Skip to main content

Advertisement

SpringerLink
Log in

Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes

  • Special Issue: Mathematics to Support Drug Discovery and Development
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

This paper begins to build a theoretical framework that would enable the pharmaceutical industry to use network complexity measures as a way to identify drug targets. The variability of a betweenness measure for a network node is examined through different methods of network perturbation. Our results indicate a robustness of betweenness centrality in the identification of target genes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Azzarelli B, Miravalle L, Vidal R (2004) Immunolocalization of the oligodendrocyte transcription factor 1 (Olig1) in brain tumors. J Neuropathol Exp Neurol 63:170–179

    Article  Google Scholar 

  • Bavelas A (1950) Communication patterns in task-oriented groups. J Acoust Soc Am 22(6):725–730

    Article  Google Scholar 

  • Berscheminski J, Brun J, Speiseder T, Wimmer P, Ip WH, Terzic M, Dobner T, Schreiner S (2016) Sp100A is a tumor suppressor that activates p53-dependent transcription and counteracts E1A/E1B-55K-mediated transformation. Oncogene 35:3178–3189

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5):1170–1182

    Article  Google Scholar 

  • Breitkreutz D, Hlatky L, Rietman E, Tuszynski J (2012) Molecular signaling network complexity is correlated with cancer patient survivability. Proc Nat Acad Sci 109(23):9209–9212

    Article  Google Scholar 

  • Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, Sulman EP, Anne SL, Doetsch F, Colman H, Lasorella A, Aldape K, Califano A, Iavaone A (2010) The transcriptional network for mesenchymal transformation of brain tumours. Nature 463:318–325

    Article  Google Scholar 

  • Cooper LAD, Gutman DA, Chisolm C, Appin C, Kong J, Rong Y, Kurc T, Van Meir EG, Saltz JH, Moreno CS, Brat DJ (2012) The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma. Am J Pathol 180(5):2108–2119

    Article  Google Scholar 

  • Davis FB, Tang H-Y, Shih A, Keating T, Lansing L, Hercbergs A, Fenstermaker RA, Mousa A, Mousa SA, Davis PJ, Lin H-Y (2006) Acting via a cell surface receptor, thyroid hormone is a growth factor for glioma cells. Cancer Res 66:7270–7275

    Article  Google Scholar 

  • del Rio G, Koschützki D, Coello G (2009) How to identify essential genes from molecular networks? BMC Syst Biol 3(1):102

    Article  Google Scholar 

  • Epskamp S, Borsboom D, Fried EI (2017) Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods 1–18

  • Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 35–41

  • Han J-DJ, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJ, Cusick ME, Roth FP (2004) Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430(6995):88

    Article  Google Scholar 

  • Jakovcevski I, Zecevic N (2005) Olig transcription factors are expressed in oligodendrocyte and neuronal cells in human fetal CNS. J Neurosci 25:10064–10073

    Article  Google Scholar 

  • Jeong H, Mason SP, Barabási A-L, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411(6833):41

    Article  Google Scholar 

  • Lachmann A, Giorgi FM, Lopez G, Califano A (2016) ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32(14):2233–2235

    Article  Google Scholar 

  • Li M, Wang J, Chen X, Wang H, Pan Y (2011) A local average connectivity-based method for identifying essential proteins from the network level. Comput Biol Chem 35(3):143–150

    Article  MathSciNet  MATH  Google Scholar 

  • Madhavan S, Zenklusen J-C, Kotliarov Y, Sahni H, Fine HA, Buetow K (2009) Rembrandt: helping personalized medicine become a reality through integrative translational research. Mol Cancer Res 7(2):157–167

    Article  Google Scholar 

  • Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform 7:S7

    Article  Google Scholar 

  • Mistry D, Wise RP, Dickerson JA (2017) DiffSLC: a graph centrality method to detect essential proteins of a protein–protein interaction network. PLoS ONE 12(11):e0187091

    Article  Google Scholar 

  • Mo W, Chen J, Patel A, Zhang L, Chau V, Li Y, Cho W, Lim K, Xu J, Lazar AJ et al (2013) CXCR4/CXCL12 mediate autocrine cell-cycle progression in NF1-associated malignant peripheral nerve sheath tumors. Cell 152(5):1077–1090

    Article  Google Scholar 

  • Motizuki M, Isogaya K, Miyake K, Ikushima H, Kubota T, Miyazono K, Saitoh M, Miyazawa K (2013) Oligodendrocyte transcription factor 1 (Olig1) is a smad cofactor involved in cell motility induced by transforming growth factor-\(\beta \). J Biol Chem 288:18911–18922

    Article  Google Scholar 

  • Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Pan Y, Smithson LJ, Ma Y, Hambardzumyan D, Gutmann DH (2017) Ccl5 establishes an autocrine high-grade glioma growth regulatory circuit critical for mesenchymal glioblastoma survival. Oncotarget 8:32977–32989

    Google Scholar 

  • Pan Y, Duron C, Bush EC, Ma Y, Sims PA, Gutmann DH, Radunskaya A, Hardin J (2018) Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma. PLoS ONE 13(5):e0190001

