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.

An integrated computational and experimental study uncovers FUT 9 as a metabolic driver of colorectal cancer

“…GSMMs can also be used to predict the phenotypic effects of genetic and environmental perturbations on the cell's flux distribution and viability. Such modeling studies have been employed in recent years to describe human metabolism (Duarte et al , 2007) in general and in cancer (Folger et al , 2011; Agren et al , 2012, 2014; Nam et al , 2014; Yizhak et al , 2014). …”

Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease

“…Several generic (non-cell type-specific) GEMs for human metabolism have been previously constructed [12][13][14][15][16] . However, neither of these generic networks contain extensive lipid metabolism, which is necessary to study the effect of lipids on the underlying molecular mechanism of NAFLD.…”

“…To provide a resource for automated and semi-automated reconstruction of cell-type-specific GEMs, we previously constructed the HMR database 15 . This comprehensive database, together with the INIT (Integrative Network Inference for Tissues) algorithm, have been employed for automated generation of cell-type-specific GEMs 15 . These models form the basis for the Human Metabolic Atlas (http://www.…”

Model-based identification of drug targets that revert disrupted metabolism and its application to ageing

“…First, although several CBM-based methods for inferring flux rates using gene expression data have been developed 9,10,14 , the correlation between these measurements is known to be limited 53 . Hence, gene expression levels serve only as cues for the likelihood that an enzyme supports the metabolic flux of its associated reaction.…”

“…4). GSMMs have been used for a variety of applications [5][6][7][8][9][10] including drug discovery 2 and metabolic engineering tasks 11 . Over the last 6 years, GSMM has been successfully used for modelling human metabolism as well, both in health and disease 3,[12][13][14][15][16] .…”