Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT
Artikel i vetenskaplig tidskrift, 2012

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.

Författare

Rasmus Ågren

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Sergio Velasco

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Adil Mardinoglu

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Natapol Pornputtapong

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Intawat Nookaew

Chalmers, Kemi- och bioteknik, Livsvetenskaper

Jens B Nielsen

Chalmers, Kemi- och bioteknik, Livsvetenskaper

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 8 5 e1002518

Styrkeområden

Informations- och kommunikationsteknik

Livsvetenskaper och teknik (2010-2018)

Ämneskategorier

Kemi

DOI

10.1371/journal.pcbi.1002518

Mer information

Senast uppdaterat

2022-04-05