Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
166 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Stability analysis for large-scale multi-agent molecular communication systems (2311.06730v2)

Published 12 Nov 2023 in eess.SY, cs.IT, cs.SY, math.IT, and q-bio.MN

Abstract: Molecular communication (MC) is recently featured as a novel communication tool to connect individual biological nanorobots. It is expected that a large number of nanorobots can form large multi-agent MC systems through MC to accomplish complex and large-scale tasks that cannot be achieved by a single nanorobot. However, most previous models for MC systems assume a unidirectional diffusion communication channel and cannot capture the feedback between each nanorobot, which is important for multi-agent MC systems. In this paper, we introduce a system theoretic model for large-scale multi-agent MC systems using transfer functions, and then propose a method to analyze the stability for multi-agent MC systems. The proposed method decomposes the multi-agent MC system into multiple single-input and single-output (SISO) systems, which facilitates the application of simple analysis technique for SISO systems to the large-scale multi-agent MC system. Finally, we demonstrate the proposed method by analyzing the stability of a specific large-scale multi-agent MC system and clarify a parameter region to synchronize the states of nanorobots, which is important to make cooperative behaviors at a population level.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. T. Suda and T. Nakano, “Molecular communication : a personal perspective,” IEEE Trans. Nanobiosci., vol. 17, no. 4, pp. 424–432, 2018.
  2. D. Bi, A. Almpanis, A. Noel, Y. Deng, and R. Schober, “A Survey of Molecular Communication in Cell Biology: Establishing a New Hierarchy for Interdisciplinary Applications,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1494–1545, 2021.
  3. C. A. Söldner, E. Socher, V. Jamali, W. Wicke, G. S. Member, A. Ahmadzadeh, H.-g. Breitinger, A. Burkovski, K. Castiglione, R. Schober, and H. Sticht, “A Survey of Biological Building Blocks for Synthetic Molecular Communication Systems,” IEEE Commun. Surveys Tuts., vol. 22, no. 4, pp. 2765–2800, 2020.
  4. S. Lotter, L. Brand, V. Jamali, M. Schäfer, H. M. Loos, H. Unterweger, S. Greiner, J. Kirchner, C. Alexiou, D. Drummer, G. Fischer, A. Buettner, and R. Schober, “Experimental research in synthetic molecular communications – part ii,” IEEE Nanotechnol. Mag., vol. 17, no. 3, pp. 54–65, 2023.
  5. N. Farsad, H. B. Yilmaz, A. Eckford, C. B. Chae, and W. Guo, “A comprehensive survey of recent advancements in molecular communication,” IEEE Commun. Surveys Tuts., vol. 18, no. 3, pp. 1887–1919, 2016.
  6. M. Femminella, G. Reali, and A. V. Vasilakos, “Molecular communications model for drug delivery,” IEEE Trans. Nanobiosci., vol. 14, no. 7, pp. 935–945, 2015.
  7. W. Gao and J. Wang, “Synthetic micro/nanomotors in drug delivery,” Nanoscale, vol. 6, pp. 10 486–10 494, 2014.
  8. M. Pierobon and I. Akyildiz, “A physical end-to-end model for molecular communication in nanonetworks,” IEEE J. Select. Areas Commun., vol. 28, no. 4, pp. 602–611, 2010.
  9. U. A. Chude-Okonkwo, R. Malekian, and B. T. Maharaj, “Diffusion-controlled interface kinetics-inclusive system-theoretic propagation models for molecular communication systems,” Eurasip J. Adv. Signal Process., vol. 2015, no. 1, pp. 1–23, 2015.
  10. Y. Huang, F. Ji, Z. Wei, M. Wen, X. Chen, Y. Tang, and W. Guo, “Frequency Domain Analysis and Equalization for Molecular Communication,” IEEE Trans. Signal Processing, vol. 69, pp. 1952–1967, 2021.
  11. S. Lotter, A. Ahmadzadeh, and R. Schober, “Channel modeling for synaptic molecular communication with re-uptake and reversible receptor binding,” in IEEE Int. Conf. Commun., 2020, pp. 1–7.
  12. M. Schäfer, W. Wicke, W. Haselmayr, R. Rabenstein, and R. Schober, “Spherical diffusion model with semi-permeable boundary: A transfer function approach,” in IEEE Int. Conf. Commun., 2020, pp. 1–7.
  13. B. C. Akdeniz, A. E. Pusane, and T. Tugcu, “2-d channel transfer function for molecular communication with an absorbing receiver,” in IEEE Int. Conf. Intell. Comput. Commun. Processing.   IEEE, jul 2017, pp. 938–942.
