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Network alignment and motif discovery in dynamic networks

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Abstract

In the -omics era, bioinformatics technologies allowed the development of several approaches useful to analyze biological systems. To provide a deep insight into a biological system, the interactions between molecules are usually modeled as a dynamic network (also known as “temporal network” or “time-varying network”). The latter allows investigating how the interactions evolve over time, contrary to a static network. This survey presents an assessment of the software tools for network alignment and motif discovery in dynamic networks. We considered a set of criteria belonging to the following macro areas: (i) methodology, (ii) functionality, and (iii) availability. For instance, we investigated the objective functions and the scores used for the processing, alignment methods, use of a method for the alignment of static networks adapted to the dynamic context, network discrimination performance, and other additional information. We reported how several issues may be transferred from static to dynamic networks by taking into account the temporal information. Furthermore, we encountered a systematic convergence toward iterative strategies both for network alignment and motif discovery, justified by the fact that a dynamic network is usually analyzed through the sub-analysis of its time points.

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Notes

  1. In vivo: “Literally, in life, but used generally for procedures or tests done on intact organisms rather than on isolated cells in culture (in-vitro)”. Dictionary of Biomedicine, Oxford University Press. https://doi.org/10.1093/acref/9780199549351.001.0001.

References

  • Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. https://doi.org/10.1016/S0022-2836(05)80360-2

    Article  Google Scholar 

  • Aparício D, Ribeiro P, Milenković T et al (2019) Temporal network alignment via got-wave. Bioinformatics 35(18):3527–3529

    Article  Google Scholar 

  • Athanasios A, Charalampos V, Vasileios T et al (2017) Protein–protein interaction (PPI) network: recent advances in drug discovery. Curr Drug Metab 18(1):5–10

    Article  Google Scholar 

  • Ay F, Kellis M, Kahveci T (2011) SubMAP: aligning metabolic pathways with subnetwork mappings. J Comput Biol 18(3):219–235

    Article  MathSciNet  Google Scholar 

  • Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68

    Article  Google Scholar 

  • Barros CDT, Mendonça MRF, Vieira AB et al (2021) A survey on embedding dynamic graphs. arXiv:2101.01229

  • Cai Y, Wang J, Deng L (2020) SDN2GO: an integrated deep learning model for protein function prediction. Front Bioeng Biotechnol 8:391

    Article  Google Scholar 

  • Chen B, Fan W, Liu J et al (2014) Identifying protein complexes and functional modules-from static PPI networks to dynamic PPI networks. Brief Bioinform 15(2):177–194

    Article  Google Scholar 

  • Chiu C, Zhan J (2018) Deep learning for link prediction in dynamic networks using weak estimators. IEEE Access 6:35937–35945. https://doi.org/10.1109/access.2018.2845876

    Article  Google Scholar 

  • Chow K, Sarkar A, Elhesha R et al (2021) ANCA: alignment-based network construction algorithm. IEEE/ACM Trans Comput Biol Bioinform 18(2):512–524. https://doi.org/10.1109/tcbb.2019.2923620

    Article  Google Scholar 

  • Cinaglia P, Cannataro M (2022) Forecasting COVID-19 epidemic trends by combining a neural network with RT estimation. Entropy (Basel) 24(7):929. https://doi.org/10.3390/e24070929

    Article  Google Scholar 

  • Cinaglia P, Guzzi PH, Veltri P (2018) Integro: an algorithm for data-integration and disease-gene association. In: 2018 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 2076–2081. https://doi.org/10.1109/BIBM.2018.8621193

  • Consortium GO (2006) The gene ontology (GO) project in 2006. Nucl Acids Res 34:D322-d326. https://doi.org/10.1093/nar/gkj021

    Article  Google Scholar 

  • Dai C, He J, Hu K et al (2020) Identifying essential proteins in dynamic protein networks based on an improved \(h\)-index algorithm. BMC Med Inform Decis Mak 20(1):110

    Article  Google Scholar 

  • Dassi E, Quattrone A (2016) DynaMIT: the dynamic motif integration toolkit. Nucl Acids Res 44(1):e2

