Reconstruction of Metabolic Networks from High‐Throughput Metabolite Profiling Data

Abstract:  We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high‐throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well‐established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

[1]  M Graziani,et al.  ATP requirement of the sodium‐dependent magnesium extrusion from human red blood cells. , 1989, The Journal of physiology.

[2]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[3]  V. Lew,et al.  Refinement and evaluation of a model of Mg2+ buffering in human red cells. , 1999, European journal of biochemistry.

[4]  Peter D. Karp,et al.  Evaluation of computational metabolic-pathway predictions for Helicobacter pylori , 2002, Bioinform..

[5]  P. J. Garrahan,et al.  The interaction of adenosinetriphosphate and inorganic phosphate with the sodium pump in red cells. , 1975, The Journal of physiology.

[6]  Fangping Mu,et al.  Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites , 2006, Bioinform..

[7]  G J Kemp,et al.  Erythrocyte phosphate metabolism and pH in vitro: a model for clinical phosphate disorders in acidosis and alkalosis. , 1988, Mineral and electrolyte metabolism.

[8]  M. Barbagallo,et al.  Effects of glutathione on red blood cell intracellular magnesium: relation to glucose metabolism. , 1999, Hypertension.

[9]  Barrett C. Foat,et al.  Predictive modeling of genome-wide mRNA expression: from modules to molecules. , 2007, Annual review of biophysics and biomolecular structure.

[10]  Pedro Mendes,et al.  Artificial gene networks for objective comparison of analysis algorithms , 2003, ECCB.

[11]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[12]  P W Kuchel,et al.  Hypophosphite ion as a 31P nuclear magnetic resonance probe of membrane potential in erythrocyte suspensions. , 1988, Biophysical journal.

[13]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[14]  I. A. Rose,et al.  Control of glycolysis in the human red blood cell. , 1966, The Journal of biological chemistry.

[15]  Fergus J Cameron,et al.  Diabetic ketoacidosis, hyperosmolarity and hypernatremia: are high‐carbohydrate drinks worsening initial presentation? , 2005, Pediatric diabetes.

[16]  S. Rapoport,et al.  Cellular and molecular biology of erythrocytes , 1974 .

[17]  Joshua D Rabinowitz,et al.  Cellular metabolomics of Escherchia coli , 2007, Expert review of proteomics.

[18]  Y. Tu,et al.  Quantitative noise analysis for gene expression microarray experiments , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[19]  I. Kurtz,et al.  New insights into the pathophysiology of the dysnatremias: a quantitative analysis. , 2004, American journal of physiology. Renal physiology.

[20]  Adam A. Margolin,et al.  Reverse engineering cellular networks , 2006, Nature Protocols.

[21]  Bernhard O. Palsson,et al.  Dynamic simulation of the human red blood cell metabolic network , 2001, Bioinform..

[22]  Joachim Kopka,et al.  Methods, applications and concepts of metabolite profiling: primary metabolism. , 2007, EXS.

[23]  Chris Wiggins,et al.  Process pathway inference via time series analysis , 2002, physics/0206031.

[24]  Joachim Kopka,et al.  Methods, applications and concepts of metabolite profiling: primary metabolism. , 2007 .

[25]  J. Keurentjes,et al.  Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry , 2007, Nature Protocols.

[26]  R S Balaban,et al.  Intracellular pH and inorganic phosphate content of heart in vivo: a 31P-NMR study. , 1988, The American journal of physiology.

[27]  Sylvia Butler,et al.  PROTEIN-BINDING OF INORGANIC PHOSPHATE IN PLASMA OF NORMAL SUBJECTS AND PATIENTS WITH RENAL DISEASE* , 1959 .

[28]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[29]  Kai Wang,et al.  Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes , 2006, RECOMB.

[30]  Kenneth J. Kauffman,et al.  Advances in flux balance analysis. , 2003, Current opinion in biotechnology.

[31]  David Wile,et al.  Characteristics and mortality of severe hyponatraemia – a hospital‐based study , 2006, Clinical endocrinology.