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.
slide + paper
clustering, constraint-based modeling, flux sampling, transcriptomics, cancer cell line
English
31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2023 - 01-03 March 2023
2023
2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
9798350337631
2023
185
192
reserved
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/444398
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