Systems Biology Approaches to Cancer Energy Metabolism

Marín‐Hernández, Álvaro; López-Ramírez, Sayra Y.; Gallardo‐Pérez, Juan Carlos; Rodrı́guez-Enrı́quez, Sara; Moreno‐Sánchez, Rafael; Saavedra, Emma

doi:10.1007/978-3-642-38505-6_9

Cited by 5 publications

(9 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Variation in the K m values for aldolase (ALDO) or TPI also resulted in significant changes in the Fru1,6BP and dihydroxyacetone phosphate (DHAP) concentrations, respectively. Like the previous model , the present model exhibited high sensitivity to variation in the ATPase kinetics (Table S1).…”

Section: Resultssupporting

confidence: 51%

“…For any given pathway reaction, the experimentally determined V max value is the result of combination of the various catalytic capacities of the participating isoforms. Therefore, the rate equations for GLUT and HK were modified to take into consideration the contribution of the two main isoforms, as previously proposed . The fractional isoform contributions (f1 and f2) included in the rate equations 1 and 2 (i.e.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast to the change in isoforms expressed under hypoglycemia, cancer cells grown under 25 m m glucose and expose to severe hypoxia (0.1%) maintain high contents of the low‐affinity isoforms GLUT1 and HKII, which induces an increase in total GLUT and HK activities and glycolytic flux, compared to normoxia . This suggests that changing the isoform ratios of the main controlling steps of tumor glycolysis is an efficient strategy to modulate the glycolytic flux.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms

et al. 2014

Self Cite

View full text Add to dashboard Cite

The effect of hypoglycemia on the contents of glycolytic proteins, activities of enzymes/transporters and flux of HeLa and MCF-7 tumor cells was experimentally analyzed and modeled in silico. After 24 h hypoglycemia (2.5 mM initial glucose), significant increases in the protein levels of glucose transporters 1 and 3 (GLUT 1 and 3) (3.4 and 2.1-fold, respectively) and hexokinase I (HKI) (2.3-fold) were observed compared to the hyperglycemic standard cell culture condition (25 mM initial glucose). However, these changes did not bring about a significant increase in the total activities (V max ) of GLUT and HK; instead, the affinity of these proteins for glucose increased, which may explain the twofold increased glycolytic flux under hypoglycemia. Thus, an increase in more catalytically efficient isoforms for two of the main controlling steps was sufficient to induce increased flux. Further, a previous kinetic model of tumor glycolysis was updated by including the ratios of GLUT and HK isoforms, modified pyruvate kinase kinetics and an oxidative phosphorylation reaction. The updated model was robust in terms of simulating most of the metabolite levels and fluxes of the cells exposed to various glycemic conditions. Model simulations indicated that the main controlling steps were glycogen degradation > HK > hexosephosphate isomerase under hyper-and normoglycemia, and GLUT > HK > glycogen degradation under hypoglycemia. These predictions were experimentally evaluated: the glycolytic flux of hypoglycemic cells was more sensitive to cytochalasin B (a GLUT inhibitor) than that of hyperglycemic cells. The results indicated that cancer glycolysis should be inhibited at multiple controlling sites, regardless of external glucose levels, to effectively block the pathway. DatabaseThe mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at

show abstract

Section: Resultssupporting

confidence: 51%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because mitochondria are critical to life, mitochondrial dysfunction is hypothesised to be the source of ageing (Alberts et al, 2015;Wellstead, 2012), neuro-degenerative diseases Drion et al, 2012;Francis et al, 2012;Le Masson et al, 2014;Poliquin et al, 2013;Wellstead, 2012; cancer (Gogvadze et al, 2008;Marin-Hernandez et al, 2014;Solaini et al, 2011) and other diseases (Nunnari and Suomalainen, 2012;Wallace, 2005). Although mathematical models of mitochondria exist already Cortassa and Aon, 2014;Wu et al, 2007), it is hoped that the engineering-inspired bond graph approach of this paper will shed further light on the function and dysfunction of mitochondria.…”

Section: Resultsmentioning

confidence: 98%

Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction

Gawthrop

2017

IEEE Trans.on Nanobioscience

View full text Add to dashboard Cite

Engineering systems modelling and analysis based on the bond graph approach has been applied to biomolecular systems. In this context, the notion of a Faraday-equivalent chemical potential is introduced which allows chemical potential to be expressed in an analogous manner to electrical volts thus allowing engineering intuition to be applied to biomolecular systems. Redox reactions, and their representation by half-reactions, are key components of biological systems which involve both electrical and chemical domains. A bond graph interpretation of redox reactions is given which combines bond graphs with the Faradayequivalent chemical potential. This approach is particularly relevant when the biomolecular system implements chemoelectrical transduction -for example chemiosmosis within the key metabolic pathway of mitochondria: oxidative phosphorylation.An alternative way of implementing computational modularity using bond graphs is introduced and used to give a physically based model of the mitochondrial electron transport chain (ETC). To illustrate the overall approach, this model is analysed using the Faradayequivalent chemical potential approach and engineering intuition is used to guide affinity equalisation: a energy based analysis of the mitochondrial electron transport chain.

show abstract

“…The supply of energy is essential to life and disruption of energy supply has been implicated in many diseases such as cardiac failure [48,49], Parkinson's disease [50][51][52][53][54] and cancer [55][56][57]. Therefore it seems natural to apply the energy-based methods of this paper to investigate such systems.…”

Section: Resultsmentioning

confidence: 99%

Energy-based analysis of biomolecular pathways

Gawthrop

Crampin

2017

Proc. R. Soc. A.

View full text Add to dashboard Cite

Decomposition of biomolecular reaction networks into pathways is a powerful approach to the analysis of metabolic and signalling networks. Current approaches based on analysis of the stoichiometric matrix reveal information about steady-state mass flows (reaction rates) through the network. In this work we show how pathway analysis of biomolecular networks can be extended using an energy-based approach to provide information about energy flows through the network. This energy-based approach is developed using the engineeringinspired bond graph methodology to represent biomolecular reaction networks. The approach is introduced using glycolysis as an exemplar; and is then applied to analyse the efficiency of free energy transduction in a biomolecular cycle model of a transporter protein (Sodium-Glucose Transport Protein 1, SGLT1). The overall aim of our work is to present a framework for modelling and analysis of biomolecular reactions and processes which considers energy flows and losses as well as mass transport. *

show abstract

Systems Biology Approaches to Cancer Energy Metabolism

Cited by 5 publications

References 74 publications

Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms

Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms

Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction

Energy-based analysis of biomolecular pathways

Contact Info

Product

Resources

About