• O. P. Mintser Shupyk National Medical Academy of Postgraduate Education
  • V. M. Zalisky National Scientific Center «M. D. Strazhesko Institute of Cardiology» NAMS of Ukraine



network medicine, cardiac diseases, network analysis, interdisciplinary approaches, intracellular signaling, multi «omics» data, molecular networks, bioinformatic resource, knowledge ontology


Background. The article provides a brief overview of the main publications on networked medicine and the application of an integrated network analysis of protein interactions in the study of human diseases. Given the functional interdependencies between the molecular components in a human cell, the disease is rarely a consequence of an anomaly in one gene, but reflects a complex anomaly of the intracellular network. New tools of network medicine offer a platform for a systematic study not only of the molecular complexity of a specific disease, leading to the identification of modules and pathways of the disease, but also the molecular relationships between manifestly (pathogenic) phenotypes.

Results. The purpose of the study: summarize the results of using the methodology of network medicine, primarily in cardiology.

It is postulated that, when revealing new disease genes, it is necessary to determine the biological significance of disease-related mutations identified as a result of genome studies as a whole, and complete sequencing of the genome, as well as for detection of targets and biomarkers of complex diseases.

Conclusions. Network medicine and the ontology of knowledge have much in common both in the creation strategy and in the technologies of use. However, the tasks of multidimensional modeling are now preferably performed in the «knowledge ontology» strategy. You can also say about education, where ontological solutions are more popular.

Attention is given to the issues of network interconnection of various cardiac diseases at the molecular and phenotypic level. Many diseases of the heart are examined with the help of complex clinical phenotypes formed as a result of integrative influence on the molecular (interactive), genetic (genomic) and ecological (metabolic) levels.


Amosov, N. M., Mintser, O. P., Palets, B. L. (1977).

vozmozhnostyakh kibernetiki v meditsine [On the possibilities of cybernetics in medicine]. Kardiologiya (Cardiology), 7, 19-25.

Zalesskii, V. N. (2018). Proteomnyi analiz sekreta mezenkhimal'nykh stvolovykh kletok [Proteomic analysis of the secretion of mesenchymal stem cells]. In Timchenko A. S., Zalesskii V. N. Mezenkhimal'nye

opukholevye stvolovye kletki: mekhanizmy immunovospalitel'noi modulyatsii stvolovykh kletok pri personalizirovannoi kletochnoi reabilitatsii bol'nykh s mul'tifaktornymi i onkogematologicheskimi zabolevaniyami [Mesenchymal and tumor stem cells: mechanisms of immune-inflammatory modulation of stem cells in personalized cellular rehabilitation of patients with multifactorial and oncohematological diseases]: monograph (pp. 275-306). Kyiv: Mizhregional'nii vidavnichii tsentr «MEDINFORM» (Interregional Publishing Center «MEDINFORM»).

Mintser, O. P. (2008). Novye informatsionnye tekhnologii v meditsine [New information technologies in medicine]. Zhurnal prakt. vracha (Journal of Practical Physician), 3, 22-33.

Mintser, O. P. (2015). Kontseptual'no-tekhnologichni pidkhodi v stvorenni edinogo medichnogo osvitn'ogo proektu [Conceptual and technological approaches to the creation of an unified medical educational project]. Medichna informatika ta inzheneriya (Medical Informatics and Engineering), 1, 5-8.

Albert, R., Barabasi, A. L. (2002). Statistical mechanics of complex networks. Rev. Mod. Phys., 74(1), 47-97. doi: 10.1103/RevModPhys.74.47.

Tompa, P., Davey, W. E., Gibson, T. J., & Babu, M. M. (2014). A million peptide motifs for the molecular

biologist. Mol. Cell., 55(2), 161-169. doi: 10.1016/j. molcel.2014.05.032.

Venkatesan, K., Rual, J. F., Vazquez, A. Stelzl, U., Lemmens, I., Hirozane-Kishikawa, T., ... Vidal, M. (2008). An empirical framework for binary interactome mapping. Nature Methods, 6(1), 83-90. doi: 10.1038/ nmeth.1280.

