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.
Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity / Galuzzi, B. G.; Izzo, S.; Giampaolo, F.; Cuomo, S.; Vanoni, M. E.; Alberghina, L.; Damiani, C.; Piccialli, F.. - (2023), pp. 185-192. (Intervento presentato al convegno 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023 tenutosi a University of Naples "Parthenope", ita nel 2023) [10.1109/PDP59025.2023.00037].
Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity
Izzo S.;Giampaolo F.;Cuomo S.;Piccialli F.
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.