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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 應用力學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97771
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dc.contributor.advisor林哲宇zh_TW
dc.contributor.advisorChe-Yu Linen
dc.contributor.author莊詠丞zh_TW
dc.contributor.authorYung-Cheng Chuangen
dc.date.accessioned2025-07-16T16:12:51Z-
dc.date.available2025-07-17-
dc.date.copyright2025-07-16-
dc.date.issued2025-
dc.date.submitted2025-07-08-
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33. Sawada, Y., Tamada, M., Dubin-Thaler, B. J., Cherniavskaya, O., Sakai, R., Tanaka, S., & Sheetz, M. P. (2006). Force sensing by mechanical extension of the Src family kinase substrate p130Cas. Cell, 127(5), 1015-1026.
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37. Zhang, Y., & Habibovic, P. (2022). Delivering mechanical stimulation to cells: state of the art in materials and devices design. Advanced Materials, 34(32), e2110267.
38. Kaunas, R., Nguyen, P., Usami, S., & Chien, S. (2005). Cooperative effects of Rho and mechanical stretch on stress fiber organization. Proceedings of the National Academy of Sciences, 102(44), 15895-15900.
39. Ouyang, X., Xie, Y., & Wang, G. (2019). Mechanical stimulation promotes the proliferation and the cartilage phenotype of mesenchymal stem cells and chondrocytes co-cultured in vitro. Biomedicine & Pharmacotherapy, 117, 109146.
40. Virjula, S., Zhao, F., Leivo, J., Vanhatupa, S., Kreutzer, J., Vaughan, T. J., ... & Miettinen, S. (2017). The effect of equiaxial stretching on the osteogenic differentiation and mechanical properties of human adipose stem cells. Journal of the Mechanical Behavior of Biomedical Materials, 72, 38-48.
41. Ergene, E., Bilecen, D. S., Kaya, B., Huri, P. Y., & Hasirci, V. (2020). 3D cellular alignment and biomimetic mechanical stimulation enhance human adipose-derived stem cell myogenesis. Biomedical Materials, 15(5), 055017.
42. Wang, T., Thien, C., Wang, C., Ni, M., Gao, J., Wang, A., ... & Zheng, M. H. (2018). 3D uniaxial mechanical stimulation induces tenogenic differentiation of tendon‐derived stem cells through a PI3K/AKT signaling pathway. The FASEB Journal, 32(9), 4804-4814.
43. Ahn, H., Ju, Y. M., Takahashi, H., Williams, D. F., Yoo, J. J., Lee, S. J., ... & Atala, A. (2015). Engineered small diameter vascular grafts by combining cell sheet engineering and electrospinning technology. Acta Biomaterialia, 16, 14-22.
44. Kapur, S., Baylink, D. J., & Lau, K. H. W. (2003). Fluid flow shear stress stimulates human osteoblast proliferation and differentiation through multiple interacting and competing signal transduction pathways. Bone, 32(3), 241-251.
45. Tse, J. M., Cheng, G., Tyrrell, J. A., Wilcox-Adelman, S. A., Boucher, Y., Jain, R. K., & Munn, L. L. (2012). Mechanical compression drives cancer cells toward invasive phenotype. Proceedings of the National Academy of Sciences, 109(3), 911-916.
46. Novak, C. M., Horst, E. N., Lin, E., & Mehta, G. (2020). Compressive stimulation enhances ovarian cancer proliferation, invasion, chemoresistance, and mechanotransduction via CDC42 in a 3D bioreactor. Cancers, 12(6), 1521.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97771-
dc.description.abstract細胞對機械刺激的反應在組織工程、再生醫學和力學生物學中扮演關鍵角色,然而根據現有的施力策略多依賴經驗法則來去設計,缺乏系統性與可量化的依據。本研究提出一套以最佳控制理論為核心,結合細胞生物力學的建模與數值模擬的分析,嘗試設計出細胞於特定時間內所受到的最佳週期性弦波施力策略,以最大化細胞變形響應、兼顧能量消耗效率以及保護細胞內部結構。
本研究分別建立線性彈性模型與非線性彈性模型,以描述細胞在力學刺激下之動態行為,並應用前向-後向疊代法進行數值模擬。透過時間網格數與容許誤差值之收斂性分析,選定適合之模擬參數後,進一步探討不同的控制輸入條件下細胞變形響應與目標函數的變化。模擬結果顯示,細胞變形與控制輸入呈現正相關;此外,最佳控制策略相較於任意振幅輸入可顯著提升系統效益。進一步於頻率掃描中亦發現最佳頻率點可使目標函數達最大值,說明控制輸入之動態參數設計具關鍵影響。
綜合而言,本研究驗證了最佳控制理論於細胞機械刺激設計中之可行性,並提出具彈性與擴展潛力之施力優化架構。未來若能結合實驗驗證與更複雜之生物動態模型,將有望應用於智能化細胞刺激平台之開發,提升再生醫學與生物工程之應用效益。
zh_TW
dc.description.abstractThe cellular response to mechanical stimulation plays a pivotal role in tissue engineering, regenerative medicine, and mechanobiology. However, existing force application strategies are often designed based on empirical rules, lacking systematic structure and quantitative justification. This study proposes a framework centered on optimal control theory, integrating cellular biomechanical modeling with numerical simulation analysis, to design an optimal sin wave stimulation strategy applied within a specific time window. The objective is to maximize cellular deformation responses while balancing energy efficiency and protecting internal cellular structures.
