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METABOLIC NETWORKS

Metabolic Networks

The metabolic network for an organism consists of all known metabolites and the enzymes that catalyze transformations between metabolites. By specifying a particular set of input conditions, such as glucose and oxygen uptake, we use mathematical models to determine the accumulation of biomass and, hence, the growth of a cell. We apply these models to study the metabolism of pathogens under different conditions and exploit these networks to determine drug-dose responses.

Publications

Tewari, S. G., S. T. Prigge, J. Reifman, and A. Wallqvist. Using a genome-scale metabolic network model to elucidate the mechanism of chloroquine action in Plasmodium falciparum. International Journal for Parasitology: Drugs and Drug Resistance. 2017 March 22; 7(2):138-146. [PDF]

Blais, E. M., K. D. Rawls, B. V. Dougherty, Z. I. Li, G. L. Kolling, P. Ye, A. Wallqvist, and J. A. Papin. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nature Communications. 2017 February 8; 8:14250. [PDF]

Wallqvist, A., X. Fang, S. G. Tewari, P. Ye, and J. Reifman. Metabolic host responses to malarial infection during the intraerythrocytic developmental cycle. BMC Systems Biology. 2016 August 8; 10:58. [PDF]

Vital-Lopez, F. G., J. Reifman, and A. Wallqvist. Biofilm formation mechanisms of Pseudomonas aeruginosa predicted via genome-scale kinetic models of bacterial metabolism. PLOS Computational Biology. 2015 October 2; 11(10):e1004452. [PDF]

Song, H. S., J. Reifman, and A. Wallqvist. Prediction of metabolic flux distribution from gene expression data based on the flux minimization principle. PLOS ONE. 2014 November 14; 9(11):e112524. [PDF]

Fang, X., J. Reifman, and A. Wallqvist. Modeling metabolism and stage-specific growth of Plasmodium falciparum HB3 during the intraerythrocytic developmental cycle. Molecular BioSystems. 2014 October 1; 10:2526-2537. [PDF]

Vital-Lopez, F. G., A. Wallqvist, and J. Reifman. Bridging the gap between gene expression and metabolic phenotype via kinetic models. BMC Systems Biology. 2013 July 22; 7:63. [PDF]

Chaudhury, S., M. D. AbdulHameed, N. Singh, G. Tawa, P. M. D'haeseleer, A. T. Zemla, A. Navid, C. E. Zhou, M. C. Franklin, J. Cheung, M. J. Rudolph, J. Love, J. F. Graf, D. A. Rozack, J. L. Dankmeyer, K. Amemiya, S. Daefler, and A. Wallqvist. Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction. PLOS ONE. 2013 May 21; 8(5):e63369. [PDF]

Fang, X., A. Wallqvist, and J. Reifman. Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia. PLOS Computational Biology. 2012 September; 8(9):e1002688. [PDF]

Fang, X., A. Wallqvist, and J. Reifman. Modeling synergistic drug inhibition of Mycobacterium tuberculosis growth in murine macrophages. Molecular BioSystems. 2011 September 1; 7(9):2622-2636. [PDF]

Fang, X., A. Wallqvist, and J. Reifman. Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis. BMC Systems Biology. 2010 November 26; 4:160. [PDF]

Fang, X., A. Wallqvist, and J. Reifman. A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis. BMC Systems Biology. 2009 September 15; 3:92. [PDF]