Systems Biology Approaches to Cancer Energy Metabolism

  • Alvaro Marín-Hernández
  • Sayra Y. López-Ramírez
  • Juan Carlos Gallardo-Pérez
  • Sara Rodríguez-Enríquez
  • Rafael Moreno-SánchezEmail author
  • Emma SaavedraEmail author
Part of the Springer Series in Biophysics book series (BIOPHYSICS, volume 16)


Application of Systems Biology approaches to energy metabolism of cancer cells help in the understanding of their controlling and regulatory mechanisms and identification of new drug targets. Our group built and validated a kinetic model of tumor glycolysis based on the experimental determination of all the enzyme/transporter kinetic parameters, metabolite concentrations, and fluxes in tumor cells. Model predictions enabled to understand how glycolysis is controlled and allowed identification of the main controlling steps which can be the most promising therapeutic targets. In this chapter, the model was extended to determine the contribution on the pathway function of the expression of different glycolytic isoforms displaying different catalytic properties, a feature commonly observed in tumor cells subjected to hypoxia. Model predictions now indicated that, by fully changing the glucose transporter (GLUT), hexokinase (HK), or both, from low- to high affinity isoforms, the glycolytic flux can be increased (GLUT+HK>GLUT>>HK); however, this concurred with a marked deregulation of the adenine nucleotides concentration. To gradually increase glycolytic flux with no alteration of adenine nucleotides homeostasis, which is closer to the physiological response of tumor cells, the model indicated that simultaneous expression in different ratios of GLUT and HK isoforms with different affinities should be accomplished. Mitochondrial metabolism is also active and essential for cancer cells. Therefore, a cancer energy metabolism model, including glycolysis and oxidative phosphorylation (Krebs cycle, respiratory chain, Pi/ADP transport, ATP synthase), should identify the most appropriate sites for successful multi-target therapies.


Metabolite Concentration Krebs Cycle Glycolytic Flux System Biology Approach Triose Phosphate Isomerase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





Dihydroxyacetone phosphate












Flux control coefficient


Glyceraldehyde-3-phosphate dehydrogenase






Glucose transporter




Hexosephosphate isomerase


Krebs cycle


Lactate dehydrogenase


Oxidative phosphorylation






Phosphofructokinase type 1


Phosphofructokinase type 2 B3




Phosphoglycerate kinase


3-phosphoglycerate mutase


Pyruvate kinase




Triosephosphate isomerase.



The present work was partially supported by the following grants from CONACyT-México: Nos. 180322 (AM-H); 107183 (SR-E); 80534 and 123636 (RM-S); and 83084 and 178638 (ES); and from the Instituto de Ciencia y Tecnología del Distrito Federal No. PICS08-5 (RM-S).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Alvaro Marín-Hernández
    • 1
  • Sayra Y. López-Ramírez
    • 1
  • Juan Carlos Gallardo-Pérez
    • 1
  • Sara Rodríguez-Enríquez
    • 1
    • 2
  • Rafael Moreno-Sánchez
    • 1
    Email author
  • Emma Saavedra
    • 1
    Email author
  1. 1.Departamento de BioquímicaInstituto Nacional de Cardiología Ignacio ChávezTlalpanMexico
  2. 2.Laboratorio de Medicina TraslacionalInstituto Nacional de CancerologíaMéxico D.F.Mexico

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