    Article  Google Scholar 

  • Doria-Belenguer S, Youssef MK, Böttcher R et al (2020) Probabilistic graphlets capture biological function in probabilistic molecular networks. Bioinformatics 36(Suppl-2):i804–i812

    Article  Google Scholar 

  • Elhesha R, Kahveci T (2016) Identification of large disjoint motifs in biological networks. BMC Bioinform 17(1):408

    Article  Google Scholar 

  • Elhesha R, Sarkar A, Boucher C et al (2019a) Identification of co-evolving temporal networks. BMC Genom 20(Suppl 6):434

  • Elhesha R, Sarkar A, Cinaglia P et al (2019b) Co-evolving patterns in temporal networks of varying evolution. In: Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics. ACM, New York, NY, USA, Bcb’19, pp 494–503. https://doi.org/10.1145/3307339.3342152

  • Faisal FE, Meng L, Crawford J et al (2015) The post-genomic era of biological network alignment. EURASIP J Bioinform Syst Biol 2015:3

    Article  Google Scholar 

  • Fisher DN, Pinter-Wollman N (2021) Using multilayer network analysis to explore the temporal dynamics of collective behavior. Curr Zool 67(1):71–80

    Article  Google Scholar 

  • Ghoshal G, Chi L, Barabasi AL (2013) Uncovering the role of elementary processes in network evolution. Sci Rep 3:2920. https://doi.org/10.1038/srep02920

    Article  Google Scholar 

  • Gligorijević V, Malod-Dognin N, Pržulj N (2016) Fuse: multiple network alignment via data fusion. Bioinformatics 32(8):1195–1203

    Article  Google Scholar 

  • Guerra C, Guzzi P (2020) Evaluation of the topological agreement of network alignments. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE Computer Society, New York, pp 1785–1792. https://doi.org/10.1109/bibm49941.2020.9313513

  • Gupta S, Stamatoyannopoulos JA, Bailey TL et al (2007) Quantifying similarity between motifs. Genome Biol 8(2):R24

    Article  Google Scholar 

  • Gurukar S, Ranu S, Ravindran B (2015) Commit: a scalable approach to mining communication motifs from dynamic networks. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data

  • Guzzi PH, Milenkovic T (2018) Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin. Brief Bioinform 19(3):472–481

    Google Scholar 

  • Holme P (2015) Modern temporal network theory: a colloquium. Eur Phys J B. https://doi.org/10.1140/epjb/e2015-60657-4

    Article  Google Scholar 

  • Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519(3):97–125. https://doi.org/10.1016/j.physrep.2012.03.001

    Article  Google Scholar 

  • Hu J, He J, Li J et al (2019) A novel algorithm for alignment of multiple PPI networks based on simulated annealing. BMC Genom 20(Suppl 13):932

    Article  Google Scholar 

  • Hulovatyy Y, Chen H, Milenković T (2015) Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics 31(12):i171-180

    Article  Google Scholar 

  • Huynh-Thu VA, Sanguinetti G (2019) Gene regulatory network inference: an introductory survey. Methods Mol Biol 1883:1–23

    Article  Google Scholar 

  • Ivarsson Y, Jemth P (2019) Affinity and specificity of motif-based protein–protein interactions. Curr Opin Struct Biol 54:26–33

    Article  Google Scholar 

  • Jazayeri A, Yang CC (2020) Motif discovery algorithms in static and temporal networks: a survey. J Complex Netw. https://doi.org/10.1093/comnet/cnaa031

    Article  MathSciNet  Google Scholar 

  • Joakim S, Musial K, Gabrys B (2020) Foundations and modelling of dynamic networks using dynamic graph neural networks: a survey. CoRR arXiv:2005.07496

  • Ju J, Wei P (2017) Signaling network-based functional cell design. Sheng Wu Gong Cheng Xue Bao 33(3):386–392

    MathSciNet  Google Scholar 

  • Kim B, Lee KH, Xue L et al (2018) A review of dynamic network models with latent variables. Stat Surv 12:105–135

