survey

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Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey

Authors:

Sajal K. DasAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 8

Article No.: 166, Pages 1 - 36

https://doi.org/10.1145/3468266

Published: 04 October 2021 Publication History

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Abstract

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

References

[1]

J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami. 2013. Internet of things (IoT): A vision, architectural elements, and future directions. Fut. Gener. Comput. Syst. 29, 7 (2013), 1645–1660.

Digital Library

[2]

W. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed. 2019. Edge computing: A survey. Fut. Gener. Comput. Syst. 97 (2019), 219–235.

Digital Library

[3]

J. Yick, B. Mukherjee, and D. Ghosal. 2008. Wireless sensor network survey. Comput. Netw. 52, 12 (2008), 2292–2330.

Digital Library

[4]

V. K. Shah, S. Roy, S. Silvestri, and S. K. Das. 2017. Ctr: Cluster based topological routing for disaster response networks. In Proceedings of the 2017 IEEE International Conference on Communications (ICC'17). IEEE, 1–6.

[5]

M. Wazid, A. K. Das, V. Odelu, N. Kumar, M. Conti, and M. Jo. 2017. Design of secure user authenticated key management protocol for generic iot networks. IEEE IoT J. 5, 1 (2017), 269–282.

[6]

J. Barriga, J. Sulca, J. León, A. Ulloa, D. Portero, R. Andrade, and S. Yoo. 2019. Smart Parking: A literature review from the technological perspective. Appl. Sci. 9, 21 (2019), 4569.

[7]

F. Dressler and O. B. Akan. 2010. A survey on bio-inspired networking. Comput. Netw. 54, 6 (2010), 881–900.

Digital Library

[8]

C. Jones, K. Sivalingam, P. Agrawal, and J. Chen. 2001. A survey of energy efficient network protocols for wireless networks. Wireless Netw. 7, 4 (2001), 343–358.

Digital Library

[9]

U. Aickelin, D. Dasgupta, and F. Gu. 2014. Artificial immune systems. In Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Edmund K. Burke and Graham Kendall (Eds.).

[10]

A. Fukushima, S. Kanaya, and K. Nishida. 2014. Integrated network analysis and effective tools in plant systems biology. Front. Plant Sci. 5 (2014), 598.

[11]

H. Hernández, C. Blum, and G. Francès. 2008. Ant colony optimization for energy-efficient broadcasting in ad-hoc networks. In Proceedings of the International Conference on Ant Colony Optimization and Swarm Intelligence. Springer, 25–36.

Digital Library

[12]

F. Dressler, I. Dietrich, R. German, and B. Krüger. 2009. A rule-based system for programming self-organized sensor and actor networks. Comput. Netw. 53, 10 (2009), 1737–1750.

Digital Library

[13]

V. Sevim and P. A. Rikvold. 2008. Chaotic gene regulatory networks can be robust against mutations and noise. J. Theor. Biol. 253, 2 (2008), 323–332.

[14]

N. Noman et al. 2015. Evolving robust gene regulatory networks. PLoS ONE 10, 1 (2015), e0116258.

[15]

Y. Du, J. Gong, Z. Wang, and N. Xu. 2018. A distributed energy-balanced topology control algorithm based on a noncooperative game for wireless sensor networks. Sensors 18, 12 (2018), 4454.

[16]

J. Sheu, S. Tu, and C. Hsu. 2008. Location-free topology control protocol in wireless ad hoc networks. Comput. Commun. 31, 14 (2008), 3410–3419.

Digital Library

[17]

S. Aluru. 2005. Handbook of Computational Molecular Biology. Chapman & Hall/CRC.

Digital Library

[18]

G. Karlebach and R. Shamir. 2008. Modelling and analysis of gene regulatory networks. Nat. Rev. Molec. Cell Biol. 9, 10 (2008), 770–780.

[19]

H. De Jong. 2002. Modeling and simulation of genetic regulatory systems: A literature review. J. Comput. Biol. 9, 1 (2002), 67–103.

