Systems Biology of Metabolic and Signaling Networks pp 213-239

Part of the Springer Series in Biophysics book series (BIOPHYSICS, volume 16) | Cite as

Systems Biology Approaches to Cancer Energy Metabolism

  • Alvaro Marín-Hernández
  • Sayra Y. López-Ramírez
  • Juan Carlos Gallardo-Pérez
  • Sara Rodríguez-Enríquez
  • Rafael Moreno-Sánchez
  • Emma Saavedra
Chapter

Abstract

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.

Abbreviations

ALDO

Aldolase

DHAP

Dihydroxyacetone phosphate

ENO

Enolase

Ery4P

Erythrose-4-phosphate

FBP

Fructose-1,6-bisphosphate

F6P

Fructose-6-phosphate

F2,6BP

Fructose-2,6-bisphosphate

CJEi or FCC

Flux control coefficient

GAPDH

Glyceraldehyde-3-phosphate dehydrogenase

G3P

Glyceraldehyde-3-phosphate

G6P

Glucose-6-phosphate

GLUT

Glucose transporter

HK

Hexokinase

HPI

Hexosephosphate isomerase

KC

Krebs cycle

LDH

Lactate dehydrogenase

OxPhos

Oxidative phosphorylation

PEP

Phosphoenolpyruvate

Pyr

Pyruvate

PFK-1

Phosphofructokinase type 1

PFKFB3

Phosphofructokinase type 2 B3

6PG

6-phosphogluconate

PGK

Phosphoglycerate kinase

PGAM

3-phosphoglycerate mutase

PYK

Pyruvate kinase

TK

Transketolase

TPI

Triosephosphate isomerase.

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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
  • Emma Saavedra
    • 1
  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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