電腦模擬或電腦模型目前是了解複雜生物過程的最前沿工具。然而，生物組織的錯綜複雜性，以及其對不同物理和化學刺激的多種細胞機制的反應，使得相應的數學模型具有高度非線性特性，涉及眾多參數。這些參數對模型至關重要，但通常僅針對特定條件進行調整，使得總結的普遍性受到挑戰；而且亦時常面臨無法通過臨床測量或實驗獲取部分參數的窘境。
於2015年的兩篇研究突破性發現顯示，從中樞神經系統中清除廢物的主要方式並非通過擴散，即廢物從腦組織逐漸遷移到血管。相反，人們發現了一種名為膠質淋巴系統的專門的宏觀清除系統。該系統通過由星形細胞形成的獨特的周血管通道網絡高效清除廢物，星形細胞是一種特定類型的膠質細胞。為了更深入地了解膠淋巴系統內流體運動的機制，在這項研究中，我們引入了一種基於感知器的方法，並應用大腦四重網絡多孔彈性力學模型，找尋大腦的水傳輸係數的範圍；通過使用這個模型，並借助機器學習分類方法，我們進行了一個系統而高效的參數研究。
考慮到可用數據的性質和特點，本研究所發展的演算法為一個簡單而功能強大的模型，用來求解穩態方程組之反問題，具體而言是一種生理學啟發的感知器方法，目標是探索水傳輸係數的功能影響。為了進一步限制模型的學習過程，我們加入了一個解析解方法，用於研究和闡明大腦多重網絡多孔彈性力學模型中的未知參數。此外，文中亦詳細介紹演算法，認識到它在需要估計PDE中的未知參數的各個領域中的潛力應用。此外，研究尾聲亦檢驗了所提出方法的效率和有效性，有效地展示了我們的框架如何促進快速參數估計。最後，我們討論了未滿足的需求並勾勒了該框架的未來任務。
我們的研究結果表明，該模型成功地捕捉到了人腦內的運輸現象。此外，在特定的水轉移係數範圍內，該模型間接預測了膠淋巴系統的存在，從而為膠淋巴系統提供了理論支持。我們的研究不僅證明了通過計算機輔助建模揭示複雜生物系統的生理特性的可行性，還突出了所提出的新方法在進行參數研究方面的潛力。

Simulations using computer systems and in silico models are at the forefront of advancing our comprehension of intricate biological processes. Yet, the intricate composition of biological tissues, where multiple cellular mechanisms interact with diverse physical and chemical stimuli, imparts a highly nonlinear nature to the corresponding mathematical models, which encompass a multitude of parameters. While these parameters hold significance in the models, they often undergo customization to suit specific conditions, thus posing challenges in establishing universally applicable findings. Additionally, obtaining certain parameters through clinical measurements or experiments can pose formidable difficulties.
In a recent breakthrough, scientists have challenged the previous notion that waste clearance from the central nervous system occurs primarily through diffusion, with waste gradually migrating from brain tissue to blood vessels. Instead, the discovery of a specialized macroscopic waste clearance system, known as the glymphatic system, has occurred. This system efficiently eliminates waste through a unique network of perivascular channels formed by astrocytes, a specific type of glial cell. To delve deeper into the intricate dynamics of fluid movement within the glymphatic system, we have introduced a perceptron-based method and targeted the water transfer coefficients in a four-compartmental poroelasticity model of the brain. Employing this model, we have conducted a systematic and efficient parametric study aided by classification methods of machine learning.
Considering the nature and characteristics of the available data, the algorithm developed in this study presents a simple yet functional model that addresses a steady-state inverse problem, specifically utilizing a physiologically inspired perceptron method to explore the functional impact of water transfer coefficients. In order to enhance the model's learning process, we have integrated an analytical solution method to explore and elucidate the unidentified parameters within the multi-compartmental poroelasticity model of the brain. Additionally, we provide a comprehensive exposition of the algorithm, acknowledging its potential applicability in various domains that necessitate the estimation of unknown parameters in partial differential equations. Furthermore, we assess the efficiency and effectiveness of the suggested approach, effectively demonstrating how our framework facilitates prompt parameter estimation. Finally, we acknowledge outstanding requirements and delineate future goals to advance this framework.
The results of our study demonstrate that the model successfully captures the transport phenomena within the human brain. Furthermore, within a specific range of water transfer coefficients, the model indirectly predicts the existence of the glymphatic system, thereby providing theoretical support for the glymphatic theory. This study not only demonstrates the feasibility of uncovering the physiological properties of complex biological systems through computer-aided modelling but also underscores the potential of the presented novel method for conducting parametric studies.

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