[20]

P. Ghosh, M. Mayo, V. Chaitankar, T. Habib, E. Perkins, and S. K. Das. 2011. Principles of genomic robustness inspire fault-tolerant WSN topologies: A network science based case study. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops'11). IEEE, 160–165.

[21]

Y. Xiao. 2009. A tutorial on analysis and simulation of boolean gene regulatory network models. Curr. Genom. 10, 7 (2009), 511–525.

[22]

I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang. 2002. Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 2 (2002), 261–274.

[23]

Z. Ghahramani. 1997. Learning dynamic Bayesian networks. In International School on Neural Networks, Initiated by IIASS and EMFCSC. Springer, 168–197.

Digital Library

[24]

B. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. d'Alche Buc. 2003. Gene networks inference using dynamic Bayesian networks. Bioinformatics 19, suppl. 2 (2003), ii138–ii148.

[25]

V. Mihajlovic and M. Petkovic. 2001. Dynamic bayesian networks: A state of the art.

[26]

A. V. Werhli and D. Husmeier. 2007. Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Molec. Biol. 6, 1 (2007).

[27]

M. Newman. 2003. The structure and function of complex networks. SIAM Rev. 45, 2 (2003), 167–256.

Digital Library

[28]

Y. Fu, L. R. Jarboe, and J. A. Dickerson. 2011. Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities. BMC Bioinform. 12, 1 (2011), 233.

[29]

I. Pournara and L. Wernisch. 2007. Factor analysis for gene regulatory networks and transcription factor activity profiles. BMC Bioinform. 8, 1 (2007), 61.

[30]

T. Schaffter, D. Marbach, and D. Floreano. 2011. GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27, 16 (2011), 2263–2270.

Digital Library

[31]

H. Han, H. Shim, D. Shin, J. E. Shim, Y. Ko, J. Shin, H. Kim, A. Cho, E. Kim, T. Lee, et al. 2015. TRRUST: A reference database of human transcriptional regulatory interactions. Sci. Rep. 5 (2015), 11432.

[32]

H. Han et al. 2017. TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 46, D1 (2017), D380–D386.

[33]

S. Roy, V. K. Shah, and S.K. Das. 2016. Characterization of E. coli gene regulatory network and its topological enhancement by edge rewiring. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies. 391–398.

Digital Library

[34]

S. Roy, V. K. Shah, and S. K. Das. 2019. Design of robust and efficient topology using enhanced gene regulatory networks. IEEE Trans. Molec. Biol. Multi-Scale Commun. (2019).

[35]

M. B. Gerstein, A. Kundaje, M. Hariharan, S. G. Landt, K. Yan, C. Cheng, X. J. Mu, E. Khurana, J. Rozowsky, R. Alexander, et al. 2012. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 7414 (2012), 91.

[36]

N. Bhardwaj, P. M. Kim, and M. B. Gerstein. 2010. Rewiring of transcriptional regulatory networks: Hierarchy, rather than connectivity, better reflects the importance of regulators. Sci. Signal. 3, 146 (2010), ra79–ra79.

[37]

H. Ma, J. Buer, and A. Zeng. 2004. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach. BMC Bioinform. 5, 1 (2004), 199.

[38]

A. Barabási. 2009. Scale-free networks: A decade and beyond. Science 325, 5939 (2009), 412–413.

[39]

R. Albert. 2005. Scale-free networks in cell biology. J. Cell Sci. 118, 21 (2005), 4947–4957.

[40]

B. Kamapantula, A. Abdelzaher, M. Mayo, E. Perkins, S. Das, and P. Ghosh. 2017. Quantifying robustness in biological networks using NS-2. In Modeling, Methodologies and Tools for Molecular and Nano-scale Communications. Springer, 273–290.

[41]

R. D. Leclerc. 2008. Survival of the sparsest: Robust gene networks are parsimonious. Molec. Syst. Biol. 4, 1 (2008), 213.

[42]

Q. K. Telesford, K. E. Joyce, S. Hayasaka, J. H. Burdette, and P. J. Laurienti. 2011. The ubiquity of small-world networks. Brain Connect. 1, 5 (2011), 367–375.