  • Ramadan E, Alinsaif S, Hassan MR (2016) Network topology measures for identifying disease-gene association in breast cancer. BMC Bioinform 17(7):274

    Article  Google Scholar 

  • Sabo JK, Heine V, Silbereis JC, Schirmer L, Levison SW, Rowitch DH (2017) Olig1 is required for noggin-induced neonatal myelin repair. Ann Neurol 81:560–571

    Article  Google Scholar 

  • Sahin A, Velten M, Pietsch T, Knuefermann P, Okuducu A, Hahne J, Wernert N (2005) Inactivation of Ets 1 transcription factor by a specific decoy strategy reduces rat C6 glioma cell proliferation and mmp-9 expression. Int J Mol Med 15:771–776

    Google Scholar 

  • Schlierf B, Friedrich RP, Roerig P, Felsberg J, Reifenberger G, Wegner M (2007) Expression of SoxE and SoxD genes in human gliomas. Neuropathol Appl Neurobiol 33:621–630

    Article  Google Scholar 

  • Segarra S, Ribeiro A (2016) Stability and continuity of centrality measures in weighted graphs. IEEE Trans Signal Process 64(3):543–555

    Article  MathSciNet  MATH  Google Scholar 

  • Selagea L, Mishra A, Anand M, Ross J, Tucker-Burden C, Kong J, Brat DJ (2016) EGFR and C/EBP-\(\beta \) oncogenic signaling is bidirectional in human glioma and varies with the C/EBP-\(\beta \) isoform. FASEB 30:4098–4108

    Article  Google Scholar 

  • Solga AC, Pong WW, Kim K-Y, Cimino PJ, Toonen JA, Walker J, Wylie T, Magrini V, Griffith M, Griffith OL et al (2015) RNA sequencing of tumor-associated microglia reveals Ccl5 as a stromal chemokine critical for neurofibromatosis-1 glioma growth. Neoplasia 17(10):776–788

    Article  Google Scholar 

  • Tang X, Wang J, Zhong J, Pan Y (2014) Predicting essential proteins based on weighted degree centrality. IEEE/ACM Trans Comput Biol Bioinform (TCBB) 11(2):407–418

    Article  Google Scholar 

  • Vallabhajosyula RR, Chakravarti D, Lutfeali S, Ray A, Raval A (2009) Identifying hubs in protein interaction networks. PLoS ONE 4(4):e5344

    Article  Google Scholar 

  • Wasylyk C, Schlumberger S, Criqui-Filipe P, Wasylyk B (2002) Sp100 interacts with ETS-1 and stimulates its transcriptional activity. Mol Cellul Biol 22:2687–2702

    Article  Google Scholar 

  • Xie SL, Fan S, Zhang SY, Chen WX, Li QX, Pan GK, Zhang HQ, Wang WW, Weng B, Zhang Z, Li JS, Lin ZY (2018) SOX8 regulates cancer stem-like properties and cisplatin-induced EMT in tongue squamous cell carcinoma by acting on the Wnt\(\beta \)-catenin pathway. Int J Cancer 142:1252–1265

    Article  Google Scholar 

  • Yin J, Oh Y, Kim J, Kim S, Choi E, Kim T, Hong J, Chang N, Cho H, Sa J, Kim J, Kwon H, Park S, Lin W, Nakano I, Gwak H, Yoo H, Lee S, Lee J, Kim J, Kim S, Nam D, Park M, Park J (2017) Transglutaminase 2 inhibition reverses mesenchymal transdifferentiation of glioma stem cells by regulating C/EPB\(\beta \) signaling. Cancer Res 77:4973–4984

    Article  Google Scholar 

  • Zhang X, Xu J, Xiao W (2013) A new method for the discovery of essential proteins. PLoS ONE 8(3):e58763

    Article  Google Scholar 

  • Zhang S, Zhu C, Zhu L, Liu H, Liu S, Zhao N, Wu J, Huang X, Zhang Y, Jin J, Ji T, Ding X (2014) Oncogenicity of the transcription factor SOX8 in hepatocellular carcinoma. Med Oncol 31

Download references

Author information

Authors and Affiliations

  1. Math Department, Claremont Graduate University, Claremont, CA, 91711, USA

    Christina Durón

  2. Neurology and Neurological Sciences, Stanford University Medical Center, Palo Alto, CA, USA

    Yuan Pan

  3. Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

    David H. Gutmann

  4. Math Department, Pomona College, Claremont, CA, 91711, USA

    Johanna Hardin & Ami Radunskaya

Authors
  1. Christina Durón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David H. Gutmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Johanna Hardin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ami Radunskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ami Radunskaya.

Additional information

This work was partially supported by NIH R01CA195692.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Durón, C., Pan, Y., Gutmann, D.H. et al. Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes. Bull Math Biol 81, 3655–3673 (2019). https://doi.org/10.1007/s11538-018-0526-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11538-018-0526-z

Keywords

Search

Navigation