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  1. NTU Theses and Dissertations Repository
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96590
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dc.contributor.advisor陳和麟zh_TW
dc.contributor.advisorHo-Lin Chenen
dc.contributor.author李怡萱zh_TW
dc.contributor.authorYi-Xuan Leeen
dc.date.accessioned2025-02-19T16:39:58Z-
dc.date.available2025-02-20-
dc.date.copyright2025-02-19-
dc.date.issued2024-
dc.date.submitted2024-12-17-
dc.identifier.citation[1] L. Adleman, Q. Cheng, A. Goel, and M.-D. Huang. Running time and program size for self-assembled squares. In Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing, page 740–748, 2001.
[2] R. M. Alaniz, J. Brunner, M. Coulombe, E. D. Demaine, J. Diomidova, R. Knobel, T. Gomez, E. Grizzell, J. Lynch, A. Rodriguez, R. Schweller, and T. Wylie. Com- plexity of reconfiguration in surface chemical reaction networks, 2023.
[3] R. M. Alaniz, D. Caballero, S. C. Cirlos, T. Gomez, E. Grizzell, A. Rodriguez, R. Schweller, A. Tenorio, and T. Wylie. Building squares with optimal state com- plexity in restricted active self-assembly. Journal of Computer and System Sciences, 138:103462, 2023.
[4] J. C. Alumbaugh, J. J. Daymude, E. D. Demaine, M. J. Patitz, and A. W. Richa. Simulation of programmable matter systems using active tile-based self-assembly. In DNA Computing and Molecular Programming, pages 140–158, 2019.
[5] D. Angluin, J. Aspnes, M. J. Fischer, and H. Jiang. Self-stabilizing population pro- tocols. ACM Trans. Auton. Adapt. Syst., 3(4), 12 2008.
[6] D. Angluin, J. Aspnes, M. J. Fischer, and H. Jiang. Self-stabilizing population pro- tocols. ACM Trans. Auton. Adapt. Syst., 3(4), dec 2008.
[7] F. Becker, D. Maldonado, N. Ollinger, and G. Theyssier. Universality in freezing cellular automata. In F. Manea, R. G. Miller, and D. Nowotka, editors, Sailing Routes in the World of Computation, pages 50–59, Cham, 2018. Springer International Pub- lishing.
[8] K. Bhattacharjee, N. Naskar, S. Roy, and S. Das. A survey of cellular automata: Types, dynamics, non-uniformity and applications. Natural Computing, 19:433– 461, 06 2020.
[9] T. Brailovskaya, G. Gowri, S. Yu, and E. Winfree. Reversible computation using swap reactions on a surface. In DNA Computing and Molecular Programming, pages 174–196, 2019.
[10] D. Caballero, T. Gomez, R. Schweller, and T. Wylie. Verification and computation in restricted tile automata. Natural Computing, pages 1–19, 2021.
[11] C. Chalk, A. Luchsinger, E. Martinez, R. Schweller, A. Winslow, and T. Wylie. Freezing simulates non-freezing tile automata. In DNA Computing and Molecular Programming, pages 155–172, 2018.
[12] H. L. Chen, D. Doty, and D. Soloveichik. Deterministic function computation with chemical reaction networks. Natural Computing, 13(4):517–534, 2014.
[13] H.L.Chen,D.Doty,D.Soloveichik,andW.Reeves.Rate-independentcomputation in continuous chemical reaction networks. Journal of the ACM, 70(3):1–61, 2023.
[14] S.Clamons,L.Qian,andE.Winfree.Programmingandsimulatingchemicalreaction networks on a surface. Journal of The Royal Society Interface, 17:20190790, 2020.
[15] M. Cook, D. Soloveichik, E. Winfree, and J. Bruck. Programmability of Chemical Reaction Networks, pages 543–584. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.
[16] A. Dennunzio, E. Formenti, and L. Manzoni. Computing issues of asynchronous ca. Fundam. Inf., 120(2):165–180, 2012.
[17] Z. Derakhshandeh, S. Dolev, R. Gmyr, A. W. Richa, C. Scheideler, and T. Stroth- mann. Amoebot - a new model for programmable matter. In Proceedings of the 26th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA ’14, page 220–222, New York, NY, USA, 2014. Association for Computing Machinery.
[18] D. Doty, J. H. Lutz, M. J. Patitz, R. T. Schweller, S. M. Summers, and D. Woods. The tile assembly model is intrinsically universal. In IEEE 54th Annual Symposium on Foundations of Computer Science, pages 302–310, 2012.