[43]

L. Gu, H. L. Huang, and X. D. Zhang. 2013. The clustering coefficient and the diameter of small-world networks. Acta Math. Sin. 29, 1 (2013), 199–208.

[44]

Luıs A. Nunes Amaral, Antonio Scala, Marc Barthelemy, and H. Eugene Stanley. 2000. Classes of small-world networks. Proc. Natl. Acad. Sci. U.S.A. 97, 21 (2000), 11149–11152.

[45]

T. Guo. 2014. Design of Genetic Regulatory Networks. Masters Thesis. Industrial Engineering, University of Illinois at Urbana-Champaign.

[46]

R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. Network motifs: simple building blocks of complex networks. Science 298, 5594 (2002), 824–827.

[47]

E. Wong, B. Baur, S. Quader, and C. Huang. 2011. Biological network motif detection: Principles and practice. Brief. Bioinform. 13, 2 (2011), 202–215.

[48]

U. Alon. 2007. Network motifs: Theory and experimental approaches. Nat. Rev. Genet. 8, 6 (2007), 450.

[49]

S. S. Shen-Orr, R. Milo, S. Mangan, and U. Alon. 2002. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31, 1 (2002), 64.

[50]

S. Mangan and U. Alon. 2003. Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. U.S.A. 100, 21 (2003), 11980–11985.

[51]

E. Estrada. 2012. The Structure of Complex Networks: Theory and Applications. Oxford University Press.

Digital Library

[52]

R. J. Prill, P. A. Iglesias, and A. Levchenko. 2005. Dynamic properties of network motifs contribute to biological network organization. PLoS Biol. 3, 11 (2005), e343.

[53]

T. I. Lee, N. J Rinaldi, F. Robert, D. T. Odom, Z. Bar-Joseph, G. K. Gerber, N. M. Hannett, C. T. Harbison, C. M. Thompson, I. Simon, et al. 2002. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 5594 (2002), 799–804.

[54]

N. Kashtan, S. Itzkovitz, R. Milo, and U. Alon. 2004. Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20, 11 (2004), 1746–1758.

Digital Library

[55]

F. Schreiber and H. Schwöbbermeyer. 2005. MAVisto: A tool for the exploration of network motifs. Bioinformatics 21, 17 (2005), 3572–3574.

Digital Library

[56]

S. Wernicke and F. Rasche. 2006. FANMOD: A tool for fast network motif detection. Bioinformatics 22, 9 (2006), 1152–1153.

Digital Library

[57]

B. D. McKay et al. Practical Graph Isomorphism.

Digital Library

[58]

M. Zuba. 2009. A comparative study of network motif detection tools. UConn Bio-Grid, REU Summer.

[59]

M. E. Wall, M. J. Dunlop, and W. S. Hlavacek. 2005. Multiple functions of a feed-forward-loop gene circuit. J. Molec. Biol. 349, 3 (2005), 501–514.

[60]

E. Alm and A. P. Arkin. 2003. Biological networks. Curr. Opin. Struct. Biol. 13, 2 (2003), 193–202.

[61]

M. Madan Babu, Nicholas M. Luscombe, L. Aravind, Mark Gerstein, and Sarah A. Teichmann. 2004. Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14, 3 (2004), 283–291.

[62]

O. Shoval and U. Alon. 2010. SnapShot: Network motifs. Cell 143, 2 (2010), 326–326.

[63]

A. Martínez-Antonio, S. C. Janga, and D. Thieffry. 2008. Functional organisation of Escherichia coli transcriptional regulatory network. J. Molec. Biol. 381, 1 (2008), 238–247.

[64]

R. Pinho, V. Garcia, M. Irimia, and M. W. Feldman. 2014. Stability depends on positive autoregulation in boolean gene regulatory networks. PLoS Comput. Biol. 10, 11 (2014), e1003916.

[65]

J. Gómez-Gardeñes and Y. Moreno. 2006. From scale-free to Erdos-Rényi networks. Phys. Rev. E 73, 5 (2006), 056124.

[66]

S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang. 2006. Complex networks: Structure and dynamics. Phys. Rep. 424, 4–5 (2006), 175–308.