Two mechanical models were constructed: a linear elastic model and a nonlinear elastic model, to describe the dynamic behavior of cells under mechanical loading. A forward–backward iterative method was employed for numerical simulation. Convergence analysis was performed with respect to time grid density and tolerance values to determine suitable simulation parameters. The effects of various control input conditions on cell deformation and the objective function were further examined. Simulation results indicated a positive correlation between cell deformation and control input amplitude. In addition, the optimized control strategy significantly outperformed fixed-amplitude inputs in terms of system performance. Frequency analysis also revealed an optimal frequency that maximized the objective function, highlighting the critical role of dynamic parameter design in stimulation effectiveness.
In conclusion, this study demonstrates the feasibility of applying optimal control theory to the design of cell mechanical stimulation strategies, and proposes a flexible, extensible framework for stimulation optimization. Future work combining experimental validation with more complex biological dynamic models is expected to facilitate the development of intelligent cell stimulation platforms, contributing to advancements in regenerative medicine and biomedical engineering.
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dc.description.tableofcontents誌謝 i
中文摘要 ii
Abstract iii
目次 v
圖次 vii
表次 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 2
1.2.1 細胞結構與功能 2
1.2.2 細胞對機械性微環境的感應 5
1.2.3 機械刺激技術 7
1.2.4 應變強度與細胞反應 8
第二章 最佳控制理論 10
2.1 基本問題和必要條件 10
2.1.1 定義 10
2.1.2 目標函數 10
2.1.3 必要條件 11
2.2 Pontryagin’s 最大值原理 15
2.3 回報項 18
2.4 求解最佳控制問題步驟 19
第三章 研究方法 24
3.1 細胞受週期性外力的數學模型 24
3.1.1 質量-彈簧-阻尼器模型 24
3.1.2 Duffing模型 25
3.2 建立數學模型 25
3.3 建立目標函數 28
3.4 最佳控制理論建構與求解 29
3.4.1 目標函數一 29
3.4.2 目標函數二 33
3.5 數值模擬方法 34
第四章 數值模擬與結果分析 36
4.1 目標函數一 36
4.1.1 收斂性分析 36
4.1.2 應用最佳控制理論之模擬結果 41
4.1.3 未應用最佳控制理論之模擬結果 43
4.1.4 最佳頻率輸入 53
4.2 目標函數二 54
4.2.1 收斂性分析 54
4.2.2 應用最佳控制理論之模擬結果 59
4.2.3 未應用最佳控制理論之模擬結果 61
4.2.4 最佳頻率輸入 71
4.3 分析 73
第五章 結果與討論 77
5.1 結論 77
5.2 未來展望 77
參考文獻 79
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dc.language.isozh_TW-
dc.subject力學生物學zh_TW
dc.subject數值模擬zh_TW
dc.subject細胞變形zh_TW
dc.subject機械刺激設計zh_TW
dc.subject線性黏彈性模型zh_TW
dc.subject非線性黏彈性模型zh_TW
dc.subject最佳控制理論zh_TW
dc.subjectNonlinear Viscoelastic Modelen
dc.subjectOptimal Control Theoryen
dc.subjectMechanical Biologyen
dc.subjectNumerical Simulationen
dc.subjectCell Deformationen
dc.subjectMechanical Stimulation Designen
dc.subjectLinear Viscoelastic Modelen
dc.title最佳控制理論於力學生物學之應用:刺激細胞生長的最佳週期性施力之設計zh_TW
dc.titleApplication of Optimal Control Theory to Mechanical Biology: Design of Optimal Cyclic Force Application to Stimulate Cell Growthen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee劉立偉;李宇修zh_TW
dc.contributor.oralexamcommitteeLi-Wei Liu;Yu-Hsiu Leeen
dc.subject.keyword最佳控制理論,力學生物學,數值模擬,細胞變形,機械刺激設計,線性黏彈性模型,非線性黏彈性模型,zh_TW
dc.subject.keywordOptimal Control Theory,Mechanical Biology,Numerical Simulation,Cell Deformation,Mechanical Stimulation Design,Linear Viscoelastic Model,Nonlinear Viscoelastic Model,en
dc.relation.page84-
dc.identifier.doi10.6342/NTU202500960-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2025-07-10-
dc.contributor.author-college工學院-
dc.contributor.author-dept應用力學研究所-
dc.date.embargo-lift2025-07-17-
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