[19] F. Fages, G. Le Guludec, O. Bournez, and A. Pouly. Strong turing completeness of continuous chemical reaction networks and compilation of mixed analog-digital programs. In J. Feret and H. Koeppl, editors, Computational Methods in Systems Biology, pages 108–127, Cham, 2017. Springer International Publishing.
[20] N. Fatès and L. Gerin. Examples of fast and slow convergence of 2d asynchronous cellular systems. In Cellular Automata, pages 184–191, 2008.
[21] E. Goles, D. Maldonado, P. Montealegre, and M. Ríos-Wilson. On the complex- ity of asynchronous freezing cellular automata. Information and Computation, 281:104764, 2021.
[22] E. Goles, N. Ollinger, and G. Theyssier. Introducing Freezing Cellular Automata. In Cellular Automata and Discrete Complex Systems, 21st International Workshop (AUTOMATA 2015), volume 24 of TUCS Lecture Notes, pages 65–73, 2015.
[23] D.HaderandM.J.Patitz.Theimpactsofdimensionality,diffusion,anddirectedness on intrinsic cross-model simulation in tile-based self-assembly, 2023.
[24] M. J. Patitz. An introduction to tile-based self-assembly. In J. Durand-Lose and N. Jonoska, editors, Unconventional Computation and Natural Computation, pages 34–62, 2012.
[25] L.QianandE.Winfree.Parallelandscalablecomputationandspatialdynamicswith dna-based chemical reaction networks on a surface. In S. Murata and S. Kobayashi, editors, DNA Computing and Molecular Programming, pages 114–131, Cham, 2014. Springer International Publishing.
[26] P. Rothemund and E. Winfree. The program-size complexity of self-assembled squares. In Proceedings of the Annual ACM Symposium on Theory of Computing, pages 459–468, 2000.
[27] D.Soloveichik,M.Cook,E.Winfree,andJ.Bruck.Computationwithfinitestochas- tic chemical reaction networks. Natural Computing: An International Journal, 7(4):615–633, dec 2008.
[28] J. von Neumann. Theory of self-reproducing automata, 1966. https://cba.mit. edu/events/03.11.ASE/docs/VonNeumann.pdf.
[29] E. Winfree. Algorithmic self-assembly of dna. In Proceedings of the International Conference on Microtechnologies in Medicine and Biology, pages 4–4, 2006.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96590-
dc.description.abstract化學反應網路(CRN)是一個被廣泛研究的模型,描述混合溶液中分子的相互作用。2014年,Qian和 Winfree提出了表面化學反應網路模型(sCRN),將分子置於平面圖的節點上,達到利用空間限制分子間相互作用的效果。在此模型裡,任何分子僅能與相鄰的分子進行反應。許多後續的研究著重在sCRN的計算能力並探討分子所能長成的圖案。
為了刻畫sCRN所能完成的事情,我們將此模型與其他已被廣泛研究的分子及自我組合系統做了連結。我們證明只要給定相同的初始條件,sCRN、親合力增強磁磚自動機、細胞自動機,及阿米巴機器人都可以互相模擬(經過一些必要的旋轉和翻轉)。此外,我們也考慮了凍結系統之間的模擬,在凍結限制下任意節點的狀態都只能依照某種篇序成長。特別地,我們引入凍結sCRN,並證明了凍結親合力增強磁磚自動機及凍結細胞自動機可以被凍結sCRN模擬(經過必要的旋 轉和翻轉)。其中一個我們使用的技巧是即時著色,透過即時著色我們能賦予分子對於方向的共識。
zh_TW
dc.description.abstractThe Chemical Reaction Network (CRN) is a well-studied model that describes the interaction of molecules in well-mixed solutions. In 2014, Qian and Winfree proposed the abstract surface chemical reaction network model (sCRN), which takes advantage of spatial separation by placing molecules on a structured surface, limiting the interaction between molecules. In this model, molecules can only react with their immediate neighbors. Many follow-up works study the computational and pattern-construction power of sCRNs.

In this work, our goal is to describe the power of sCRN by relating the model to other well-studied models in distributed computation. We show that given the same initial configuration, sCRN, affinity-strengthening tile automata, cellular automata, and amoebot can all simulate each other (up to unavoidable rotation and reflection of the pattern). As an extension, we also consider the simulation between freezing systems where the state in any fixed position can only increase according to some partial order. In particular, we introduce freezing sCRN and show that freezing affinity-strengthening tile automata and the freezing cellular automata can be simulated by freezing sCRN (up to unavoidable rotation and reflection of the pattern). One of our techniques is coloring on-the-fly, which allows all molecules in sCRN to have a global orientation.