Davidsen, P. K., Herbert, J. M., Antczak, P., Clarke, K., Ferrer, E., Peinado, V. I., ... Falciani. F. (2014). A system biology approach reveals a link between systemic cytokines and skeletal muscle energy metabolism in rodent. Genome Med., 6(8), 59. doi: 10.1186/s13073-014-0059-5.

Arrell, D. K., & Terzic, A. (2010). Network systems biology for drug discovery. Clin. Pharmacol., 88(1), 120-125. doi: 10.1038/clpt.2010.91.

Barabasi, A. L., Gulbahce, N., & Lascalzo, J. (2011). Network medicine: a network-based approach to human disease. Nat. Rev. Genet., 12(1), 56-68. doi: 10.1038/ nrg2918.

Barabasi, A. L. (2007). Network medicine — from obesity to the "diseasome". N. Engl. J. Med., 357(4), 404-407. doi: 10.1056/NEJMe078114.

Omura, S., Kawai, E., Sato, F., Martinez, N. E., Chaitanya, G. V., Rollyson, P. A., ... Tsunoda, I.

(2014). Bioinformatics multivariate analysis determine a set of phase-specific biomarker candidates in a novel mouse model for viral myocarditis. Circ. Cardiovasc. Genet., 7(4), 444-454. doi: 10.1161/ CIRCGENETICS.114.000505.

Börner, K., Sanyal, S., & Vespignani, A. (2007). Network science. Annual review of information science and technology, 41(1), 537-607, doi: 10.1002/ aris.2007.1440410119.

Brown, T. R., Krogh-Madsen, T., & Christini, D. J.

(2015). Computational approaches to understanding the role of fibroblast-monocyte interaction in cardiac arrhythmogenesis. Biomed. Res. Int., 2015, 465714. doi: 10.1155/2015/465714.

Caldarelli, G. (2007). Scale-free networks. Oxford: Oxford University Press.

Shih, Y. H., Zhang, Y., Ding, Y., Ross, C. A., Li, H., Olson, T. M., & Xu, X. (2015). Cardiac transcriptome and dilated cardiomyopathy genes in zebrafish. Circ. Cardiovasc. Genet., 8(2), 261-269. doi: 10.1161/ CIRCGENETICS.114.000702.

Corbi-Verge, C., & Kim, P. M. (2016). Motif mediated protein-protein interactions as drug targets. Cell Commun. Signal., 14, 8. doi: 10.1186/s12964-016-0131-4.

Menche, J., Sharma, A., Kitsak, K. M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabasi, A. L. (2015). Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science, 347(6224), 1257601. doi: 10.1126/science.1257601.

Ullah, S., Lin, S., Xu, Y., Deng, W., Ma, L., Zhang, Y., Liu, Z., & Xue, Y. (2016). dpPAF: an integrative database of protein. Sci. Rep., 6, 23534. doi: 10.1038/ srep23534.

Eisenberg, E., & Levanon, E. Y. (2003). Preferential attachment in the protein network evolution. Phys. Rev. Lett., 91(13), 138701. doi: 10.1103/ PhysRevLett.91.138701.

Erler, J. T., & Linding, R. (2012). Network medicine strikes a blow against breast cancer. Cell, 149(4), 731-733. doi: 10.1016/j.cell.2012.04.014.

Fraser, H. B., Hirsh, A. E., Steinmetz, L. M., Scharfe, C., & Feldman, M. W. (2002). Evolutionary rate in the protein interaction network. Science, 296(5568), 750-752. doi: 10.1126/science.1068696.

Gibson, D. G. (2014). Programming biological operation systems: genome design, assembly and activation. Nat. Methods, 11(5), 521-526. doi: 10.1038/nmeth.2894.

Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. Cell, 100(1), 57-70.

Henney, A., & Superti-Furga, G. (2008). A network solution. Nature, 455(7214), 730-731. doi: 10.1038/455730a.

