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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 生醫電子與資訊學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99784
完整後設資料紀錄
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dc.contributor.advisor魏安祺zh_TW
dc.contributor.advisorAn-Chi Weien
dc.contributor.author江承昱zh_TW
dc.contributor.authorCheng-Yu Jiangen
dc.date.accessioned2025-09-17T16:40:26Z-
dc.date.available2025-09-18-
dc.date.copyright2025-09-17-
dc.date.issued2024-
dc.date.submitted2025-08-21-
dc.identifier.citationDavid Reinhart, Lukas Damjanovic, Christian Kaisermayer, Wolfgang Sommeregger, Andreas Gili, Bernhard Gasselhuber, Andreas Castan, Patrick Mayrhofer, Clemens Grünwald-Gruber, and Renate Kunert. Bioprocessing of recombinant cho-k1, cho-dg44, and cho-s: Cho expression hosts favor either mab production or biomass synthesis. Biotechnology Journal, 14(3):1700686, June 2018.
Lucas Lemire, Phuong Lan Pham, Yves Durocher, and Olivier Henry. Practical Considerations for the Scale-Up of Chinese Hamster Ovary (CHO) Cell Cultures, pages 367–400. Springer International Publishing, 2021.
Neelesh Gangwar, Keerthiveena Balraj, and Anurag S. Rathore. Explainable ai for cho cell culture media optimization and prediction of critical quality attribute. Applied Microbiology and Biotechnology, 108(1), April 2024.
Kexue Liang, Hongzhen Luo, and Qi Li. Enhancing and stabilizing monoclonal antibody production by chinese hamster ovary (cho) cells with optimized perfusion culture strategies. Frontiers in Bioengineering and Biotechnology, 11, January 2023.
Niraj Kumar, Patrick Gammell, and Martin Clynes. Proliferation control strategies to improve productivity and survival during cho based production culture: A summary of recent methods employed and the effects of proliferation control in product secreting cho cell lines. Cytotechnology, 53(1–3):33–46, March 2007.
Nikolas Zeh, Patrick Schlossbauer, Nadja Raab, Florian Klingler, René Handrick, and Kerstin Otte. Cell line development for continuous high cell density biomanufacturing: Exploiting hypoxia for improved productivity. Metabolic Engineering Communications, 13:e00181, December 2021.
Trenton L. Place, Frederick E. Domann, and Adam J. Case. Limitations of oxygen delivery to cells in culture: An underappreciated problem in basic and translational research. Free Radical Biology and Medicine, 113:311–322, December 2017.
Jana Schellenberg, Michaela Dehne, Ferdinand Lange, Thomas Scheper, Dörte Solle, and Janina Bahnemann. Establishment of a perfusion process with antibody-producing cho cells using a 3d-printed microfluidic spiral separator with web-based flow control. Bioengineering, 10(6):656, May 2023.
E.D. Israels and L.G. Israels. The cell cycle. The Oncologist, 5(6):510–513, December 2000.
Brian Ortmann, Jimena Druker, and Sonia Rocha. Cell cycle progression in response to oxygen levels. Cellular and Molecular Life Sciences, 71(18):3569–3582, May 2014.
Gennady Bocharov, Willi Jäger, Jonas Knoch, Maria Neuss-Radu, and Manfred Thiel. A mathematical model of hif-1 regulated cellular energy metabolism. Vietnam Journal of Mathematics, 49(1):119–141, June 2020.
Atefeh Ghorbaniaghdam, Olivier Henry, and Mario Jolicoeur. An in-silico study of the regulation of cho cells glycolysis. Journal of Theoretical Biology, 357:112–122, September 2014.
Brett A. Wagner, Sujatha Venkataraman, and Garry R. Buettner. The rate of oxygen utilization by cells. Free Radical Biology and Medicine, 51(3):700–712, August 2011.
Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B. Shah. Julia: A fresh approach to numerical computing. SIAM Review, 59(1):65–98, January 2017.
Artem Pelenitsyn, Julia Belyakova, Benjamin Chung, Ross Tate, and Jan Vitek. Type stability in julia: avoiding performance pathologies in jit compilation. Proceedings of the ACM on Programming Languages, 5(OOPSLA):1–26, October 2021.
Eric Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl_3):7280–7287, May 2002.
Taylor M. Anderson and Suzana Dragićević. Network-agent based model for simulating the dynamic spatial network structure of complex ecological systems. Ecological Modelling, 389:19–32, December 2018.
Kerri-Ann Norton, Chang Gong, Samira Jamalian, and Aleksander S. Popel. Multiscale agent-based and hybrid modeling of the tumor immune microenvironment. Processes, 7(1):37, January 2019.
Zhihui Wang, Joseph D. Butner, Romica Kerketta, Vittorio Cristini, and Thomas S. Deisboeck. Simulating cancer growth with multiscale agent-based modeling. Seminars in Cancer Biology, 30:70–78, February 2015.
Demarcus Briers, Iman Haghighi, Douglas White, Melissa L. Kemp, and Calin Belta. Pattern synthesis in a 3d agent-based model of stem cell differentiation. In 2016 IEEE 55th Conference on Decision and Control (CDC), pages 4202–4207. IEEE, December 2016.
