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



system biology, systemic medicine, cell metabolism, multidisciplinary and transdisciplinary approaches, in silico modeling


Background. System biology allows the use of mathematical models to analyze large data sets and helps to simulate the dynamics of complex biological systems. The analytical study discusses the use of the system approach to promote the development of personalized medicine in the treatment of metabolic diseases, insulin resistance, obesity, non-alcoholic fatty liver disease, non-alcoholic steatohepatitis and malignant neoplasms.

Results. The purpose of the study: evaluate the effectiveness of using system biology and system medicine, as well as propose new approaches.

The results of the integral analysis of large data sets for the identification of new biomarker molecules, which are the main personalized therapies, are considered. It is shown that quantitative system analysis can give a new understanding of the molecular mechanisms in the cell, form new concepts for the organization, coordination and regulation of cellular processes. It is extremely necessary to converge experimental and in silico analysis, both individual cellular processes and technological networks.

Conclusions. System-biological and system-medical analyzes require the wide application of multidisciplinary and transdisciplinary approaches, as was demonstrated by the example of sequencing of whole genomes. It is proposed to use a multistage mathematical modeling system in in silico format with an estimation of the probability of each of the key events ensuring the performance of a cascade of biochemical reactions.


Knyaginin, V. N., & Lipetskaya, M. S. (Eds.) (2017). Biomeditsina — 2040. Gorizonty nauki glazami uchenykh [Biomedicine — 2040. Horizons of science through the eyes of scientists]. St. Petersburg: fond «Tsentr strategicheskikh razrabotok «Severo-Zapad» (fund «Center for Strategic Research «North-West»). [In Russian].

Newgard, C. B., An, J., Bain, J. R., Stevens, R. D., Lien, L. F., Haqq, A. M., ... Svetkey, L. P. (2009). A branched-chain amino acid-related metabolic signature that differentiates obese and lean human and contributes to insulin resistance. Cell Metab., 9(4), 311-326. doi: 10.1016/j.cmet.2009.02.002.

Thiele, I., Swainston, N., Fleming, R. M., Hoppe, A, Sahoo, S, Aurich, M. K., ... Palsson, B. 0. (2013). A community-driven global reconstruction of human

metabolism. Nat. Biotechnol., 31(5), 419-425. doi: 10.1038/nbt.2488.

Suhre, K., Wallaschofski, H., Raffler J., Friedrich, N, Haring, R, Michael, K., ... Nauck, M. (2011). A genome-wide association study of metabolic traits in human urine. Nat. Genet., 43(6), 565-569. doi: 10.1038/ng.837.

Rhee, E. P., Ho, J. E., Chen, M. H., Shen, D, Cheng, S, Larson, M. G., ... Gerszten, R. E. (2013). A genome-wide association study of the human metabolome in acommunity-based cohort. Cell Metab., 18(1), 130-143. doi: 10.1016/j.cmet.2013.06.013.

Cuperlovic-Culf, M. (2018). Medicine learning method for analysis of metabolic date and metabolic pathway modeling. Metabolites, 8(1), E4. doi: 10.3390/ metabo8010004.

Angermueller, C., Parnamqa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Mol. Syst. Biol., 12(7), 878. doi: 10.15252/msb.20156651.

Mardinoglu, A., Kampf, C., Asplund, A., Fagerberg, L, Hallstrom, B. M., Edlund, K, ... Nielsen, J. (2014). Defining the human adipose tissue proteome to reveal metabolic alterations in obesity. J. Proteome Res., 13(11), 5106-5115. doi: 10.1021/pr500586e.

Dimitrov, D. V. (2016). Medical internet of things and big data in healthcare. Healthc. Inform. Res., 22(3), 156-163. doi: 10.4258/hir.2016.22.3.156.

Rabinovich, S., Adler, L., Yizhak, K., Sarver, A, Silberman, A, Agron, S., . Erez, A. (2015). Diversion of aspartate in ASS1-deficient tumors' foster de novo pyrimidine synthesis. Nature, 527(7578), 379-383. doi: 10.1038/nature15529.

Gatto, F., Schulze, A., & Nielsen, J. (2016). Systematic analysis reveals than cancer mutations converge on deregulated metabolism of arachidonate and xenobiotics. Cell Rep., 16(3), 878-895. doi: 10.1016/j. celrep.2016.06.038.

Shlomi, T., Benyamini, T., Gottlieb, E., Sharan, R, & Ruppin, E. (2011). Genome-scale metabolic modeling elucidates the role of proliferative adaptations in causing the Warburg effect. PLoS Comput. Biol., 7(3), e1002018. doi: 10.1371/journal.pcbi.1002018.

