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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

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 1326 KB]

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 2307 KB]

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 1425 KB]

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 723 KB]

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 2535 KB]

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 1236 KB]

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 609 KB]

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 1116 KB]

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 2157 KB]

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 1094 KB]

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 1152 KB]