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

• Published in: Nature biotechnology 2010-12, Vol.28 (12), p.1279-1285
• Main Authors: Palsson, Bernhard Ø, Lewis, Nathan E, Schramm, Gunnar, Bordbar, Aarash, Schellenberger, Jan, Andersen, Michael P, Cheng, Jeffrey K, Patel, Nilam, Yee, Alex, Lewis, Randall A, Eils, Roland, König, Rainer
• Format: Article
• Language: English
• Subjects: 631/114; 631/1647/767/1658; 631/443/319/333/1465; 692/699/375/365/1283; Acetylcholine - metabolism; Agriculture; Alzheimer's disease; analysis; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Brain; Brain - cytology; Brain - metabolism; Cell metabolism; Cellular biology; Computer Simulation; Databases, Factual; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling; General aspects; Genetic aspects; Genomics - methods; Genotype; Health. Pharmaceutical industry; Humans; Industrial applications and implications. Economical aspects; Life Sciences; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Metabolic Networks and Pathways; Metabolism; Metabolites; Miscellaneous; Models, Biological; Neurons; Phenotype; Physiological aspects; Properties
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt.1711

The metabolism of tissues often involves interactions between several types of cell. Lewis et al . model metabolism within and between neurons in the human brain, gaining insight into energy metabolism and Alzheimer's disease. 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.