Christian Nitzsche and Stefan Simm. Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on sars-cov-2. Scientific Reports, 14(1), June 2024.
C.M. Macal and M.J. North. Tutorial on agent-based modeling and simulation. In Proceedings of the Winter Simulation Conference, 2005., pages 14 pp.–, 2005.
Faza Fawzan Bastarianto, Thomas O. Hancock, Charisma Farheen Choudhury, and Ed Manley. Agent-based models in urban transportation: review, challenges, and opportunities. European Transport Research Review, 15(1), June 2023.
Ewout ter Hoeven, Jan Kwakkel, Vincent Hess, Thomas Pike, Boyu Wang, rht, and Jackie Kazil. Mesa 3: Agent-based modeling with python in 2025. Journal of Open Source Software, 10(107):7668, March 2025.
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Christopher Rackauckas and Qing Nie. Differentialequations.jl –a performant and feature-rich ecosystem for solving differential equations in julia. Journal of Open Research Software, 5(1):15, May 2017.
Elif S. Bayrak, Tony Wang, Matt Jerums, Myra Coufal, Chetan Goudar, Ali Cinar, and Cenk Undey. In silico cell cycle predictor for mammalian cell culture bioreactor using agent-based modeling approach. IFAC-PapersOnLine, 49(7):200–205, 2016.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99784-
dc.description.abstractCHO細胞為中國倉鼠卵巢細胞,其具穩定生長特性,廣泛應用於重組蛋白與單株抗體的生產。CHO細胞培養在24孔盤中是常見的培養模式,製藥產業中亦常見以此種培養方式,作為放大培養之前的早期培養階段,用以產生所需的蛋白。
因為24孔盤的半封閉幾何形狀以及擴散限制,此種培養形式無法快速補充培養液中的氧氣濃度,故在細胞數目隨時間快速增長的情形下,細胞容易遭遇缺氧環境,進而影響代謝。
本論文利用Julia程式語言實作一代理人模型,以細胞週期為基礎,搭配不同環境條件下的代謝趨勢,模擬典型CHO細胞在24孔盤中的培養情形,並觀察缺氧情形下幾個重要的量測指標。
zh_TW
dc.description.abstractChinese hamster ovary (CHO) cells are widely used in the production of recombinant proteins and monoclonal antibodies due to their stable growth characteristics. Culturing CHO cells in 24-well plates is a common practice, particularly in the pharmaceutical industry, where it serves as an early-stage system prior to large-scale bioreactor cultivation.
However, the semi-enclosed geometry and diffusion limitations of 24-well plates restrict oxygen replenishment in the culture medium. As cell density increases, these limitations often lead to hypoxic conditions, which in turn affect cell metabolism and growth dynamics.
In this thesis, the Julia programming language is used to develop an agent-based model that integrates cell cycle regulation with metabolic responses under varying environmental conditions. The model simulates CHO cell culture in 24-well plates and enables the analysis of key indicators of cell behavior under hypoxia, providing insights that may guide experimental design and bioprocess optimization.
en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-17T16:40:26Z
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dc.description.provenanceMade available in DSpace on 2025-09-17T16:40:26Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents摘要 i
Abstract ii
Contents iii
List of Tables v
List of Figures vi
1 Introduction 1
1.1 Background 1
1.1.1 CHO Cell 1
1.1.2 Microenvironment in Micro-Well Plate 2
1.1.3 Cellular Responses to Hypoxia 4
1.2 Tools for Modeling 5
1.2.1 Julia 5
1.2.2 Agent-Based Model 5
1.3 Motivation and Objectives 8
2 Methodology 10
2.1 Developing Environment and Tools 10
2.2 Assumption of the Model 11
2.3 Architecture of the Model 12
2.4 Simulation of Cell Growth 16
2.4.1 Sensing Rules 16
2.4.2 Cell Cycle Phase Updating Rules 17
2.4.3 Consumption Rules 22
2.5 Simulation of Culture Medium 23
3 Results and Discussion 25
3.1 Results of Cell Growth Simulation 25
4 Conclusion 37
References 39
Appendices 43
A How to Run the Model 44
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dc.language.isoen-
dc.subjectCHO細胞zh_TW
dc.subject細胞培養zh_TW
dc.subject代理人模型zh_TW
dc.subject缺氧zh_TW
dc.subjectcell cultureen
dc.subjectagent-based modelen
dc.subjectCHO cellen
dc.subjecthypoxiaen
dc.title利用代理人模型模擬CHO細胞在靜態培養下的生長zh_TW
dc.titleSimulating Growth of CHO Cells in Static Culture Using an Agent-Based Modelen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee何亦平;劉彥良;游舒涵zh_TW
dc.contributor.oralexamcommitteeMegan Yi-Ping Ho;Yen-Liang Liu;Shu-Han Yuen
dc.subject.keywordCHO細胞,細胞培養,代理人模型,缺氧,zh_TW
dc.subject.keywordCHO cell,cell culture,agent-based model,hypoxia,en
dc.relation.page44-
dc.identifier.doi10.6342/NTU202504422-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2025-08-21-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept生醫電子與資訊學研究所-
dc.date.embargo-lift2025-09-18-
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