[67]

M. Mayo, A. Abdelzaher, E. J. Perkins, and P. Ghosh. 2012. Motif participation by genes in E. coli transcriptional networks. Front. Physiol. 3 (2012), 357.

[68]

F. M. Camas and J. F. Poyatos. 2008. What determines the assembly of transcriptional network motifs in Escherichia coli?PLoS One 3, 11 (2008), e3657.

[69]

A. F. Abdelzaher, A. F. Al-Musawi, P. Ghosh, M. L. Mayo, and E. J. Perkins. 2015. Transcriptional network growing models using Motif-Based preferential attachment. Front. Bioeng. Biotechnol. 3 (2015), 157.

[70]

M. Aldana, E. Balleza, S. Kauffman, and O. Resendiz. 2007. Robustness and evolvability in genetic regulatory networks. J. Theor. Biol. 245, 3 (2007), 433–448.

[71]

S. Mizutaka and K. Yakubo. 2013. Structural robustness of scale-free networks against overload failures. Phys. Rev. E 88, 1 (2013), 012803.

[72]

G. Paul, T. Tanizawa, S. Havlin, and H. E. Stanley. 2004. Optimization of robustness of complex networks. Eur. Phys. J. B 38, 2 (2004), 187–191.

[73]

C. M. Schneider, A. A Moreira, J. Andrade, S. Havlin, and H. J. Herrmann. 2011. Mitigation of malicious attacks on networks. Proc. Natl Acad. Sci. U.S.A. 108, 10 (2011), 3838–3841.

[74]

H. Chan, L. Akoglu, and H. Tong. 2014. Make it or break it: Manipulating robustness in large networks. In Proceedings of the 2014 SIAM International Conference on Data Mining. SIAM, 325–333.

[75]

D. Koschützki, H. Schwöbbermeyer, and F. Schreiber. 2007. Ranking of network elements based on functional substructures. J. Theor. Biol. 248, 3 (2007), 471–479.

[76]

S. Roy, M. Raj, P. Ghosh, and S. K. Das. 2017. Role of motifs in topological robustness of gene regulatory networks. In Proceedings of the 2017 IEEE International Conference on Communications (ICC'17). IEEE, 1–6.

[77]

M. Newman. 2010. Networks: An Introduction. Oxford University Press.

Digital Library

[78]

A. F. Abdelzaher. 2016. Identifying Parameters for Robust Network Growth using Attachment Kernels: A Case Study on Directed and Undirected Networks. Ph.D. Dissertation. Virginia Commonwealth University.

[79]

A. Abdelzaher, T. Dinh, M. Mayo, and P. Ghosh. 2016. Optimal topology of gene-regulatory networks: Role of the average shortest path. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies. ICST, 241–244.

Digital Library

[80]

T. E. Gorochowski, C. S. Grierson, and M. di Bernardo. 2018. Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks. Sci. Adv. 4, 3 (2018), eaap9751.

[81]

J. Goni, A. Avena-Koenigsberger, N. V. de Mendizabal, M. P van den Heuvel, R. F. Betzel, and O. Sporns. 2013. Exploring the morphospace of communication efficiency in complex networks. PLoS One 8, 3 (2013), e58070.

[82]

S. Camazine. 2006. Self-Organizing Systems. Encyclopedia of Cognitive Science (2006).

[83]

S. Roy, V. K Shah, and S. K. Das. 2018. Design of robust and efficient topology using enhanced gene regulatory networks. IEEE Trans. Molec. Biol. Multi-Scale Commun. 4, 2 (2018), 73–87.

[84]

V. K. Shah, S. Roy, S. Silvestri, and S. K. Das. 2019. Bio-DRN: Robust and energy-efficient bio-inspired disaster response networks. In Proceedings of the 16th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS'19).

[85]

V. K. Shah, S. Roy, S. Silvestri, and S. K. Das. 2019. Towards energy-efficient and robust disaster response networks. In Proceedings of the ACM Conference on Distributed Computing and Networking. 397–400.

