Cancer is one of the most common and diverse diseases in todays world. Due to its tendency for rapid change and growth, the effectiveness of treatment is often limited. One of its key characteristics is high heterogeneity, both within the tumor tissue itself and among patients with the same type of cancer. To investigate tumor heterogeneity, we generated context-specific genome-scale metabolic models (GEMs) using publicly available scRNA-seq data and the ftINIT algorithm (fast task-driven integrative network inference for tissues) which offered us greater insight into the metabolism of cancer tissue cells. Our aim was to analyze the tissue and identify potential therapeutic targets. This was achieved through various methods that enabled the observation of variable metabolic tasks, the identification of structural differences between the constructed models, the determination if subsystems are expressed or not expressed in specific cancer cell types, and, ultimately, the identification of potential target genes for treatment. The comprehensive analysis provided a better understanding of tumor tissue and demonstrated the utility of systems biology approaches in understanding the metabolic basis of certain diseases, serving as a foundation for experimental procedures.
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