    Article  MathSciNet  MATH  Google Scholar 

  • Kim H, Jo HH, Jeong H (2021) Impact of environmental changes on the dynamics of temporal networks. PLoS ONE 16(4):1–19. https://doi.org/10.1371/journal.pone.0250612

    Article  Google Scholar 

  • Klein C, Marino A, Sagot MF et al (2012) Structural and dynamical analysis of biological networks. Brief Funct Genom 11(6):420–433

    Article  Google Scholar 

  • Kovanen L, Karsai M, Kaski K et al (2011) Temporal motifs in time-dependent networks. J Stat Mech Theory Exp 2011(11):P11005. https://doi.org/10.1088/1742-5468/2011/11/p11005

    Article  Google Scholar 

  • Li M, Meng X, Zheng R et al (2020) Identification of protein complexes by using a spatial and temporal active protein interaction network. IEEE/ACM Trans Comput Biol Bioinform 17(3):817–827

    Article  Google Scholar 

  • Linke AC, Mash LE, Fong CH et al (2020) Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders. Neuroimage 223(117):383

    Google Scholar 

  • Ma CY, Liao CS (2020) A review of protein–protein interaction network alignment: from pathway comparison to global alignment. Comput Struct Biotechnol J 18:2647–2656. https://doi.org/10.1016/j.csbj.2020.09.011

    Article  Google Scholar 

  • Meng L, Striegel A, Milenković T (2016) Local versus global biological network alignment. Bioinformatics 32(20):3155–3164

    Article  Google Scholar 

  • Mertzios GB, Michail O, Chatzigiannakis I et al (2013) Temporal network optimization subject to connectivity constraints. In: Fomin FV, Freivalds R, Kwiatkowska M et al (eds) Automata, languages, and programming. Springer, Berlin, pp 657–668

    Chapter  Google Scholar 

  • Milano M, Guzzi PH, Tymofieva O et al (2017) An extensive assessment of network alignment algorithms for comparison of brain connectomes. BMC Bioinform 18(Suppl 6):235

    Article  Google Scholar 

  • Milenković T, Ng WL, Hayes W et al (2010) Optimal network alignment with graphlet degree vectors. Cancer Inform 9:121–137

    Article  Google Scholar 

  • Mohan A, Pramod K (2021) Temporal network embedding using graph attention network. Complex Intell Syst. https://doi.org/10.1007/s40747-021-00332-x

    Article  Google Scholar 

  • Mukherjee K, Hasan MM, Boucher C et al (2018) Counting motifs in dynamic networks. BMC Syst Biol 12(Suppl 1):6

    Article  Google Scholar 

  • Ou-Yang L, Dai DQ, Li XL et al (2014) Detecting temporal protein complexes from dynamic protein–protein interaction networks. BMC Bioinform 15:335

    Article  Google Scholar 

  • Paranjape A, Benson AR, Leskovec J (2017) Motifs in temporal networks. In: Proceedings of the 10th ACM international conference on web search and data mining. https://doi.org/10.1145/3018661.3018731

  • Patra S, Mohapatra A (2020) Review of tools and algorithms for network motif discovery in biological networks. IET Syst Biol 14(4):171–189

    Article  Google Scholar 

  • Patro R, Kingsford C (2012) Global network alignment using multiscale spectral signatures. Bioinformatics 28(23):3105–3114

    Article  Google Scholar 

  • Pearson WR (1995) Comparison of methods for searching protein sequence databases. Protein Sci 4(6):1145–1160

    Article  Google Scholar 

  • Rao VS, Srinivas K, Sujini GN et al (2014) Protein–protein interaction detection: methods and analysis. Int J Proteomics 2014:147648

    Article  Google Scholar 

  • Ren Y, Ay A, Kahveci T (2018a) Shortest path counting in probabilistic biological networks. BMC Bioinform 19(1):465

  • Ren Y, Sarkar A, Kahveci T (2018b) ProMotE: an efficient algorithm for counting independent motifs in uncertain network topologies. BMC Bioinform 19(1):242

  • Rossetti G, Cazabet R (2018) Community discovery in dynamic networks. ACM Comput Surv 51(2):1–37. https://doi.org/10.1145/3172867

