Transactions on Computational Systems Biology XII pp 146-162

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5945)

Mechanistic Insights into Metabolic Disturbance during Type-2 Diabetes and Obesity Using Qualitative Networks

  • Antje Beyer
  • Peter Thomason
  • Xinzhong Li
  • James Scott
  • Jasmin Fisher


In many complex biological processes quantitative data is scarce, which makes it problematic to create accurate quantitative models of the system under study. In this work, we suggest that the Qualitative Networks (QNs) framework is an appropriate approach for modeling biological networks when only little quantitative data is available. Using QNs we model a metabolic network related to fat metabolism, which plays an important role in type-2 diabetes and obesity. The model is based on gene expression data of the regulatory network of a key transcription factor Mlxipl. Our model reproduces the experimental data and allows in-silico testing of new hypotheses. Specifically, the QN framework allows to predict new modes of interactions between components within the network. Furthermore, we demonstrate the value of the QNs approach in directing future experiments and its potential to facilitate our understanding of the modeled system.


computational modeling Qualitative Networks metabolic pathways obesity type-2 diabetes Mlxipl 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beyer, D.: Relational Programming with CrocoPat. In: Proceedings of the 28th International Conference on Software Engineering (ICSE 2006), Shanghai, May 20-28, pp. 807–810. ACM Press, New York (2006), Google Scholar
  2. 2.
    Beyer, A., Fisher, J.: Unpublished results (2009)Google Scholar
  3. 3.
    von Dassow, G., Meir, E., Munro, E.M., Odell, G.M.: The segment polarity network is a robust developmental module. Nature 406, 188–192 (2000)CrossRefGoogle Scholar
  4. 4.
    Fisher, J., Piterman, N., Hajnal, A., Henzinger, T.A.: Predictive modeling of signaling crosstalk during C. elegans vulval development. PLoS Comput. Biol. 3, e92 (2007)CrossRefGoogle Scholar
  5. 5.
    Giurumescu, C.A., Sternberg, P.W., Asthagiri, A.R.: Intercellular coupling amplifies fate segregation during Caenorhabditis elegans vulval development. PNAS 103, 1331–1336 (2006)CrossRefGoogle Scholar
  6. 6.
    Iizuka, Y., Horikawa, K.: ChREBP: a Glucose-activated Transcription Factor Involved in the Development of Metabolic Syndrome. Endocr. J. 55(4), 617–624 (2008)CrossRefGoogle Scholar
  7. 7.
    Kooner, J.S., Chambers, J.C., Aquilar-Salinas, C.A., Hinds, D.A., Hyde, C.L., Warnes, G.R., Gómez Pérez, F.J., Frazer, K.A., Elliot, P., Scott, J., Milos, P.M., Cox, D.R., Thompson, J.F.: Genome-wide scan identifies variation in Mlxipl associated with plasma triglycerides. Nature Genetics 40, 149–151 (2008)CrossRefGoogle Scholar
  8. 8.
    Li, F., Long, T., Lu, Y., Ouyang, Q., Tang, C.: The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. USA 101, 4781–4786 (2004)CrossRefGoogle Scholar
  9. 9.
    Lin, J., Yang, R., Tarr, P.T., Wu, P., Handschin, C., Li, S., Yang, W., Pei, L., Uldry, M., Tontonoz, P., Newgard, C.B., Spiegelman, B.M.: Hyperlipidemic Effects of Dietary Saturated Fats Mediated through PGC-1β Coactivation of SREBP. Cell 120, 261–273 (2005)CrossRefGoogle Scholar
  10. 10.
    Mootha, V.K., Lindgren, C.M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., Houstis, N., Daly, M.J., Patterson, N., Mesirov, J.P., Golub, T.R., Tamayo, P., Spiegelman, B.M., Lander, E.S., Hirschhorn, J.N., Altshuler, D., Groop, L.C.: PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics 34, 267–273 (2003)CrossRefGoogle Scholar
  11. 11.
    Neels, J.G., Olefsky, J.M.: Inflamed fat: what starts the fire? J. Clin. Invest. 116, 33–35 (2006)CrossRefGoogle Scholar
  12. 12.
    Schaub, M.A., Henzinger, T.A., Fisher, J.: Qualitative networks: a symbolic approach to analyze biological signaling networks. BMC Systems Biology 1, 4 (2007), CrossRefGoogle Scholar
  13. 13.
    Schaub, M.A.: QNBuilder v0.1 User Manual, Draft (2008)Google Scholar
  14. 14.
    Schaub, M.A.: QNBuilder v0.1.c (2008),
  15. 15.
    Scott, J.: Oxidative stress in adipose tissue is a unifying trigger for inflammation and insulin resistance (manuscript and personal communication)Google Scholar
  16. 16.
    Shmulevich, I., Zhang, W.: Binary analysis and optimization-based normalization of gene expression data. Bioinformatics 18, 555–565 (2002)CrossRefGoogle Scholar
  17. 17.
    Shmulevich, I., Lahdesmaki, H., Dougherty, E.R., Astola, J., Zhang, W.: The role of certain Post classes in Boolean Network models of genetic networks. Proc. Natl. Acad. Sci. USA 100, 10734–10739 (2003)CrossRefGoogle Scholar
  18. 18.
    Uyeda, K., Repa, J.J.: Carbohydrate hydrate response element binding protein, ChREBP, a transcription factor coupling hepatic glucose utilization and lipid synthesis. Cell Metabolism 4, 107–110 (2006)CrossRefGoogle Scholar
  19. 19.
    Wellen, K.E., Fucho, R., Gregor, M.F., Furuhashi, M., Morgan, C., Lindstad, T., Vaillancourt, E., Gorgun, C.Z., Saatcioglu, F., Hotamisligil, G.S.: Coordinated Regulation of Nutrient and Inflammatory Responses by STAMP2 is Essential for Metabolic Homeostasis. Cell 129, 537–548 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Antje Beyer
    • 1
  • Peter Thomason
    • 2
  • Xinzhong Li
    • 2
  • James Scott
    • 2
  • Jasmin Fisher
    • 3
  1. 1.Department of GeneticsUniversity of CambridgeCambridgeUK
  2. 2.National Heart and Lung InstituteImperial College LondonUK
  3. 3.Microsoft ResearchCambridgeUK

Personalised recommendations