請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88409
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭原忠 | zh_TW |
dc.contributor.advisor | Yuan-Chung Cheng | en |
dc.contributor.author | 楊允中 | zh_TW |
dc.contributor.author | Yun-Chung Yang | en |
dc.date.accessioned | 2023-08-15T16:09:57Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-15 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-28 | - |
dc.identifier.citation | [1] D. I. Bennett, K. Amarnath, and G. R. Fleming. A structurebased model of energy transfer reveals the principles of light harvesting in photosystem ii supercomplexes. J. Am. Chem. Soc., 135(24):9164–9173, 2013.
[2] C. Böde, I. A. Kovács, M. S. Szalay, R. Palotai, T. Korcsmáros, and P. Csermely. Network analysis of protein dynamics. FEBS letters, 581(15):2776–2782, 2007. [3] W. P. Bricker, P. M. Shenai, A. Ghosh, Z. Liu, M. G. M. Enriquez, P. H. Lambrev,H.S. Tan, C. S. Lo, S. Tretiak, S. FernandezAlberti,et al. Nonradiative relaxation of photoexcited chlorophylls: theoretical and experimental study. Sci. Rep., 5(1):13625, 2015. [4] K. Broess, G. Trinkunas, C. D. van der Weijde,J. P. Dekker, A. van Hoek, H. van Amerongen, et al. Excitation energy transfer and charge separation in photosystem ii membranes revisited. Biophys. J., 91(10):3776–3786, 2006. [5] S. Caffarri, K. Broess, R. Croce, and H. van Amerongen. Excitation energy transfer and trapping in higher plant photosystem ii complexes with different antenna sizes. Biophys. J., 100(9):2094–2103, 2011. [6] Y.J.Chang, V. Dani, T. P. Hayes, and S. Pettie. The energy complexity of bfs in radio networks. In Proceedings of the 39th Symposium on Principles of Distributed Computing, pages 273–282, 2020. [7] A. Damle, V. Minden, and L. Ying. Simple, direct and efficient multiway spectral clustering. Inf. Inference, 8(1):181–203, 2019. [8] A. S. Davydov. The theory of molecular excitons. Soviet Physics Uspekhi, 7(2):145,1964. [9] J.C.Delvenne, S. N. Yaliraki, and M. Barahona. Stability of graph communities across time scales. Proc. Natl. Acad. Sci. U.S.A., 107(29):12755–12760, 2010. [10] D. L. Dexter. A theory of sensitized luminescence in solids. J. Chem. Phys.,21(5):836–850, 1953. [11] E. W. Dijkstra. A note on two problems in connexion with graphs. In Edsger Wybe Dijkstra: His Life, Work, and Legacy, pages 287–290. 1959. [12] T. N. Do, H. L. Nguyen, P. Akhtar, K. Zhong, T. L. Jansen, J. Knoester, S. Caffarri,P. H. Lambrev, and H.S. Tan. Ultrafast excitation energy transfer dynamics in the lhcii–cp29–cp24 subdomain of plant photosystem ii. J. Phys. Chem. Lett., 13:4263–4271, 2022. [13] J. R. Durrant, D. R. Klug, S. L. Kwa, R. Van Grondelle, G. Porter, and J. P. Dekker.A multimer model for p680, the primary electron donor of photosystem ii. Proc.Natl. Acad. Sci., 92(11):4798–4802, 1995. [14] J. Edmonds and R. M. Karp. Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM, 19(2):248–264, 1972. [15] E. Estrada. The structure of complex networks: theory and applications. Oxford University Press, 2012. [16] Z.k.Feng, W.j.Niu, R. Zhang, S. Wang, and C.t.Cheng. Operation rule derivation of hydropower reservoir by kmeans clustering method and extreme learning machine based on particle swarm optimization. Journal of hydrology, 576:229–238, 2019. [17] L. R. Ford and D. R. Fulkerson. Maximal flow through a network. Canad. J. Math.,8:399–404, 1956. [18] T. Förster. Zwischenmolekulare energiewanderung und fluoreszenz. Ann Phys, 437(12): 55–75, 1948. [19] M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, G. A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A. V. Marenich, J. Bloino, B. G. Janesko, R. Gomperts, B. Mennucci, H. P. Hratchian, J. V. Ortiz, A. F. Izmaylov, J. L. Sonnenberg, D. WilliamsYoung, F. Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V. G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. J. Bearpark, J. J. Heyd, E. N. Brothers, K. N. Kudin, V. N. Staroverov, T. A. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. P. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, J. M. Millam, M. Klene, C. Adamo, R. Cammi, J. W. Ochterski, R. L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, and D. J. Fox. Gaussian˜16 Revision C.01, 2016. Gaussian Inc. Wallingford CT. [20] P. Govender and V. Sivakumar. Application of kmeans and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollut. Res., 11(1):40–56, 2020. [21] C. Houssier and K. Sauer. Circular dichroism and magnetic circular dichroism of the chlorophyll and protochlorophyll pigments. J. Am. Chem. Soc., 92(4):779–791,1970. [22] S.T.Hsieh, L. Zhang, D.W. Ye, X. Huang, and Y.C. Cheng. A theoretical study on the dynamics of light harvesting in the dimeric photosystem ii core complex: regulation and robustness of energy transfer pathways. Faraday Discuss., 216:94–115, 2019. [23] R. Kannan, S. Vempala, and A. Vetta. On clusterings: Good, bad and spectral. J. ACM, 51(3):497–515, 2004. [24] R. Kaur Bijral, I. Singh, J. Manhas, and V. Sharma. Discovery of egfr kinase's t790m variant inhibitors through molecular dynamics simulations, pcabased dimension reduction, and hierarchical clustering. Struct. Chem., 33(6):197–1964, 2022. [25] M. Khaled, A. Gorfe, and A. SayyedAhmad. Conformational and dynamical effects of tyr32 phosphorylation in kras: molecular dynamics simulation and markov state models analysis. J. Phys. Chem. B, 123(36):7667–7675, 2019. [26] S. Klus and N. Djurdjevac Conrad. Koopmanbased spectral clustering of directed and timeevolving graphs. Journal of Nonlinear Science, 33(1):8, 2023. [27] R. S. Knox and B. Q. Spring. Dipole strengths in the chlorophylls. Photochem. Photobiol., 77(5):497–501, 2003. [28] C. Kreisbeck and A. AspuruGuzik. Efficiency of energy funneling in the photosystem ii supercomplex of higher plants. Chem. Sci., 7(7):4174–4183, 2016. [29] F. Liu, D. Choi, L. Xie, and K. Roeder. Global spectral clustering in dynamic networks. Proc. Natl. Acad. Sci. U.S.A., 115(5):927–932, 2018. [30] T. Lu and F. Chen. Multiwfn: a multifunctional wavefunction analyzer. J. Comput. Chem., 33(5):580–592, 2012. [31] M. Madjet, A. Abdurahman, and T. Renger. Intermolecular coulomb couplings from ab initio electrostatic potentials: application to optical transitions of strongly coupled pigments in photosynthetic antennae and reaction centers. J. Phys. Chem. B, 110(34):17268–17281, 2006. [32] F. Müh, D. Lindorfer, M. S. am Busch, and T. Renger. Towards a structurebased exciton hamiltonian for the cp29 antenna of photosystem ii. Phys. Chem. Chem. Phys., 16(24):11848–11863, 2014. [33] K. Murota and A. Shioura. Dijkstra's algorithm and lconcave function maximization. Mathematical Programming, 145:163–177, 2014. [34] Z. Neal. Making big communities small: Using network science to understand the ecological and behavioral requirements for community social capital. Am. J. Community Psychol., 55:369–380, 2015. [35] M. E. Newman. Analysis of weighted networks. Physical review E, 70(5):056131, 2004. [36] W. G. Noid. Perspective: Coarsegrained models for biomolecular systems. J. Chem. Phys., 139(9), 2013. [37] P. S. Peixoto, D. Marcondes, C. Peixoto, and S. M. Oliva. Modeling future spread of infections via mobile geolocation data and population dynamics. an application to covid19 in brazil. PloS one, 15(7):e0235732, 2020. [38] M. Piraveenan, M. Prokopenko, and L. Hossain. Percolation centrality: Quantifying graphtheoretic impact of nodes during percolation in networks. PLoS One, 8(1):e53095, 2013. [39] G. Raszewski and T. Renger. Light harvesting in photosystem ii core complexes is limited by the transfer to the trap: can the core complex turn into a photoprotective mode? J. Am. Chem. Soc., 130(13):4431–4446, 2008. [40] T. Renger. Theory of excitation energy transfer: from structure to function. Photosynth. Res., 102:471–485, 2009. [41] T. Renger, I. Trostmann, C. Theiss, M. Madjet, M. Richter, H. Paulsen, H. Eichler, A. Knorr, and G. Renger. Refinement of a structural model of a pigmentprotein complex by accurate optical line shape theory and experiments. J. Phys. Chem. B, 111(35):10487–10501, 2007. [42] G. D. Scholes, C. Curutchet, B. Mennucci, R. Cammi, and J. Tomasi. How solvent controls electronic energy transfer and light harvesting. J. Phys. Chem. B, 111(25):6978–6982, 2007. [43] J. Shao, S. W. Tanner, N. Thompson, and T. E. Cheatham. Clustering molecular dynamics trajectories: 1. characterizing the performance