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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張書瑋 | zh_TW |
dc.contributor.advisor | Shu-Wei Chang | en |
dc.contributor.author | 惠維翰 | zh_TW |
dc.contributor.author | Wri-Han Hui | en |
dc.date.accessioned | 2023-09-22T17:09:19Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-09-22 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-11 | - |
dc.identifier.citation | 1. Fratzl, P., Collagen: structure and mechanics, an introduction. 2008: Springer.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90037 | - |
dc.description.abstract | 膠原蛋白在人體中是重要的蛋白質之一,並且佔身體中蛋白質組成的三分之 一,主要的分佈在締結組織如骨頭、軟骨、肌腱、皮膚和角膜等等。由於締結組織 多為多層結構,從微觀尺度了解膠原蛋白分子的結構、分子間作用力以及力學性質 對於我們了解組織是如何運作是相當重要的。此外,膠原蛋白在人體中的提供優異 的機械特性,近年來也被運用在許多的生物醫學材料當中,膠原蛋白對人體高度的 生物相容以及可塑形的特性被應用在許多創傷修復支架中。
此外,近年來不管在材料開發和製程中,理論、模擬以及機器學習的幫助加入 了新穎材料開發的進程。透過理論以及模擬的方法減少材料開發和製程的所需的 時間以及材料。實驗以及模擬的龐大資料,經由機器學學習不管在分類或是性質預 測都是相當優異的。另外,也可以藉由機器學習的模型找出更優異材料組成作為開 發新穎材料的重要指標。 本研究的方向會分為膠原蛋白組織的性質研究以及粗粒最佳化。膠原蛋白組 織的三個效應:交聯、溫度效應以及突變進行討論。交聯效應以模擬的方法探討正 常、老化和疾病等交聯狀況對膠原蛋白纖維之影響,並提出上述之變形機制。溫度 效應以模擬的方法研究膠原蛋白分子對溫度的敏感程度,從力學性質到分子結構 的分析了解溫度造成的效應,此外利用機器學習的方法建立應變預測模型提供序 列的變形預測。突變效應主要討論成骨不全症的致死的預測,並建立表現更優異的 預測模型提供未來診斷之輔助。透過模擬的方法針對膠原蛋白的微觀進行探討,更 加瞭解上述效應在分子尺度下的影響,並藉由機器學習的方法建立預測模型以提 供未來新穎材料設計的想法。粗粒最佳化以逆波茲曼法為啟發,結合交叉熵最佳化 方法發展出多目標最佳化的映射粗粒化模型的方法。此外,粗粒化的模型以及參數 化的能量描述讓複雜的系統簡單化,除了放大模型尺度以及降低計算量外也提供 方法建立複合高分子材料之模型。 | zh_TW |
dc.description.abstract | Collagen is one of the most important proteins in the human body, comprising one- third of the body's protein composition. It is mainly distributed in connective tissues such as bones, cartilage, tendons, skin, and corneas. Understanding the structure, molecular interactions, and mechanical properties of collagen molecules at the microscale is crucial for understanding how tissues function. Collagen's excellent mechanical properties have been utilized in many biomedical materials, particularly in wound repair scaffolds, due to its high biocompatibility and pliability. In recent years, theoretical, simulation, and machine learning methods have been incorporated into novel material development processes, reducing the time and materials required. Machine learning has proven to be a powerful tool for classification and property prediction, which serves as a perfect fit for studying immense data generated from experiments and simulations. Moreover, machine learning models can furtherly identify superior material compositions, which could be a vital indicator for material development.
