ALBERGHINA LILIA
- U03, Floor: 5, Room: 5008
Publications
Lin, L., Lapi, F., Galuzzi, G., Vanoni, M., Alberghina, L., Damiani, C. (2026). Constraint-based informed Machine Learning links non-canonical TCA cycle activity to Warburg metabolism and hallmarks of malignancy. Intervento presentato a: 10th Conference on Constraint-Based Reconstruction and Analysis (COBRA2026) - March 17 to 19, 2026, Potsdam, Germania. Detail
Vanoni, M., Palumbo, P., Papa, F., Busti, S., Gotti, L., Wortel, M., et al. (2025). A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations. PLOS COMPUTATIONAL BIOLOGY, 21(7) [10.1371/journal.pcbi.1013296]. Detail
Lin, L., Lapi, F., Galuzzi, B., Vanoni, M., Alberghina, L., Damiani, C. (2025). Mechanistically informed machine learning links non-canonical TCA cycle activity to Warburg metabolism and hallmarks of malignancy. PLOS COMPUTATIONAL BIOLOGY, 21(12), 1-29 [10.1371/journal.pcbi.1013384]. Detail
Goglia, I., Węglarz‐tomczak, E., Gioia, C., Liu, Y., Virtuoso, A., Bonanomi, M., et al. (2024). Fusion–fission–mitophagy cycling and metabolic reprogramming coordinate nerve growth factor (NGF)‐dependent neuronal differentiation. THE FEBS JOURNAL, 291(13 (July 2024)), 2811-2835 [10.1111/febs.17083]. Detail
Vanoni, M., Palumbo, P., Busti, S., Alberghina, L. (2024). A critical review of multiscale modeling for predictive understanding of cancer cell metabolism. CURRENT OPINION IN SYSTEMS BIOLOGY, 39(December 2024) [10.1016/j.coisb.2024.100531]. Detail