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dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-19T16:39:58Z
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dc.description.provenanceMade available in DSpace on 2025-02-19T16:39:58Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontentsAcknowledgements iii
摘要 v
Abstract vii
Contents ix
List of Figures xiii
List of Tables xv
Chapter 1 Introduction 1
1.1 Our results 3
Chapter 2 Models 5
2.1 Surface chemical reaction networks 5
2.1.1 Surface chemical reaction network with orientation 6
2.1.2 Reachabilityandtermination 7
2.2 Abstract tile assembly model 8
2.3 Cellular automata 10
2.4 Tile automata 12
2.5 Amoebot 14
2.6 Freezing system 17
Chapter 3 Simulation 19
3.1 Representation function 19
3.2 Simulation definition 20
3.3 Notations 22
Chapter 4 Simulation of unit-seeded directed sCRN by unit-seeded sCRN (up to rotation and reflection) 23
4.1 Simulation overview 23
4.2 Protocol description 25
4.3 Proof 37
4.3.1 Gamma follows Gamma’ 37
4.3.2 Gamma’models Gamma 39
4.3.3 Gamma’ and Gamma have equivalent productions 45
4.4 Complexity 46
Chapter 5 Simulate aTAM by unit-seeded directed sCRN 49
5.1 Simulation overview 49
5.2 Protocols 51
5.3 Proof 54
5.4 Complexity 55
Chapter 6 Simulation between unit-seeded TA with affinity-strengthening rule and unit-seeded directed sCRN 57
6.1 Simulation overview 57
6.2 Protocols 58
6.3 Proof 60
6.4 Complexity 61
Chapter 7 Simulation between non-deterministic async-CA and directed sCRN 63
7.1 Simulate non-deterministic async-CA by directed sCRN 63
7.1.1 Simulation overview 64
7.1.2 Protocols 66
7.1.3 Proof 70
7.1.4 Complexity 72
7.2 Simulate directed sCRN by non-deterministic async-CA 72
7.2.1 Simulation overview 72
7.2.2 Protocols 74
7.2.3 Proof 76
7.2.4 Complexity 77
Chapter 8 Simulation between amoebot and clockwise sCRN 79
8.1 Simulating amoebot by clockwise sCRN 79
8.1.1 Simulation overview 79
8.1.2 Encoding particles 81
8.1.3 Observing flags 82
8.1.4 Movements 84
8.1.5 Proof 88
8.1.6 Complexity 89
8.2 Simulate clockwise sCRN by amoebot 90
8.2.1 Simulation overview 90
Chapter 9 Simulate unit-seeded, freezing TA with affinity-strengthening rule
by unit-seeded, freezing directed sCRN 91
9.1 Simulationoverview 93
9.2 Protocol 94
9.3 Complexity 97
Chapter 10 Simulate freezing async-CA by freezing directed sCRN 99
10.1 Simulation overview 100
10.2 Protocol 102
10.3 Proof 108
10.4 Complexity 114
Chapter 11 Conclusion 115
References 117
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dc.language.isoen-
dc.subject細胞自動機zh_TW
dc.subject磁磚自動機zh_TW
dc.subject模擬zh_TW
dc.subject表面化學反應網路zh_TW
dc.subjectCellular automataen
dc.subjectSurface chemical reaction networksen
dc.subjectSimulationen
dc.subjectTile automataen
dc.title表面化學反應網路的模擬能力zh_TW
dc.titleOn the Simulation Power of Surface Chemical Reaction Networksen
dc.typeThesis-
dc.date.schoolyear113-1-
dc.description.degree碩士-
dc.contributor.oralexamcommittee于天立;呂學一;蔡孟宗zh_TW
dc.contributor.oralexamcommitteeTian-Li Yu;Hsueh-I Lu;Meng-Tsung Tsaien
dc.subject.keyword表面化學反應網路,模擬,磁磚自動機,細胞自動機,zh_TW
dc.subject.keywordSurface chemical reaction networks,Simulation,Tile automata,Cellular automata,en
dc.relation.page120-
dc.identifier.doi10.6342/NTU202401564-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2024-12-17-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept電機工程學系-
dc.date.embargo-lift2025-02-20-
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