Digital Library

[86]

S. Roy, N. Ghosh, P. Ghosh, and S. K. Das. 2020. bioMCS: A bio-inspired collaborative data transfer framework over fog computing platforms in mobile crowdsensing. In Proceedings of the 21st International Conference on Distributed Computing and Networking.

Digital Library

[87]

S. Roy, N. Ghosh, and S. K. Das. 2019. biosmartsense: A bio-inspired data collection framework for energy-efficient, qoi-aware smart city applications. In Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom'19). IEEE, 1–10.

[88]

P. Bradford. 2018. Preliminary exploration of co-functional gene networks for wireless sensor networks. In Proceedings of the IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC'18). IEEE, 1005–1008.

[89]

H. Byun and S. Yang. 2017. Energy-balancing node scheduling inspired by gene regulatory networks for wireless sensor networks. IET Commun. 11, 17 (2017), 2650–2659.

[90]

S. Das, P. Koduru, X. Cai, S. Welch, and V. Sarangan. 2008. The gene regulatory network: An application to optimal coverage in sensor networks. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. ACM, 1461–1468.

Digital Library

[91]

N. El-Mawass, N. Chendeb, and N. Agoulmine. 2014. Robust self-organized wireless sensor network: A gene regulatory network bio-inspired approach. In Genetic and Evolutionary Computing. Springer, 105–114.

[92]

T. Taylor. 2004. A genetic regulatory network-inspired real-time controller for a group of underwater robots. In Intelligent Autonomous Systems, Vol. 8. 403–412.

[93]

A. Markham and N. Trigoni. 2010. Discrete Gene Regulatory Networks (dGRNs): A novel approach to configuring sensor networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM'10). IEEE, 1–9.

Digital Library

[94]

S. Roy and S. K. Das. 2019. A bio-inspired approach to design robust and energy-efficient communication network topologies. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops'19). IEEE, 449–450.

[95]

S. Roy, N. Ghosh, and S. K. Das. 2020. bioSmartSense+: A bio-inspired probabilistic data collection framework for priority-based event reporting in IoT environments. Perv. Mobile Comput. 67 (2020), 101179.

[96]

A. Nazi, M. Raj, M. Di Francesco, P. Ghosh, and S. K. Das. 2015. Exploiting gene regulatory networks for robust wireless sensor networking. In Proceedings of the IEEE Global Communications Conference (GLOBECOM'15). IEEE, 1–7.

[97]

A. Nazi, M. Raj, M. Di Francesco, P. Ghosh, and S. K. Das. 2016. Efficient communications in wireless sensor networks based on biological robustness. In Proceedings of the International Conference on Distributed Computing in Sensor Systems (DCOSS'16). IEEE, 161–168.

[98]

A. Nazi, M. Raj, M. Di Francesco, P. Ghosh, and S. K. Das. 2013. Robust deployment of wireless sensor networks using gene regulatory networks. In Proceedings of the International Conference on Distributed Computing and Networking (ICDCN'13). Springer, 192–207.

[99]

A. Nazi, M. Raj, M. Di Francesco, P. Ghosh, and S. K. Das. 2014. Deployment of robust wireless sensor networks using gene regulatory networks: An isomorphism-based approach. Perv. Mobile Comput. 13 (2014), 246–257.

Digital Library

[100]

B. K. Kamapantula, A. Abdelzaher, P. Ghosh, M. Mayo, E. J. Perkins, and S. K. Das. 2014. Leveraging the robustness of genetic networks: A case study on bio-inspired wireless sensor network topologies. J. Ambient Intell. Human. Comput. 5, 3 (2014), 323–339.

[101]

B. K. Kamapantula, A. Abdelzaher, P. Ghosh, M. Mayo, E. Perkins, and S. K. Das. 2012. Performance of wireless sensor topologies inspired by E. coli genetic networks. In Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE, 302–307.

[102]

Y. Meng and H. Guo. 2012. A gene regulatory network based framework for self-organization in mobile sensor networks. In Proceedings of the 2012 IEEE Congress on Evolutionary Computation. IEEE, 1–7.

