Characterizing the heterogeneity of cancer metabolism requires the knowledge of metabolic fluxes in different tumor types. These fluxes cannot be directly determined, especially at a sub-cellular level. Still, they can be obtained numerically through constraint-based steady-state models after integrating other high-throughput -omics data, such as transcriptomics. In this work, we proposed to study cancer metabolism through data analysis and machine learning methodologies. To this aim, we considered transcriptomics profiles for a large set of cancer cells. Using a core metabolic network as a scaffold, we generated many feasible flux distributions for each cancer cell. Then, we used cluster analysis to analyze these data. This preliminary analysis revealed three well-separated clusters having different metabolic behaviors.
Galuzzi, B., Izzo, S., Giampaolo, F., Cuomo, S., Vanoni, M., Alberghina, L., et al. (2023). Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity. In 2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp.185-192). IEEE [10.1109/PDP59025.2023.00037].
Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity
Galuzzi, BG;Vanoni, ME;Alberghina, L;Damiani, C;
2023
Abstract
Characterizing the heterogeneity of cancer metabolism requires the knowledge of metabolic fluxes in different tumor types. These fluxes cannot be directly determined, especially at a sub-cellular level. Still, they can be obtained numerically through constraint-based steady-state models after integrating other high-throughput -omics data, such as transcriptomics. In this work, we proposed to study cancer metabolism through data analysis and machine learning methodologies. To this aim, we considered transcriptomics profiles for a large set of cancer cells. Using a core metabolic network as a scaffold, we generated many feasible flux distributions for each cancer cell. Then, we used cluster analysis to analyze these data. This preliminary analysis revealed three well-separated clusters having different metabolic behaviors.File | Dimensione | Formato | |
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