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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.


Pannala, V. R., M. L. Wall, S. K. Estes, I. Trenary, T. P. O’Brien, R. L. Printz, K. C. Vinnakota, J. Reifman, M. Shiota, J. D. Young, and A. Wallqvist. Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat. Scientific Reports. 2018 August 3; 8:11678. [PDF, 30076366]

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, 28355531]

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, 28176778]

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, 27502771]

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, 26431398]

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, 25397773]

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, 25001103]

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, 23875723]

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, 23704901]

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, 23028286]

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, 21713281]

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, 21092312]

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, 19754970]