A Computational Multiagent Model of Bioluminescent Bacteria for the Emergence of Self-Sustainable and Self-Maintaining Artificial Wireless Networks
Biology is a rich source of inspiration in designing digital artifacts capable of autonomous, cooperative and distributed behaviors. Particularly, conceptual links can be established between (1) communication networks and (2) colonies of bacteria that communicate using chemical molecules. The goal of this paper is to propose a computational multiagent model of an interspecies bacterial communication system, termed quorum sensing, and analyze its self-sustainability and its self-maintaining ability to cooperatively form artificial wireless networks. Specifically, we propose a bottom-up agent-based approach combined with Ordinary Differential Equations, which abstract the intracellular dynamics, such as a proposed metabolism model that serves as a basis underlying self-sustainable networks. Results show that artificial bacterial cells have regeneration abilities in the light of random cell death and selected area for cell death, and a metabolism allowing them to exploit their own produced energy to cooperate at the population level to exhibit near-optimal self-organizing light-producing behaviors. The resulting artificial networks display several beneficial properties and could be used for the emergence of resistant wireless network topologies without the use of overhead messages.
T. E. Gorochowski, “Agent-based modelling in synthetic biology,” Essays Biochem., vol. 60, no. 4, pp. 325–336, 2016.
B. L. Bassler, “How bacterial talk to each other: regulation of gene expression by quorum sensing,” Curr. Opin. Microbiol., vol. 2, no. 6, pp. 582–587, 1999.
K. O. Stanley and R. Miikkulainen, “A Taxonomy for Artificial Embryogeny,” Artif. Life, vol. 9, no. 2, pp. 93–130, 2003.
R. Doursat, H. Sayama, and O. Michel, “A review of morphogenetic engineering,” Nat. Comput., vol. 12, no. 4, pp. 517–535, 2013.
A. Chavoya and Y. Duthen, “A cell pattern generation model based on an extended artificial regulatory network,” Biosystems, vol. 94, no. 1–2, pp. 95–101, 2008.
S. Nichele, T. E. Glover, and G. Tufte, “Genotype Regulation by Self-modifying Instruction-Based Development on Cellular Automata,” Springer, Cham, 2016, pp. 14–25.
S. Majumdar and S. Mondal, “Conversation game: talking bacteria,” J. Cell Commun. Signal., vol. 10, no. 4, pp. 331–335, 2016.
F. Dressler and O. B. Akan, “A survey on bio-inspired networking,” Comput. Networks, vol. 54, no. 6, pp. 881–900, 2010.
N. Ouannes, N. Djedi, Y. Duthen, and H. Luga, “Emergent group behaviors from bacteria quorum sensing simulation,” in 21st AROB, 2016, pp. 62–67.
F. J. Romero-Campero and M. J. Pérez-Jiménez, “A Model of the Quorum Sensing System in Vibrio fischeri Using P Systems,” Artif. Life, vol. 14, no. 1, pp. 95–109, 2008.
B. E. Beckmann and P. K. Mckinley, “Evolving Quorum Sensing in Digital Organisms,” in GECCO’09 Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009, pp. 97–104.
P. Bechon and J.-J. Slotine, “Synchronization and quorum sensing in a swarm of humanoid robots,” arXiv Prepr. arXiv1205.2952, 2012.
W. Ji et al., “A Formalized Design Process for Bacterial Consortia That Perform Logic Computing,” PLoS One, vol. 8, no. 2, p. e57482, 2013.
Feng Tan and J.-J. Slotine, “A quorum sensing inspired algorithm for dynamic clustering,” in 52nd IEEE Conference on Decision and Control, 2013, pp. 5364–5370.
H. Shum and A. C. Balazs, “Synthetic quorum sensing in model microcapsule colonies,” Proc. Natl. Acad. Sci., vol. 114, no. 32, pp. 8475–8480, 2017.
B. Niu, H. Wang, Q. Duan, and L. Li, “Biomimicry of quorum sensing using bacterial lifecycle model,” BMC Bioinformatics, vol. 14, no. Suppl 8, p. S8, 2013.
D. A. Sofge and W. F. Lawless, “Quorum Sensing for Collective Action and Decision-Making in Mobile Autonomous Teams,” in ICAART (1), 2011, pp. 195–204.
A. C. Burgos and D. Polani, “Cooperation and antagonism in information exchange in a growth scenario with two species,” J. Theor. Biol., vol. 399, pp. 117–133, 2016.
J. W. Williams, X. Cui, A. Levchenko, and A. M. Stevens, “Robust and sensitive control of a quorum-sensing circuit by two interlocked feedback loops,” Mol. Syst. Biol., vol. 4, no. 1, p. 234, 2008.
P. Melke, P. Sahlin, A. Levchenko, and H. Jönsson, “A cell-based model for quorum sensing in heterogeneous bacterial colonies,” PLoS Comput. Biol., vol. 6, no. 6, p. e1000819, 2010.
G. Wei, C. Walsh, I. Cazan, and R. Marculescu, “Molecular tweeting: Unveiling the social network behind heterogeneous bacteria ions,” in BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2015, pp. 366–375.
A. A. Aziz, Y. A. Sekercioglu, P. Fitzpatrick, and M. Ivanovich, “A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks,” IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 121–144, 2013.
J. F. Miller, “Evolving Developmental Programs for Adaptation, Morphogenesis, and Self-Repair,” in European Conference on Artificial Life., 2004, pp. 256–265.
