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

By Rasmus Agren, Sergio Bordel, Adil Mardinoglu, Natapol Pornputtapong, Intawat Nookaew, Jens Nielsen. PLoS Comput. Biol., 2012

450 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bd901c8dc54506a29b8b86c63daf907a9b6b7153

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Citations in the corpus

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, background): “Metabolic Atlas (40) for microorganisms and human cell types, respectively.”
RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor (2018, background): “0 as built-in resource for human metabolic modeling [18,33].”
Using Genome-Scale Models to Predict Biological Capabilities (2015, unknown): “This model summarizes the experimental data in a self-consistent and integrated format and forms the starting point for further computational and biological inquiry (Agren et al., 2012; Shlomi et al., 2008) (see Figures 5D and 5E).”
The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, methodology): “The software also implements the INIT algorithm, which is a powerful approach for reconstruction of tissue-specific models [59].”
Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism (2014, methodology): “The Integrative Network Inference for Tissues algorithm (INIT) is a method for building tissue-specific models from genome-scale reconstructions [28].”
Predicting Network Activity from High Throughput Metabolomics (2013, background): “Intense manual curation, however, was required in the building of two high-quality human models [15,16], which were followed by a number of derivatives [17–20].”