among various components and facilitating in silico testing of intervention strategies. Alzheimer's disease (AD) is characterized by multifactorial causes and intricate interactions among biological entities, necessitating a personalized approach due to the lack of effective treatments. Therefore, mathematical models offer promise as indispensable tools in combating AD. However, existing models in this emerging field often suffer from limitations such as inadequate validation or a narrow focus on single proteins or pathways.MethodsIn this paper, we present a multiscale mathematical model that describes the progression of AD through a system of 19 ordinary differential equations. The equations describe the evolution of proteins (nanoscale), cell populations (microscale), and organ-level structures (macroscale) over a 50-year lifespan, as they relate to amyloid and tau accumulation, inflammation, and neuronal death.ResultsDistinguishing our model is a robust foundation in biological principles, ensuring improved justification for the included equations, and rigorous parameter justification derived from published experimental literature.ConclusionThis model represents an essential initial step toward constructing a predictive framework, which holds significant potential for identifying effective therapeutic targets in the fight against AD.
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阿尔茨海默氏症的纳米、微观和宏观计算模型
导言数学模型在研究复杂的生物系统中起着至关重要的作用,它能使人们全面了解各种成分之间的相互作用,并促进干预策略的硅学测试。阿尔茨海默病(AD)的特点是多因素病因和生物实体之间错综复杂的相互作用,由于缺乏有效的治疗方法,因此需要采取个性化的方法。因此,数学模型有望成为防治阿尔茨海默病不可或缺的工具。然而,这一新兴领域的现有模型往往存在局限性,如验证不足或仅关注单一蛋白质或途径。这些方程描述了蛋白质(纳米尺度)、细胞群(微观尺度)和器官级结构(宏观尺度)在 50 年生命周期中的演变,因为它们与淀粉样蛋白和 tau 的积累、炎症和神经元死亡有关。结果我们的模型与众不同之处在于它建立在生物学原理的坚实基础上,确保了所包含方程的合理性得到了改进,并从已发表的实验文献中得出了严格的参数合理性。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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