Volume 3 • Issue 1 • 1000116 J Tumor Res, an open access journal ISSN: JTR Research Article Open Access Montero et al., J Tumor Res 2017, 3:1 DOI: 10.4172/jtr.1000110 Research Article OMICS International Journal of Tumor Research J o u r n a l of T u m o r R e se a r c h *Corresponding author: Nieto-Villar JM, Department of Chemical-Physics, A. Alzola Group of Thermodynamics of Complex Systems M.V. Lomonosov, University of Havana, Havana, 10400-Cuba, Tel: +351 220428557; E-mail: nieto@fq.uh.cu Received February 07, 2017; Accepted February 24, 2017; Published February 28, 2017 Citation: Montero S, Durán I, Pomuceno JP, Martín RR, Mesa MD, et al. (2017) How much Damage can make the Glucose in Cancer? J Tumor Res 3: 116. Copyright: © 2017 Montero S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. How much Damage can make the Glucose in Cancer? Montero S 1,2 , Durán I 1 , Pomuceno JP 1 , Martín RR 1 , Mesa MD 1 , Mansilla R 3,1 , Cocho G 4,1 and Nieto-Villar JM 1 * 1 Department of Chemical-Physics, A. Alzola Group of Thermodynamics of Complex Systems M.V. Lomonosov, University of Havana, Havana, Cuba-10400 2 Departement of Basics Science, University of Medical Science of Havana, Havana, Cuba-10400 3 Centro de Investigaciones Interdisciplinarias en Ciencias y Humanidades, UNAM, México 4 Instituto de Sistemas Complejos C3 and Instituto de Física de la UNAM, México Abstract Using the entropy production rate method to determine the inluence of glucose on a glycolysis model for HeLa tumor line, the greater complexity and robustness hypoglycemic phenotype was established. On the other side, inside the same metabolic phenotype a higher glucose concentration corresponds to a higher entropy production rate. It is concluded that this behavior is indicative of the directional character and stability of the dynamical behavior of cancer glycolysis. Keywords: Cancer; Glycolysis; Entropy production rate; Glucose Introduction Cancer is a generic name given to a complex interaction network of malignant cells that have lost their specialization and control over normal growth. his network could be modelled as a nonlinear dynamical system, self-organized in time and space, far from thermodynamic equilibrium, exhibiting high complexity, robustness, and adaptability [1]. Cancer cells, for the most part, show a high glycolytic rate and low pyruvate oxidation rate compared to normal cells, which brings with it a high consumption of glucose of the extracellular medium and an increase in the production of lactate, phenomenon known like “Warburg Efect” [2]. Increased glycolytic rate is beneicial for cell proliferation, as it provides the intermediates necessary for the synthesis of new nucleotides, lipids, amino acids and the generation of reducing power, all of which are necessary for cell division [3].he molecular mechanism behind the constitutive overexpression of aerobic glycolysis is not yet fully deined. Activation of oncogenes and tumor suppressor genes such as ras, c-myc, src and TP53 have implications for the regulation of aerobic glycolysis. hese genes cause alterations in signalling pathways of growth factors and these in turn exert control over cellular metabolism [4]. Due to a combination of high glucose consumption rates by tumor cells and reduced tumor vascularization, the glucose concentration in the tumors can be 3 to 10 times lower than in normal tissues, according to the stage of its development. herefore, tumor cells must develop strategies for their growth and survival in metabolically unfavorable environments [5]. Since the last years, cancer glycolysis has been a target in oncology research [6]. he signiicant increase of glycolysis rate observed in tumors has been recently veriied, yet only a few oncologists or cancer researchers understand the full scope of Warburg’s work [6,7] despite of its great importance. Altered energy metabolism is proving to be as widespread in cancer cells as many of the other cancer-associated traits that have been accepted as hallmarks of cancer [8]. he regulation of metabolism, relevant to senescence process, would be a key to improve and identify new anti-cancer therapies in the future. he complex systems theory and the thermodynamics formalism in the last years have shown to be a theoretical framework as well as a useful tool to understand and forecast the evolution of the tumor growth [9-17]. he goal of this work is to extend the thermodynamics formalism previously developed [18-22] to the metabolic rate of human cancer cells. he manuscript is organized as follow: Firstly, a theoretical framework based on thermodynamics formalism, particularly the entropy production rate is presented. hen results and discussion are presented. Finally, some concluding remarks are presented. Materials and Methods Kinetic model of cancer glycolysis he model used was proposed by Marin et al. [23] for the glycolytic network of HeLa tumor cell-lines growth under three metabolic states: Hypoglycemia (2.5 mM), Normoglycemia (5 mM) and Hyperglycemya (25 mM) during enough time to induce phenotypic change in cellular metabolisms. However, the growth saturation was not attained in this phase. In the other stage the cells were exposed to diferent glucose concentrations: 2.5 mM, 5 mM y 25 mM until they reach the stationary state. he rate of entropy production was calculated using the glycolysis network model of HeLa cell lines at steady state. he modelling of the metabolic network was made in the biochemical network simulator COPASI v 4.6 (Build 32) (http://www.copasi.org). he parameters and concentration values used were reported by Marin et al. [23]. hermodynamics framework As we know from classic thermodynamics, if the constraints of a system are the temperature T and the pressure P, then the entropy production can be evaluated using Gibbs’s free energy [24], as: 1 i Tp S dG T δ =− (1) If the time derivative of (1) is taken, we have that: 1 Tp i dG S dt T dt δ =− (2)