Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT
Journal article, 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.

Author

Rasmus Ågren

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Sergio Velasco

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Adil Mardinoglu

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Natapol Pornputtapong

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Intawat Nookaew

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences, System Biology

PLoS Computational Biology

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

5 e1002518

Areas of Advance

Information and Communication Technology

Life Science Engineering (2010-2018)

Subject Categories

Chemical Sciences

DOI

10.1371/journal.pcbi.1002518

More information

Created

10/7/2017