Genome-scale metabolic modelling (GEM) is an approach that integrates data from different high-throughput sequencing techniques with modelling to provide a comprehensive understanding of biological systems. It helps us understand complex diseases such as Hepatocellular carcinoma (HCC), the most common form of liver cancer affecting also the patients studied. For each patient, we reconstructed a pair of models, namely a tumour model and a non-tumour model - using different algorithms from GIMME and iMAT families. We used a variety of methods and approaches to analyse the differences between the models within each pair to determine which model extraction method (MEM) performed best with our data. Based on the conducted analyses, we have concluded that we cannot claim to have found a MEM for extracting context-specific models from GEMs that we could conclude is the most suitable for general use. The iMAT and tINIT algorithms performed best on the data studied.
|
