Sistem Biyotıbbi: insan hastalıklarına ağ tabanlı bir yaklaşım

Yıl 2019, Cilt: 2 Sayı: 1, 1 - 5, 29.06.2019

Esra Gov

Öz

Sistem Biyotıbbı, bütüncül bir bakış açısı ile hastalıkların altında yatan mekanizmaları anlamak, yeni biyobelirteç ve terapötik hedefler belirlemek ve yeni ilaçlar tasarlamak için yeni nesil bir yaklaşımı ifade etmektedir. Bu kapsamda temel araçları hücresel ağlardır. Mikroarray veya yeni nesil sekanslama çalışmaları ile elde edilen karmaşık, boyutu yüksek ve bilinmeyeni fazla olan verilerin incelenmesi son yıllarda gen, protein kodlamayan RNA, epigenetik modifikasyonlar, protein, metabolit, biyolojik yolizleri seviyelerinde araştırmalar gerektirmekte ve bu sebeple sistem biyolojisi yada sistem biyotıbbı yaklaşımı, yöntem ve gereçlerine ihtiyaç duyulmaktadır. Bu makalede yumurtalık kanseri vaka çalışması olarak ele alınarak omik verilerinin bütünleştirici analizlerine dayanan sistem biyotıbbı çalışmaları derlenmiştir.

Anahtar Kelimeler

Biyobelirteç, Sistem biyotıbbı

Kaynakça

• Liu, E.T., Lauffenburger, D.A. (2009). Systems biomedicine: concepts and perspectives (Academic Press).
• [2] Bordbar, A., Feist, A.M., Usaite-Black, R., Woodcock, J., Palsson, B.O., Famili, I. (2011). A multitissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC systems biology 5, 180.
• [3] Thiele, I., Swainston, N., Fleming, R.M., Hoppe, A., Sahoo, S., Aurich, M.K., Haraldsdottir, H., Mo, M.L., Rolfsson, O., Stobbe, M.D. (2013). A community-driven global reconstruction of human metabolism. Nature biotechnology 31, 419-425.
• [4] Wang, Y., Eddy, J.A., Price, N.D. (2012). Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC systems biology 6, 153.
• [5] Uhlén, M., Fagerberg, L., Hallström, B.M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A. (2015). Tissue-based map of the human proteome. Science 347, 1260419.
• [6] Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Nookaew, I., Jacobson, P., Walley, A.J., Froguel, P., Carlsson, L.M., Uhlen, M. (2013). Integration of clinical data with a genome- scale metabolic model of the human adipocyte. Molecular systems biology 9, 649.
• [7] Mardinoglu, A., Agren, R., Kampf, C., Asplund, A., Uhlen, M., Nielsen, J. (2014). Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nature communications 5.
• [8] Karagoz, K., Sinha, R., Arga, K.Y. (2015). Triple negative breast cancer: a multi-omics network discovery strategy for candidate targets and driving pathways. Omics: a journal of integrative biology 19, 115-130.
• [9] Calimlioglu, B., Karagoz, K., Sevimoglu, T., Kilic, E., Gov, E., Arga, K.Y. (2015). Tissue-specific molecular biomarker signatures of type 2 diabetes: an integrative analysis of transcriptomics and protein–protein interaction data. Omics: a journal of integrative biology 19, 563-573.
• [10] Kori, M., Gov, E., Arga, K.Y. (2016). Molecular signatures of ovarian diseases: Insights from network medicine perspective. Systems Biology in Reproductive Medicine 62, 266- 282.
• [11] Islam, T., Rahman, M.R., Gov, E., Turanli, B., Gulfidan, G., Haque, M.A., Arga, K.Y., Mollah, N. H. (2018). Drug Targeting and Biomarkers in Head and Neck Cancers. OMICS: A Journal of Integrative Biology 22, 422–36.
• [12] Siegel RL, Ma J, Kimberly DM, and Jemal A. (2016). Cancer statistics. CA Cancer J Clin 66, 7– 30.
• [13] Gov, E., Arga, K.Y. (2016). Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network. IET Systems Biology 10, 219-228.
• [14] Gov, E., Arga, K.Y. (2017). Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer. Scientific Reports p. 7.
• [15] Ayyildiz, D., Gov, E, Sinha, R., Arga KY. (2017). Ovarian cancer differential interactome and network entropy analysis reveal new candidate biomarkers, Omics: a journal of integrative biology 21, 285-294.
• [16] Gov, E., Kori, M., Arga, K.Y. (2017). Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer. OMICS: A Journal of Integrative Biology 21(10): 603-615.
• [17] Gov, E., Kori, M., Arga, K.Y. (2017). RNA based ovarian cancer research from ‘a gene to systems biomedicine’ perspective. Systems Biology in Reproductive Medicine 63, 219-238.