title: Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT.
abstract: Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment.
authors.affiliation: Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
authors.forename: Rasmus
authors.initials: R
authors.lastname: Agren
authors.forename: Sergio
authors.initials: S
authors.lastname: Bordel
authors.forename: Adil
authors.initials: A
authors.lastname: Mardinoglu
authors.forename: Natapol
authors.initials: N
authors.lastname: Pornputtapong
authors.forename: Intawat
authors.initials: I
authors.lastname: Nookaew
authors.forename: Jens
authors.initials: J
authors.lastname: Nielsen
mesh_terms: D000465:Algorithms; D000818:Animals; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D055442:Metabolome; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction
chemical_list: D020543:Proteome