Large-scale in silico modeling of metabolic interactions between cell types in the human brain

ABSTRACT Metabolic interactions between multiple cell types are difficult to model using existing approaches. Here we present a workflow that integrates gene expression data, proteomics data

and literature-based manual curation to model human metabolism within and between different types of cells. Transport reactions are used to account for the transfer of metabolites between

models of different cell types via the interstitial fluid. We apply the method to create models of brain energy metabolism that recapitulate metabolic interactions between astrocytes and

various neuron types relevant to Alzheimer's disease. Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential

effects of the disease on cell types and regions of the brain. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human

tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis. Access through your institution Buy or subscribe This is a preview of subscription

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The authors thank G. Gibson at Cornell University, I. Thiele at the University of Iceland and M. Abrams, M. Mo and C. Barrett at UCSD for suggestions pertaining to this work. This work was

funded in part by a Fulbright fellowship, a National Science Foundation IGERT Plant Systems Biology training grant (no. DGE-0504645), US National Institutes of Health grants

2R01GM068837_05A1 and RO1 GM071808 and the Helmholtz Alliance on Systems Biology and the BMBF by the NGFN+ neuroblastoma project ENGINE. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS *

Department of Bioengineering, University of California, San Diego, La Jolla, California, USA., Nathan E Lewis, Aarash Bordbar, Michael P Andersen, Jeffrey K Cheng, Nilam Patel, Alex Yee 

& Bernhard Ø Palsson * Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology and Bioquant, University of Heidelberg, Heidelberg, Germany

Gunnar Schramm, Roland Eils & Rainer König * Bioinformatics Program, University of California, San Diego, La Jolla, California, USA., Jan Schellenberger * Department of Economics,

Massachusetts Institute of Technology, Cambridge, Massachusetts, USA Randall A Lewis * Department of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

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A.Y., N.P., M.P.A. and B.O.P. conceived and designed the model. N.E.L., J.K.C., G.S., R.K., R.E., J.S., A.B. and R.A.L. performed data analyses. The manuscript was written by N.E.L., G.S.,

J.S., A.B. and B.O.P. CORRESPONDING AUTHOR Correspondence to Bernhard Ø Palsson. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial interests. SUPPLEMENTARY

INFORMATION SUPPLEMENTARY TEXT AND FIGURES Supplementary Notes (PDF 1971 kb) SUPPLEMENTARY MODEL 1 All neuron types normal (ZIP 72 kb) SUPPLEMENTARY MODEL 2 All neuron types normal elderly

(ZIP 72 kb) SUPPLEMENTARY MODEL 3 All neuron types Alzheimer's (ZIP 72 kb) SUPPLEMENTARY TABLE 1 Model rxns (XLS 317 kb) SUPPLEMENTARY TABLE 2 Model metabolites (XLS 109 kb)

SUPPLEMENTARY TABLE 3 Model genes (XLS 50 kb) SUPPLEMENTARY TABLE 4 Model S. matrix (ZIP 904 kb) SUPPLEMENTARY TABLE 5 Model parameters (XLS 24 kb) SUPPLEMENTARY TABLE 6 Significant Pathwave

pathways (XLS 25 kb) SUPPLEMENTARY TABLE 7 Protein data accession numbers (XLS 18 kb) SUPPLEMENTARY TABLE 8 Comparison with other models (XLS 18 kb) SUPPLEMENTARY TABLE 9 Pathwave pathway

classes (XLS 42 kb) SUPPLEMENTARY TABLE 10 Pathwave exception metabolites (XLS 15 kb) SUPPLEMENTARY TABLE 11 Significant Pathwave features (XLS 168 kb) RIGHTS AND PERMISSIONS Reprints and

permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Lewis, N., Schramm, G., Bordbar, A. _et al._ Large-scale _in silico_ modeling of metabolic interactions between cell types in the human

brain. _Nat Biotechnol_ 28, 1279–1285 (2010). https://doi.org/10.1038/nbt.1711 Download citation * Published: 21 November 2010 * Issue Date: December 2010 * DOI:

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