Hood, L. (2013). Systems biology and p4 medicine: past, precut future. Rambam Maimonides Med. J., 4(2), e0012. doi: 10.5041/RMMJ.10112.

Azuaje, F. J., Rodius, S., Zhang, L., Devaux, Y., & Wagner, D. R. (2011). Information encoded in a network inflammation protein predicts clinical outcome after MI. BMC Med. Genomics, 4, 59 doi: 10.1186/1755-87944-59.

Halasz, M., Kholodenko, B. N., Kolch, W., & Santra, T. (2016). Integrating network reconstruction with mechanistic modelling to predict cancer therapies. Sci. Signal., 9(455), ra114. doi: 10.1126/scisignal.aae0535.

Kitano, H. (2007). A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug. Discov., 6(3), 202-210. doi: 10.1038/nrd2195.

Korcsmaros, T., Schneider, M. V., & Superti-Furga, G. (2017). Next generation of network medicine: interdisciplinary signaling approach. Intergr. Biol. (Camb.), 9(2), 97-108. doi: 10.1039/c6ib00215c.

Jeong, H., Mason, S. P., Barabasi, A. L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411, 41-42. doi: 10.1038/35075138.

Miller, M. L., Jensen, L. J., Diella, F., J0rgensen, C., Tinti, M., Li, L., ... Linding, R. (2008). Linear motif atlas for phosphorylation-dependent signaling. Sci. Signal., 1(35), ra 2. doi: 10.1126/scisignal.1159433.

Liu, J., Jing, L., & Tu, X. (2016). Weighted gene co-expression network analysis identifies specific modules and hub genes related to CAD. BMC Cardiovasc. Disord., 16, 54. doi: 10.1186/s12872-016-0217-3.

Loscalzo, J., Barabasi, A. L., & Silverman, E. K. (Eds). (2017). Network medicine. Comlex systems in human

disease and therapeutics. Cambridge: Harvard Univ. Press.

Sacco, F., Gherardini, P. F., Paoluzi, S., Saez-Rodriguez, J., Helmer-Citterich, M., Ragnini-Wilson, A., Castagnoli, L., & Cesareni, G. (2012). Mapping the human phosphatome on growth pathways. Mol. Syst. Biol., 8, 603. doi: 10.1038/msb.2012.36.

Amberger, J., Bocchini, C. A., Scott, A. F., & Hamosh, A. (2009). McKusick's Online Mendelian Inheritance in Man (OMIM®). Nucleic Acids Res., 37, D793-796. doi: 10.1093/nar/gkn665.

Meszaros, B., Simon, I., & Dosztanyi, Z. (2009). Prediction of protein binding regions in disordered protein. PLoS Comput. Biol., 5(5), e1000376. doi: 10.1371/journal.pcbi.1000376.

Hägg, S., Skogsberg, J., Lundström, J., Noori, P., Nilsson, R., Zhong, H., . Björkegren, J. (2009). Multiorgan expression profiling uncovers a gene module in coronary artery disease involving transendothelial migration of leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study. PLoS Genet., 5(12), e1000754. doi: 10.1371/journal.pgen.1000754.

Farkas, I.J., Korcsmaros, T., Kovacs, I. A., Mihalik, Ä., Palotai, R., Simko, G. I., ... Csermely, P. (2011). Network-based tools for the identification of novel drug targets. Sci. Signal. , 4(173), Pt3. doi: 10.1126/ scisignal.2001950.

Bozoky, B., Savchenko, A., Csermely, P., Korcsmaros, T., Dul, Z., Ponten, F., Szekely, L., & Klein, G. (2013). Novel signatures of cancer-associated fibroblast. Int. J. Cancer, 133(2), 286-293. doi: 10.1002/ijc.28035.

Backes, C., Meder, B., Lai, A., Stoll, M., Rühle, F., Katus, H. A., & Keller, A. (2016). Pathway-based variant enrichment analysis on the example of dilated cardiomyopathy. Hum. Genet., 135(1), 31-40. doi: 10.1007/s00439-015-1609-7.