of different clustering algorithms. J. Chem. Theory Comput., 3(6):2312–2334, 2007. [44] J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8):888–905, 2000. [45] O. Sorenson, J. W. Rivkin, and L. Fleming. Complexity, networks and knowledge flow. Res Policy, 35(7):994–1017, 2006. [46] A. Stirbet, D. Lazár, Y. Guo, and G. Govindjee. Photosynthesis: basics, history and modelling. Ann. Bot., 126(4):511–537, 2020. [47] S. H. Strogatz. Exploring complex networks. Nature, 410(6825):268–276, 2001. [48] X. Su, J. Ma, X. Wei, P. Cao, D. Zhu, W. Chang, Z. Liu, X. Zhang, and M. Li. Structure and assembly mechanism of plant c2s2m2type psii-lhcii supercomplex. Science, 357(6353):815–820, 2017. [49] L.M. Tan, J. Yu, T. Kawakami, M. Kobayashi, P. Wang, Z.Y. WangOtomo, and J.P. Zhang. New insights into the mechanism of uphill excitation energy transfer from core antenna to reaction center in purple photosynthetic bacteria. J. Phys. Chem. Lett., 9(12):3278–3284, 2018. [50] Q. K. Telesford, K. E. Joyce, S. Hayasaka, J. H. Burdette, and P. J. Laurienti. The ubiquity of smallworld networks. Brain Connect., 1(5):367–375, 2011. [51] W. G. Underwood, A. Elliott, and M. Cucuringu. Motifbased spectral clustering of weighted directed networks. Appl. Netw. Sci., 5(1):1–41, 2020. [52] H. Van Lierde, T. W. Chow, and J.C. Delvenne. Spectral clustering algorithms for the detection of clusters in blockcyclic and blockacyclic graphs. J. Complex Netw., 7(1):1–53, 2019. [53] U. Von Luxburg. A tutorial on spectral clustering. Stat Comput, 17:395–416, 2007. [54] D. J. Watts and S. H. Strogatz. Collective dynamics of `smallworld'networks. Nature, 393(6684):440–442, 1998. 85 [55] J. Zhang and T. Lu. Efficient evaluation of electrostatic potential with computerized optimized code. Phys. Chem. Chem. Phys, 23(36):20323–20328, 2021. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88409 | - |
dc.description.abstract | 光系統II利用約300個葉綠素來捕獲陽光能量並將其從其天線傳遞至反應中心。這個光收穫過程通過複雜的激發能量轉網絡實現了卓越的效率。我們建立了一個有效的耗散性 Frenkel 激子模型來描述光系統II中的激發能傳遞網絡並應用網絡分析方法來研究激發能傳遞動力學的特性。具體而言,度分佈和小世界性顯示量子離域增強了激發能傳遞網絡的韌性並促進了能量傳遞至反應中心。儘管我們能夠闡明整個光系統II激發能傳遞網絡的特性,然而完整的激發能傳遞網絡過於複雜,無法對光系統II中實現光收集的高量子效率的因素產生有用的見解。為了闡明光系統II激發能傳遞動力學,我們分別採用了分層聚類、最小割、k-均值和譜聚類等方法構建了光系統II激發能傳遞動力學的粗顆粒模型。發現最小割方法能夠產生最佳的粗顆粒模型,準確地描述光系統II激發能傳遞動力學並提供亞單位之間能量流的瓶頸。我們的研究表明,光系統II激發能傳遞網絡中的能量流瓶頸並不總是在子單位的邊界處。簇間的能量傳輸速率較慢約束了光系統II中激子的隨機行走,促進了激發能傳遞至反應中心的發生。此外,負責激發能傳遞的激子態並不總是簇內的最低能量態。這種設計在每一步傳輸中最大程度地減少了能量浪費,從而實現了高效的光收穫。 | zh_TW |
dc.description.abstract | The photosystem II supercomplex (PSII) utilizes ∼300 chlorophylls to capture sunlight energy and transfer it from its antennas to the reaction center. This light harvesting process exhibits remarkable efficiency achieved by a complex excitation energy transfer (EET) network. We have constructed an effective dissipative Frenkel exciton model to describe the EET network in the PSII and applied the network analysis methods to investigate the characteristics of the EET dynamics. Specifically, the degree distribution and small-worldness show that the quantum delocalization enhances the robustness of the EET network and facilitates the EET to the reaction center. Although we can elucidate the characteristics of the entire PSII EET network, the full EET network is too complex to yield useful insights on factors enabling the high quantum efficiency of light harvesting in PSII. To elucidate the PSII EET dynamics, we constructed coarse-grained models of PSII EET dynamics using hierarchical clustering, minimum-cut, k-means, and spectral clustering methods, respectively. The minimum-cut approach was found to yield the best coarse-grained model to accurately describe the PSII EET dynamics and provide the bottlenecks of energy flow between subunits. Our study shows that the bottlenecks of energy flow in the PSII EET network are not always at the boundaries of subunits. The slow energy transfer rate between clusters constrains the random walk of the excitons in PSII, facilitating the EET to RC. In addition, the exciton states responsible for EET are not always the lowest-energy states within a cluster. This design minimizes energy waste during each transfer step, resulting in high efficiency of light harvesting. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T16:09:57Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-15T16:09:57Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Verification Letter from the Oral Examination Committee . . . . . . . . . . . .i