  14. S. Hara, T. Kotsuka, and Y. Hori, “Modeling and stability analysis for multi-agent molecular communication systems : a case study for two agents,” in Proc. SICE Annual Conf., 2021, pp. 659–662.
  15. T. Kotsuka and Y. Hori, “Spatial Frequency-Based Characterization of Disturbance Rejection in Molecular Communication Systems,” IEEE Trans. Mol. Biol. Multi-Scale Commun., vol. 8, no. 1, pp. 36–43, 2022.
  16. Y. Hori, H. Miyazako, S. Kumagai, and S. Hara, “Coordinated spatial pattern formation in biomolecular communication networks,” IEEE Trans. Mol. Biol. Multi-Scale Commun., vol. 6, no. 2, pp. 111–121, 2015.
  17. J. Hsia, W. J. Holtz, D. C. Huang, M. Arcak, and M. M. Maharbiz, “A Feedback Quenched Oscillator Produces Turing Patterning with One Diffuser,” PLoS Comput. Biol., vol. 8, no. 1, p. e1002331, 2012.
  18. K. Kashima, T. Ogawa, and T. Sakurai, “Selective pattern formation control: Spatial spectrum consensus and Turing instability approach,” Automatica, vol. 56, pp. 25–35, jun 2015.
  19. J. Qin, Q. Ma, Y. Shi, and L. Wang, “Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey,” IEEE Trans. Ind. Electron., vol. 64, no. 6, pp. 4972–4983, jun 2017.
  20. Y. Cao, W. Yu, W. Ren, and G. Chen, “An overview of recent progress in the study of distributed multi-agent coordination,” IEEE Trans. Industr. Inform., vol. 9, no. 1, pp. 427–438, 2013.
  21. R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and Cooperation in Networked Multi-Agent Systems,” Proc. IEEE, vol. 95, no. 1, pp. 215–233, jan 2007.
  22. T. Kotsuka and Y. Hori, “A control-theoretic model for bidirectional molecular communication systems,” IEEE Trans. Mol. Biol. Multi-Scale Commun., pp. 1–1, 2023.
  23. S. Hara, T. Hayakawa, and H. Sugata, “LTI Systems with Generalized Frequency Variables: A Unified Framework for Homogeneous Multi-agent Dynamical Systems,” SICE journal of control, measurement, and system integration, vol. 2, no. 5, pp. 299–306, 2009.
  24. A. M. Turing, “The chemical basis of morphogenesis,” Philos. Trans. R. Soc. Lond., B, Biol. Sci., vol. 237, no. 641, pp. 37–72, aug 1952.
  25. T. Danino, O. Mondragón-Palomino, L. Tsimring, and J. Hasty, “A synchronized quorum of genetic clocks,” Nature, vol. 463, no. 7279, p. 326, 2010.
  26. P. Du, H. Zhao, H. Zhang, R. Wang, J. Huang, Y. Tian, X. Luo, X. Luo, M. Wang, Y. Xiang, L. Qian, Y. Chen, Y. Tao, and C. Lou, “De novo design of an intercellular signaling toolbox for multi-channel cell–cell communication and biological computation,” Nat. Commun., vol. 11, no. 1, p. 4226, dec 2020.
  27. J. J. Collins, T. S. Gardner, and C. R. Cantor, “Construction of a genetic toggle switch in escherichia coli,” Nature, vol. 403, no. 6767, pp. 339–342, 2000.
  28. A. Burmeister and A. Grünberger, “Microfluidic cultivation and analysis tools for interaction studies of microbial co-cultures,” Current Opinion in Biotechnology, vol. 62, pp. 106–115, apr 2020. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0958166919300709
  29. S. Basu, Y. Gerchman, C. H. Collins, F. H. Arnold, and R. Weiss, “A synthetic multicellular system for programmed pattern formation,” Nature, vol. 434, no. 7037, pp. 1130–1134, 2005.
  30. Y. Hori, T.-h. Kim, and S. Hara, “Existence criteria of periodic oscillations in cyclic gene regulatory networks,” Automatica, vol. 47, no. 6, pp. 1203–1209, jun 2011.
  31. X. Li, J. Jin, X. Zhang, F. Xu, J. Zhong, Z. Yin, H. Qi, Z. Wang, and J. Shuai, “Quantifying the optimal strategy of population control of quorum sensing network in Escherichia coli,” NPJ Syst. Biol. Appl., vol. 7, no. 1, p. 35, sep 2021.
  32. C. M. Waters and B. L. Bassler, “Quorum sensing: cell-to-cell communication in bacteria,” Annu. Rev. Cell Dev. Biol., vol. 21, pp. 319–346, 2005.
  33. T.-h. Kim, Y. Hori, and S. Hara, “Robust stability analysis of gene–protein regulatory networks with cyclic activation–repression interconnections,” Syst. Control Lett., vol. 60, no. 6, pp. 373–382, jun 2011. [Online]. Available: http://dx.doi.org/10.1016/j.sysconle.2011.03.003https://linkinghub.elsevier.com/retrieve/pii/S0167691111000478

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now