Mardinoglu, A., Argen, R., Kampt, C., Asplund, A., Uhlen, M., & Nielsen, J. (2014). Genome-scale metabolic modeling of hepatocytes reveals serine deficiency in patients with non-alcoholic patty acid disease. Nat. Commun., 5, 3083. doi: 10.1038/ ncomms4083.

Duarte, N. C., Becker, S. A., Jamshidi, N., Thiele, I., Mo, M. L., Vo, T. D., ... Palsson, B. 0. (2007). Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA, 104(6), 1777-1782. doi: 10.1073/pnas.0610772104.

Gorski, S., & Misteli, T. (2005). Systems biology in the cell nucleus. J. Cell Sci., 118(Pt 18), 4083-4092. doi: 10.1242/jcs.02596.

Wishart, S., Jewison, T., Guo, A. C., Wilson, M., Knox,

C., Liu, Y., ... Scalbert, A. (2013). HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res., 41, D801-807. doi: 10.1093/nar/gks1065.

Agren, R., Mardinoglu, A., Asplund, A., Kampf, C, Uhlen, M, & Nielsen, J. (2014). Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modally. Mol. Syst. Biol., 10, 721. doi: 10.1002/msb.145122.

Lee, S., Zhang, C., Kilicarslan, M., Piening, B. D., Bjornson, E., Hallström, B. M., ... Mardinoglu, A. (2016). Integrated network analysis reveals an association between plasma mannose levels and insulin resistance. Cell Metab., 24(1), 172-184. doi: 10.1016/j. cmet.2016.05.026.

Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., ... Nielsen, J. (2013). Integration of clinical data with a genome-scale metabolic models of the human adipocytes. Mol. Syst. Biol., 9, 649. doi: 10.1038/msb.2013.5.

Mardinoglu, A., & Nielsen, J. (2015). New paradigms for metabolic modeling in human cell. Curr. Opin. Biotechnol., 34, 91-97. doi: 10.1016/j. copbio.2014.12.013.

Yizhak, K., Chaneton, B., Gottlieb, E., & Ruppin, E. (2015). Modeling cancer metabolism on a genome scale. Mol. Syst. Biol., 11(6), 817. doi: 10.15252/ msb.20145307.

Thiele, I., Fleming, R. M., Que, R., Bordbar, A., Diep,

D., & Palsson, B. O. (2012). Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its applications to the evolution of colon usage. PLoS ONE, 7(9), e45635. doi: 10.1371/journal.pone.0045635.

O'Brien, E. J., Monk, J. M., & Palsson, B. O. (2015). Using genome-scale models to predict biological capabilities. Cell, 161(5), 971-987. doi: 10.1016/j. cell.2015.05.019.

Palsson, B. & Zengler, K. (2010). The challenges of integrating multiomic data sets. Nat. Chem. Biol., 6(11), 787-789.

Patil, K. R., & Nielsen, J. (2005). Uncovering transcriptional regulation of metabolism by metabolic network topology. Proc. Natl. Acad. Sci. USA, 102(8), 2685-2689. doi: 10.1073/pnas.0406811102.

Mardinoglu, A., Bjornson, E., Zhang, C., Klevstig, M., Söderlund, S., Stahlman, M., ... Boren, J. (2016). Personal model-assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD. Mol. Syst. Biol., 13(3), 91610. doi: 15252/ wsb.20167422.

Folger, O., Jerby, L., Frezza, C., Gottlieb, E., Ruppin, E., & Shlomi, T. (2011). Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol., 7, 501. doi: 10.1038/msb.2011.35.

Väremo, L., Sheele, C., Broholm, C., Mardinoglu, A., Kampf, C., Asplund, A., . Nielsen, J. (2015). Proteome-

and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes, Cell. Rep., 11(6), 921-933. doi: 10.1016/j.celrep.2015.04.010.

Uhlen, M., Fagerberg, L., Hallstrom, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A., ... Ponten, F. (2015). Proteomics. Tissue-based map of the human proteome. Science, 347(6220), 1260419. doi: 10.1126/ science.1260419.

Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., & Goiyanin, I. (2007). The Edinburgh human metabolic network reconstruction and its functional analysis. Mol. Syst. Biol., 3, 135. doi: 10.1038/ msb4100177.

Psychodios, N., Hau, D. D., Peng, J., Guo, A. C., Mandal, R., Bouatra, S., ... Wishart, D. S. (2011). The human serum metabolome. PLoS ONE, 6(2), e16957. doi: 10.1371/journal.pone.0016957.

Zhao, X., Han, Q., Liu, Y., Sun, C., Gang, X., & Wang, G. (2016). The relationship between branched-chain aminoacid related metabolism signature and insulin resistance: a systematic review. J. Diabetes Res., 2016, 794591. doi: 10.1155/2016/2794591.



How to Cite