J. F. Miller, “Evolving a Self-Repairing, Self-Regulating, French Flag Organism,” in Genetic and Evolutionary Computation Conference, 2004, pp. 129–139.
H. Liu, J. F. Miller, and A. M. Tyrrell, “An intrinsic robust transient fault-tolerant developmental model for digital systems,” in Workshop on Regeneration and Learning in Developmental Systems, Genetic and Evolutionary Computation Conference., 2004.
K. Fleischer, “Investigations with a Multicellular Developmental Model,” in C. G. Langton & T. Shimohara (Eds.), Artificial Life V, 1996, pp. 229–236.
S. Cussat-Blanc, H. Luga, and Y. Duthen, “Cell2Organ: Self-repairing artificial creatures thanks to a healthy metabolism,” in 2009 IEEE Congress on Evolutionary Computation, CEC 2009, 2009, pp. 2708–2715.
N. Djezzar, N. Djedi, S. Cussat-Blanc, H. Luga, and Y. Duthen, “L-systems and artificial chemistry to develop digital organisms,” in 2011 IEEE Symposium on Artificial Life (ALIFE), 2011, pp. 225–232.
G. Ferreira, M. Smiley, M. Scheutz, and M. Levin, “Dynamic Structure Discovery and Repair for 3D Cell Assemblages,” in Proceedings of the Artificial Life Conference 2016, 2016, pp. 352–359.
L. Cobo, A. Quintero, and S. Pierre, “Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics,” Comput. Networks, vol. 54, no. 17, pp. 2991–3010, 2010.
A. Giagkos and M. S. Wilson, “Swarm intelligence to wireless ad hoc networks: adaptive honeybee foraging during communication sessions,” Adapt. Behav., vol. 21, no. 6, pp. 501–515, 2013.
I. Bojic, V. Podobnik, I. Ljubi, G. Jezic, and M. Kusek, “A self-optimizing mobile network: Auto-tuning the network with firefly-synchronized agents,” Inf. Sci. (Ny)., vol. 182, no. 1, pp. 77–92, 2012.
Z. G. Al-Mekhlafi, Z. M. Hanapi, M. Othman, and Z. A. Zukarnain, “A firefly-inspired scheme for energy-efficient transmission scheduling using a self-organizing method in a wireless sensor networks,” J. Comput. Sci., vol. 12, no. 10, pp. 482–494, 2016.
N. El Houda Bahloul, S. Boudjit, M. Abdennebi, and D. E. Boubiche, “A Flocking-Based on Demand Routing Protocol for Unmanned Aerial Vehicles,” J. Comput. Sci. Technol., vol. 33, no. 2, pp. 263–276, 2018.
J. Monod, “The Growth of Bacterial Cultures,” Annu. Rev. Microbiol., vol. 3, no. 1, pp. 371–394, 1949.
A. H. Stouthamer and C. Bettenhaussen, “Utilization of energy for growth and maintenance in continuous and batch cultures of microorganisms. A reevaluation of the method for the determination of ATP production by measuring molar growth yields,” BBA Reviews On Bioenergetics, vol. 301, no. 1. pp. 53–70, 1973.
S. A. L. M. Kooijman, Dynamic energy budget theory for metabolic organisation. Cambridge University Press, 2010.
A. I. Psarras and I. G. Karafyllidis, “Simulation of the Dynamics of Bacterial Quorum Sensing,” IEEE Trans. Nanobioscience, vol. 14, no. 4, pp. 440–446, Jun. 2015.
D. J. Sexton and M. Schuster, “Nutrient limitation determines the fitness of cheaters in bacterial siderophore cooperation,” Nat. Commun., vol. 8, no. 1, p. 230, 2017.
C. Moreno-Fenoll, M. Cavaliere, E. Martínez-García, and J. F. Poyatos, “Eco-evolutionary feedbacks can rescue cooperation in microbial populations,” Sci. Rep., vol. 7, p. 42561, 2017.
S. S. Jang, K. T. Oishi, R. G. Egbert, and E. Klavins, “Specification and Simulation of Synthetic Multicelled Behaviors,” ACS Synth. Biol., vol. 1, no. 8, pp. 365–374, 2012.
C. Anetzberger, T. Pirch, and K. Jung, “Heterogeneity in quorum sensing-regulated bioluminescence of Vibrio harveyi,” Mol. Microbiol., vol. 73, no. 2, pp. 267–277, 2009.
N. Ouannes, N. Djedi, H. Luga, and Y. Duthen, “Modeling a bacterial ecosystem through chemotaxis simulation of a single cell,” Artif. Life Robot., vol. 19, no. 4, pp. 382–387, 2014.
S. Forrest and T. Jones, “Modeling Complex Adaptive Systems with Echo,” in Complex Systems: Mechanisms of Adaptation, 1993, pp. 3–21.
S. Y. Queck et al., “RNAIII-independent target gene control by the agr quorum- sensing system: insight into the evolution of virulence regulation in Staphylococcus aureus,” Mol. Cell, vol. 32, no. 1, pp. 150–158, 2008.
Y.-C. Yong and J.-J. Zhong, “Impacts of Quorum Sensing on Microbial Metabolism and Human Health,” in Advances in biochemical engineering/biotechnology, vol. 131, 2012, pp. 25–61.
E. Goo, J. H. An, Y. Kang, and I. Hwang, “Control of bacterial metabolism by quorum sensing,” Trends in Microbiology, vol. 23, no. 9. 2015.
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