Pawson, T., & Linding, R. (2008). Network medicine. FEBS Lett., 582(8), 1266-1270. doi: 10.1016/j. febslet.2008.02.011.

Tan, C. S., Pasculescu, A., Lim, W. A., Pawson, T., Bader, G. D., & Linding, R. (2009). Positive selection of tyrosine loss in metazoan evolution. Science, 325(5948), 1686-1688. doi: 10.1126/science.1174301.

Prathipati, P., & Mizuguchi, K. (2016). Systems biology approaches to a rational drug discovery paradigm. Curr. Top. Med. Chem., 16(9), 1009-1025.

Rzhetsky, A., Wajngurt, D., Park, N., & Zheng, T. (2007). Probing genetic overlap among complex human phenotypes. Proc. Natl. Acad. Sci. USA, 104(28), 11694-11699. doi: 10.1073/pnas.0704820104.

Varusai, T.M., Kolch, W., Kholodenko, B. N., & Nguyen, L. K. (2015). Protein-protein interaction generate hidden feedback and feedforward loops to trigger bistable switches, oscillations and biphasic dose-responses. Mol. Biosyst., 11(10), 2750-2762. doi: 10.1039/c5mb00385g.

Pysz, M. A., Gambhir, S. S., & Willmann, J. K. (2010). Molecular imaging: current status and emerging strategies. Clin. Radiol., 65(7), 500-516. doi: 10.1016/j. crad.2010.03.01/1.

Vilahur, G., Cubedo, V., Casani, L., Padro, T., Sabate-Tenas, M., Badimon, J. J., & Badimon, L. (2013). Reperfusion-triggered stress protein response in the myocardium blocked by post-conditioning. Systems biology pathway analysis highlights the key role of the canonical aiyl-hydrocarbon receptor pathway. Eur. Heart J., 34(27), 2062-2093. doi: 10.1093/eurheartj/ehs211.

Santra, T., Kolch, W., & Kholodenko, B. N. (2014). Navigation the multilayered organization of eukaryotic signaling: a new trend in data integration. PLoS Comput. Biol., 10(2), e1003385. doi: 10.1371/journal. pcbi.1003385.

Zeke, A., Lukasc, M., Lim, W. A., & Remenyi, A.

(2009). Scaffolds: interaction platforms for cellular signaling circuits. Trends Cell Biol., 19(8), 364-374. doi: 10.1016/j.tcb.2009.05.007.

Nguyen, L. K., Matallanas, D., Croucher, D. R., von Kriegsheim, B. A., & Kholodenko, N. (2013). Signaling by protein phosphatases and drug development: a systems-centred view. FEBS J., 280(2), 751-765. doi: 10.1111/j.1742-4658.2012.08522.x.

Fazekas, D., Koltai, M., Türei, D., Modos, D., Palfy, M., Dul, Z., ... Korcsmaros, T. (2013). SignaLink 2 — a signaling pathway resource with multi-layered regulatory networks. BMC Syst.Biol., 7, 7. doi: 10.1186/1752-0509-7-7.

Stolovitzky, C., Monroe, D., & Califano, A. (2007). Dialogue on reverse-engineering assessment and methods: the DREAM of high throughput pathway inference. Ann. N. Y. Acad. Sci. USA, 1115, 1-22. doi: 10.1196/annals.1407.021.

Csermely, P., Korcsmaros, T., Kiss, H. J., London, G., & Nussinov, R. (2013). Structure and dynamics of molecular network: a novel paradigm of drug discovery: a comprehensive review. Pharmacol. Ther., 138(3), 333-408. doi: 10.1016/j.pharmthera.2013.01.016.

Sung, M. H., & McNally, J. G. (2011). Live cell imaging and system biology. Wiley Interdiscip. Rev. Syst. Biol. Med., 3(2), 167-182. doi: 10.1002/wsbm.108.