[103]

I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. 2002. Wireless sensor networks: A survey. Comput. Netw. 38, 4 (2002), 393–422.

Digital Library

[104]

R. Kulkarni, A. Förster, and G. Venayagamoorthy. 2010. Computational intelligence in wireless sensor networks: A survey. IEEE Commun. Surv. Tutor. 13, 1 (2010), 68–96.

Digital Library

[105]

T. Kaur and D. Kumar. 2019. Computational intelligence-based energy efficient routing protocols with QoS assurance for wireless sensor networks: a survey. Int. J. Wireless Mobile Comput. 16, 2 (2019), 172–193.

Digital Library

[106]

L. Page, S. Brin, R. Motwani, and T. Winograd. 1999. The Pagerank Citation Ranking: Bringing Order to the Web.Technical Report. Stanford InfoLab.

[107]

D. Pediaditakis, Y. Tselishchev, and A. Boulis. 2010. Performance and scalability evaluation of the Castalia wireless sensor network simulator. In Proceedings of the 3rd international ICST conference on simulation tools and techniques. ICST, 53.

Digital Library

[108]

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 2 (2002), 182–197.

Digital Library

[109]

P. Koduru, Z. Dong, S. Das, S. M Welch, J. L. Roe, and E. Charbit. 2008. A multiobjective evolutionary-simplex hybrid approach for the optimization of differential equation models of gene networks. IEEE Trans. Evol. Comput. 12, 5 (2008), 572–590.

Digital Library

[110]

T. C. Collier and C. Taylor. 2004. Self-organization in sensor networks. J. Parallel Distrib. Comput. 64, 7 (2004), 866–873.

Digital Library

[111]

K. L. Mills. 2007. A brief survey of self-organization in wireless sensor networks. Wireless Commun. Mobile Comput. 7, 7 (2007), 823–834.

Digital Library

[112]

P. K. Biswas and S. Phoha. 2006. Self-organizing sensor networks for integrated target surveillance. IEEE Trans. Comput. 55, 8 (2006), 1033–1047.

Digital Library

[113]

Y. Jin, H. Guo, and Y. Meng. 2012. A hierarchical gene regulatory network for adaptive multirobot pattern formation. IEEE Trans. Syst. Man Cybernet. B 42, 3 (2012), 805–816.

Digital Library

[114]

F. Benhamida, A. Bouabdellah, and Y. Challal. 2017. Using delay tolerant network for the internet of things: Opportunities and challenges. In Proceedings of the 2017 8th International Conference on Information and Communication Systems (ICICS'17). IEEE, 252–257.

[115]

S. Saha, S. Nandi, P. Paul, V. Shah, A. Roy, and S. Das. 2015. Designing delay constrained hybrid ad hoc network infrastructure for post-disaster communication. Ad Hoc Netw. 25 (2015), 406–429.

Digital Library

[116]

Md Y. S. Uddin, H. Ahmadi, T. Abdelzaher, and R. Kravets. 2013. Intercontact routing for energy constrained disaster response networks. IEEE Trans. Mobile Comput. 12, 10 (2013), 1986–1998.

Digital Library

[117]

L. A. Zager and G. C. Verghese. 2008. Graph similarity scoring and matching. Appl. Math. Lett. 21, 1 (2008), 86–94.

[118]

W. Ejaz, M. Naeem, A. Shahid, A. Anpalagan, and M. Jo. 2017. Efficient energy management for the internet of things in smart cities. IEEE Commun. Mag. 55, 1 (2017), 84–91.

Digital Library

[119]

J. Nielsen. 2007. Principles of optimal metabolic network operation. Molec. Syst. Biol. 3, 1 (2007), 126.

[120]

S. Sakai, M. Togasaki, and K. Yamazaki. 2003. A note on greedy algorithms for the maximum weighted independent set problem. Discr. Appl. Math. 126, 2–3 (2003), 313–322.