    Article  Google Scholar 

  • Saraph V, Milenković T (2014) MAGNA: maximizing accuracy in global network alignment. Bioinformatics 30(20):2931–2940

    Article  Google Scholar 

  • Sarkar A, Ren Y, Elhesha R et al (2019) A new algorithm for counting independent motifs in probabilistic networks. IEEE/ACM Trans Comput Biol Bioinform 16(4):1049–1062

    Article  Google Scholar 

  • Singh R, Xu J, Berger B (2008) Global alignment of multiple protein interaction networks. Pac Symp Biocomput 2008:303–314

    Google Scholar 

  • Sun Y, Crawford J, Tang J et al (2015) Simultaneous optimization of both node and edge conservation in network alignment via wave. In: Pop M, Touzet H (eds) Algorithms in bioinformatics. Springer, Berlin, pp 16–39

    Chapter  Google Scholar 

  • Torricelli M, Karsai M, Gauvin L (2020) Weg2vec: event embedding for temporal networks. Sci Rep 10(1):7164

    Article  Google Scholar 

  • Vijayan V, Milenkovic T (2018) Aligning dynamic networks with DynaWAVE. Bioinformatics 34(10):1795–1798

    Article  Google Scholar 

  • Vijayan V, Saraph V, Milenković T (2015) Magna++: maximizing accuracy in global network alignment via both node and edge conservation. Bioinformatics 31(14):2409–2411

    Article  Google Scholar 

  • Vijayan V, Critchlow D, Milenkovic T (2017) Alignment of dynamic networks. Bioinformatics 33(14):i180–i189

    Article  Google Scholar 

  • Vijayan V, Gu S, Krebs ET et al (2020) Pairwise versus multiple global network alignment. IEEE Access 8:41961–41974. https://doi.org/10.1109/access.2020.2976487

    Article  Google Scholar 

  • Wang J, Peng X, Peng W et al (2014) Dynamic protein interaction network construction and applications. Proteomics 14(4–5):338–352

    Article  Google Scholar 

  • Xia F, Wei H, Yu S et al (2019) A survey of measures for network motifs. IEEE Access 7:106576–106587. https://doi.org/10.1109/access.2019.2926752

    Article  Google Scholar 

  • Yu EY, Fu Y, Chen X et al (2020) Identifying critical nodes in temporal networks by network embedding. Sci Rep. https://doi.org/10.1038/s41598-020-69379-z

    Article  Google Scholar 

  • Zahiri J, Emamjomeh A, Bagheri S et al (2020) Protein complex prediction: a survey. Genomics 112(1):174–183

    Article  Google Scholar 

  • Zaslavskiy M, Bach F, Vert JP (2009) Global alignment of protein–protein interaction networks by graph matching methods. Bioinformatics 25(12):i259-267

    Article  Google Scholar 

  • Zhang Y, Lin H, Yang Z et al (2016) Construction of dynamic probabilistic protein interaction networks for protein complex identification. BMC Bioinform 17(1):186

    Article  Google Scholar 

  • Zhang J, Zhong C, Lin HX et al (2019) Identifying protein complexes from dynamic temporal interval protein–protein interaction networks. Biomed Res Int 2019:3726721

    Google Scholar 

  • Zhong Y, Li J, He J et al (2020) Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks. BMC Bioinform 21(Suppl 13):385

    Article  Google Scholar 

  • Zhu L, Zhang J, Zhang Y et al (2020) NAIGO: an improved method to align PPI networks based on gene ontology and graphlets. Front Bioeng Biotechnol 8:547

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Health Sciences, University of Catanzaro, Catanzaro, Italy

    Pietro Cinaglia

  2. Department of Medical and Surgical Sciences, Data Analytics Research Center, University of Catanzaro, Catanzaro, Italy

    Mario Cannataro

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  1. Pietro Cinaglia
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  2. Mario Cannataro
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Correspondence to Pietro Cinaglia.

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Cinaglia, P., Cannataro, M. Network alignment and motif discovery in dynamic networks. Netw Model Anal Health Inform Bioinforma 11, 38 (2022). https://doi.org/10.1007/s13721-022-00383-1

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