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .v Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Figures xiii Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 1.1 Brief introduction to networks . . . . . . . . . . . . . . . . . . . . . . .1 1.2 Complex networks in PSII . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 The study of PSII EET dynamics in the network representation . . . . . . . .3 1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2 The complex networks in the PSII . . . . . . . . . . . . . . . . . . .7 2.1 J2 network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.1 Effective Hamiltonian of PSII . . . . . . . . . . . . . . . . . . . . 8 2.1.2 Construction of J2 network . . . . . . . . . . . . . . . . . . . . . . 10 2.2 PSII EET network . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Network measures for three PSII complex networks . . . . . . . . . 14 2.3.1 Degree centrality . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.2 Small-world network . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Chapter 3 The impact of the delocalization on PSII EET network . . . . . . . 25 3.1 Exciton delocalization in PSII EET network . . . . . . . . . . . . . . 26 3.2 A degree study to the delocalization of PSII EET network . . . . . . 27 3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Chapter 4 Methods for constructing the CG models 35 4.1 Mean absolute error . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2 Hierarchical clustering using simple cutoff rate . . . . . . . . . . . . 39 4.3 Clustering based on a minimum-cut tree . . . . . . . . . . . . . . . . 40 4.3.1 Constructing the minimum-cut tree . . . . . . . . . . . . . . . . . . 40 4.3.2 Size-constraint clustering based on a minimum-cut tree . . . . . . . 42 4.4 k-means. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5 Spectral clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.6 summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Chapter 5 Studying PSII EET dynamics by coarse-grained models. . . . . . . 55 5.1 The analysis of CG models from different methods . . . . . . . . . . 56 5.1.1 CG models of hierarchical clustering . . . . . . . . . . . . . . . . . 56 5.1.2 CG models of minimum-cut approaches . . . . . . . . . . . . . . . 59 5.1.3 CG models of k-means . . . . . . . . . . . . . . . . . . . . . . . . 64 5.1.4 CG models of spectral clustering . . . . . . . . . . . . . . . . . . . 65 5.2 The 17-cluster model of PSII EET network . . . . . . . . . . . . . . 70 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Chapter 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81 Appendix A — Other network measures for three complex networks . . . . .89 A .1 Eigenvector centrality . . . . . . . . . . . . . . . . . . . . . . . . . 89 A .2 Closeness centrality . . . . . . . . . . . . . . . . . . . . . . . . . . 91 A .2.1 Betweenness centrality . . . . . . . . . . . . . . . . . . . . . . . . 93 A .3 Percolation centrality . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Appendix B — CG models and dynamics . . . . . . . . . . . . . . . . . . . . . .97 | - |
dc.language.iso | en | - |
dc.title | 利用網絡分析和粗粒度模型闡明光系統II中捕光動力學 | zh_TW |
dc.title | Elucidating Dynamics of Light Harvesting in Photosystem II Using Network Analysis and Coarse-Grained Models | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 金必耀;許昭萍 | zh_TW |
dc.contributor.oralexamcommittee | Bih-Yaw Jin;Chao-Ping Hsu | en |
dc.subject.keyword | 光捕獲,網路分析,聚類, | zh_TW |
dc.subject.keyword | Light harvesting,Network analysis,Clustering, | en |
dc.relation.page | 106 | - |
dc.identifier.doi | 10.6342/NTU202301671 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-08-01 | - |
dc.contributor.author-college | 理學院 | - |
dc.contributor.author-dept | 化學系 | - |
顯示於系所單位: | 化學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 授權僅限NTU校內IP使用(校園外請利用VPN校外連線服務) | 20.5 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。