Digital Library

[121]

N. Matloff. 2008. Introduction to Discrete-event Simulation and the Simpy Language. Department of Computer Science. University of California at Davis.

[122]

W. Yu, F. Liang, X. He, W. Grant Hatcher, C. Lu, J. Lin, and X. Yang. 2017. A survey on the edge computing for the Internet of Things. IEEE Access 6 (2017), 6900–6919.

[123]

A. Ferrer, J. Marquès, and J. Jorba. 2019. Towards the decentralised cloud: Survey on approaches and challenges for mobile, ad hoc, and edge computing. ACM Comput. Surv. 51, 6 (2019), 1–36.

Digital Library

[124]

W. Day and H. Edelsbrunner. 1984. Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1, 1 (1984), 7–24.

[125]

A. Capponi, C. Fiandrino, D. Kliazovich, P. Bouvry, and S. Giordano. 2017. A cost-effective distributed framework for data collection in cloud-based mobile crowd sensing architectures. IEEE Trans. Sust. Comput. 2, 1 (2017), 3–16.

[126]

S. Roy, P. Ghosh, D. Barua, and S. Das. 2020. Motifs enable communication efficiency and fault-tolerance in transcriptional networks. Sci. Rep. 10, 1 (2020), 1–15.

[127]

S. Murthy and J. J. Garcia-Luna-Aceves. 1996. An efficient routing protocol for wireless networks. Mobile Netw. Appl. 1, 2 (1996), 183–197.

Digital Library

[128]

F. Yu, Y. Li, F. Fang, and Q. Chen. 2007. A new TORA-based energy aware routing protocol in mobile ad hoc networks. In Proceedings of the 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet. IEEE, 1–4.

[129]

D. Bojic, E. Sasaki, N. Cvijetic, T. Wang, J. Kuno, J. Lessmann, S. Schmid, H. Ishii, and S. Nakamura. 2013. Advanced wireless and optical technologies for small-cell mobile backhaul with dynamic software-defined management. IEEE Commun. Mag. 51, 9 (2013), 86–93.

[130]

J. Leskovec, D. Huttenlocher, and J. Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1361–1370.

Digital Library

[131]

D. Kempe, J. Kleinberg, and É. Tardos. 2005. Influential nodes in a diffusion model for social networks. In Proceedings of the International Colloquium on Automata, Languages, and Programming. Springer, 1127–1138.

Digital Library

[132]

W. Chen, W. Lu, and N. Zhang. 2012. Time-critical influence maximization in social networks with time-delayed diffusion process. In Proceedings of the 26th AAAI Conference on Artificial Intelligence.

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Cited By

Halder SRoy SGhosh PGhosh N(2024) ADRIN2.0 : Enabling Post-Disaster Communication Through Ad aptive Mobility-Informed R out in g IEEE Access10.1109/ACCESS.2024.343286612(102368-102380)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3432866
Roy SDas S(2023)Bio-Inspired Design of Biosensor NetworksEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00131-X(86-102)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00131-X
Al Musawi ARoy SGhosh P(2022)Identifying accurate link predictors based on assortativity of complex networksScientific Reports10.1038/s41598-022-22843-412:1Online publication date: 27-Oct-2022
https://doi.org/10.1038/s41598-022-22843-4

Index Terms

Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 8

November 2022

754 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3481697

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2021

Accepted: 01 May 2021

Revised: 01 March 2021

Received: 01 January 2020

Published in CSUR Volume 54, Issue 8

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Cited By

Halder SRoy SGhosh PGhosh N(2024) ADRIN2.0 : Enabling Post-Disaster Communication Through Ad aptive Mobility-Informed R out in g IEEE Access10.1109/ACCESS.2024.343286612(102368-102380)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3432866
Roy SDas S(2023)Bio-Inspired Design of Biosensor NetworksEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00131-X(86-102)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00131-X
Al Musawi ARoy SGhosh P(2022)Identifying accurate link predictors based on assortativity of complex networksScientific Reports10.1038/s41598-022-22843-412:1Online publication date: 27-Oct-2022
https://doi.org/10.1038/s41598-022-22843-4

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