This study focused on two research directions: the study of collagen tissue properties and coarse-grained optimization. We discussed three main effects on collagen tissue: crosslinking, thermal effects, and mutations. The cross-linking effect was investigated using simulation methods to explore the influence of normal, aging, and diseased crosslinking on collagen fibers and propose deformation mechanisms. The thermal effect was studied using simulation methods to investigate the sensitivity of collagen molecules to temperature, from mechanical properties to molecular structural analysis, and establish a strain prediction model using machine learning methods. The mutation effect mainly discussed the main lethal features of osteogenesis imperfecta and established a more accurate predictive model to assist future diagnosis. By exploring collagen at the molecular level through simulation methods and building predictive models using machine learning methods, we can understand the above effects and provide ideas for future novel material designs. The coarse-grained optimization was inspired by the Boltzmann Inverse method and combined with the cross-entropy method for optimization to develop a method for multi-objective optimization of the mapping coarse-grained model. The coarse-grained model and parameterized energy description simplify complex systems, providing a method to establish models for composite polymer materials and scaling up the model size, and reducing computational costs. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:09:19Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-09-22T17:09:19Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 目錄
口試委員會審定書 # 誌謝 i 中文摘要 ii Abstract iii 圖目錄 viii 表目錄 xv 第1章、 緒論 1 1.1 背景介紹 1 1.2 文獻回顧 3 1.2.1 膠原蛋白的背景介紹 3 1.2.2 膠原蛋白與高分子之複合材料 4 1.2.3 交聯對於膠原蛋白之重要性 6 1.2.4 溫度對於膠原蛋白之重要性 7 1.2.5 突變對於膠原蛋白之重要性 8 1.2.6 全原子模型映射成粗粒模型之方法 10 1.2.7 機器學習在蛋白質領域上的研究發展 11 1.3 論文目的 14 1.4 論文方向 15 第2章、 理論與方法 16 2.1 分子動力模擬 (Molecular dynamics, MD) 16 2.1.1 分子動力模擬理論 16 2.1.2 拉伸分子動力模擬 (Steered molecular dynamics, SMD) 17 2.2 生物分子力場描述 (Force Field) 17 2.3 逆波茲曼法(Boltzmann inversion method, BIm) 19 2.4 嵌入 (Embedding) 20 2.4.1 嵌入式語言模型(Embedding from Language Model, ELMo) 20 2.4.2 基於變換器的雙向編碼器表示技術(Bidirectional Encoder Representations from Transformers, BERT) 21 2.5 交叉熵最佳化方法(Cross-entropy method for optimization, CEm) 22 2.6 圖神經網絡 (Graph Neural Network, GNN) 23 2.6.1 圖卷積網路(Graph Convolutional Networks, GCN) 23 2.6.2 梯度權重類別活化映射(Gradient-weighted Class Activation Mapping, Grad-CAM) 23 2.7 自變分編碼器(Variational Auto-Encoder, VAE) 24 2.8 極限梯度上升(eXtreme Gradient Boosting, XGBoost) 25 2.9 對應各章節所使用之方法與理論 25 第3章、 交聯對膠原蛋白對變形機制的影響 26 3.1 模型建立以及模擬流程 26 3.2 交聯在纖維尺度下的影響 28 3.2.1 交聯對膠原蛋白纖維的變形分佈改變 28 3.2.2 滑動以及分子伸長的比例 30 3.3 交聯在分子尺度下的影響 31 3.3.1 分子結構上的影響 31 3.3.2 影響生物訊號的可能性 33 3.4 結果與討論 35 第4章、 膠原蛋白的溫度效應和序列的熱性質預測 37 4.1 模型建立以及模擬過程 37 4.2 膠原蛋白分子受熱的性質變化 38 4.2.1 在力學表現上的影響 38 4.2.2 在分子結構上的影響 41 4.3 利用機器學習預測序列在313K時的拉伸應變 46 4.3.1 模型架構 46 4.3.2 預測效能 47 4.4 結果與討論 49 第5章、 膠原蛋白的成骨不全症的致死風險預測 51 5.1 資料集以及實驗設計 51 5.1.1 資料分析 51 5.1.2 測試資料集以及測試模型 53 5.2 建立成骨不全致死風險預測模型以及分析 53 5.2.1 模型架構以及參數設置 53 5.2.2 模型預測表現 55 5.2.3 模型分類表現 60 5.2.4 Grad-CAM的特徵分析 61 5.2.5 預測未知資料 71 5.3 結果與討論 72 第6章、 基於逆波茲曼法之多目標最佳化 73 6.1 架構以及流程 73 6.1.1 全原子模型模擬 73 6.1.2 Cross Entropy-Boltzmann Inversion method (CE-BIm) 架構 74 6.2 聚乙烯粗粒化 76 6.2.1 全原子模型平衡 76 6.2.2 使用CE-BIm映射至粗粒模型 76 6.2.3 驗證與比較 80 6.3 結果與討論 84 第7章、 結論與未來展望 85 7.1 總結 85 7.2 未來展望 87 參考資料 89 | - |
dc.language.iso | zh_TW | - |
dc.title | 結合分子模擬與機器學習預測膠原蛋白分子與高分子之力學特性與突變表現 | zh_TW |
dc.title | Predicting the Mechanical Properties and Mutational Effects of Collagen Molecules and Polymers by Combining Molecular Simulations and Machine Learning | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 博士 | - |
dc.contributor.oralexamcommittee | 陳俊杉;韓仁毓;游佳欣;游濟華;洪子倫;沈立軒 | zh_TW |
dc.contributor.oralexamcommittee | Chuin-Shan Chen;Jen-Yu Han;Jia-shing Yu;Chi-Hua Yu;Tzyy-Leng Horng;Li-Hsuan Shen | en |
dc.subject.keyword | 膠原蛋白,交聯,溫度效應,成骨不全症,高分子複合材料,逆波茲曼法,機器學習, | zh_TW |
dc.subject.keyword | Collagen,Crosslink,Temperature effect,Osteogenesis imperfecta,Polymer composite material,Boltzmann Inversion method,Machine Learning, | en |
dc.relation.page | 97 | - |
dc.identifier.doi | 10.6342/NTU202304121 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-13 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 土木工程學系 | - |
顯示於系所單位: | 土木工程學系 |
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