Westerhoff, H. V., Verma, M., Nardelli, M., Adamczyk, M., van Eunen, K., Simeonidis, E., & Bakker, B. M.

(2010). Systems biochemistry in practice: experimenting with modeling and understanding, with regulation and control. Biochem. Soc. Transl., 38(5), 1189-1196. doi: 10.1042/BST0381189.

Westerhoff, H. V., Nakayawa, S., Mondeel, T. D., & Barberis, M. (2015). Systems pharmacology: an opinion on how to turn the impossible into grand challenges. Drug Discov. Today Technol., 15, 23-31. doi: 10.1016/j. ddtec.2015.06.006.

Dinkel, H., Van Roey, K., Michael, S., Davey, N. E., Weatheritt, R. J., Born, D., . Gibson, T. J. (2014).

The eukaryotic linear motif resource ELM: 10 years. Nucleic Acids Res., 42(Database issue), D259-266. doi: 10.1093/nar/gkt1047.

Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., Barabasi, A. L. (2007). The human disease network. PNAS, 104(21), 8685-8690. doi: 10.1073/ pnas.0701361104.

Gamez-Pozo, A., Perez Carrion, R. M., Manso, L., Crespo, C., Mendiola, C., Lopez-Vacas, R., ... Zamora, P. (2014). The long-HER study: clinical and molecular analysis of patients with HER2+ advanced breast cancer who become long-term survivors with with trastuzumab-based therapy. PLoS One, 9(10), e109611. doi: 10.1371/ journal.pone.0109611.

Gevers, D., Kugathasan, S., Denson, L. A., Vazquez-Baeza, Y., Van Treuren, W., Ren, B., ... Xavier, R. J.

(2014). The treatment-naive microbiome in new-onset Crohn's disease. Cell. Host Microbe, 15(3), 382-392. doi: 10.1016/j.chom.2014.02.005/.

Basak, T., Varshney, S., Akhtar, S., & Sengupta, S.

(2015). Understanding different facets of cardiovascular diseases based on model systems to human studies: a proteomic and metabolomics perspective. J. Proteomics, 127(Pt A), 50-60. doi: 10.1016/j.jprot.2015.04.027.

Korcsmaros, T., Farkas, I. J., Szalay, M. S., Rovo, P., Fazekas, D., Spiro, Z., ... Csermely, P. 2010.Uniformly curved signal pathways reveal tissue-specific cross-talks and support drug target discovery. Bioinformatics, 26(16), 2042-2050. doi: 10.1093/bioinformatics/btq310.

Villaveces, J. M., Koti, P., & Habermann, B. H. (2015). Tools for visualization and analysis of molecular network, pathways and -omix data. Adv. Appl. Bioinform. Chem., 8, 11-22. doi: 10.2147/AABC. S63534.

Vojisavljevic, V., & Pirogova, E. (2016). Prediction of intrinsically disordered regions in protein using signal processing methods: application to heat-shock proteins. Med. Biol. Eng. Comput., 54(12), 1831-1844. doi: 10.1007/s11517-016-1477-x.

Zauzoni, B., Soler-Lopez, M., & Aloy, P. (2009). A network medicine approach to human disease. FEBS Lett., 583(11), 1759-1765. doi: 10.1016/j. febslet.2009.03.001.

Zhao, Y., & Jensen, O. N. (2009). Modification-specific proteomics: strategies for characterization of post-translational modifications using enrichments techniques. Proteomics, 9(20), 4632-4641. doi: 10.1002/ pmic.200900398.

Zhu, X., Gerstein, M., & Snyder, M. (2007). Getting connected: analysis and principles of biological networks. Genes and Development, 21(9), 1010-1024. doi: 10.1101/gad.1528707.



How to Cite

Mintser, O. P., & Zalisky, V. M. (2018). CARDIOLOGIC ASPECTS OF NETWORK MEDICINE. Medical Informatics and Engineering, (3), 17–27.