Motivation: The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both the experimental and computational perspectives. Here we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the emergent dynamical behaviors of highly heterogeneous systems in unperturbed and perturbed conditions, regardless of the availability of quantitative parameters, and (2) determine a minimal set of system components whose perturbation can lead to a desired system response, therefore facilitating the design of a more appropriate experimental strategy. Results: We applied this method to investigate what drives K-ras-induced cancer cells, displaying the typical Warburg effect, to death or survival upon progressive glucose depletion. The optimization analysis allowed to identify new combinations of stimuli that maximize pro-apoptotic processes. Namely, our results provide different evidences of an important protective role for protein kinase A in cancer cells under several cellular stress conditions mimicking tumor behavior. The predictive power of this method could facilitate the assessment of the response of other complex heterogeneous systems to drugs or mutations in fields as medicine and pharmacology, therefore paving the way for the development of novel therapeutic treatments.

Nobile, M., Votta, G., Palorini, R., Spolaor, S., De Vitto, H., Cazzaniga, P., et al. (2020). Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells. BIOINFORMATICS, 36(7 (1 April 2020)), 2181-2188 [10.1093/bioinformatics/btz868].

Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells

Nobile, Marco S
Co-primo
;
Votta, Giuseppina
Co-primo
;
Palorini, Roberta
Co-primo
;
Spolaor, Simone;De Vitto, Humberto;Cazzaniga, Paolo;Ricciardiello, Francesca;Mauri, Giancarlo;Alberghina, Lilia;Chiaradonna, Ferdinando
Penultimo
;
Besozzi, Daniela
Ultimo
2020

Abstract

Motivation: The elucidation of dysfunctional cellular processes that can induce the onset of a disease is a challenging issue from both the experimental and computational perspectives. Here we introduce a novel computational method based on the coupling between fuzzy logic modeling and a global optimization algorithm, whose aims are to (1) predict the emergent dynamical behaviors of highly heterogeneous systems in unperturbed and perturbed conditions, regardless of the availability of quantitative parameters, and (2) determine a minimal set of system components whose perturbation can lead to a desired system response, therefore facilitating the design of a more appropriate experimental strategy. Results: We applied this method to investigate what drives K-ras-induced cancer cells, displaying the typical Warburg effect, to death or survival upon progressive glucose depletion. The optimization analysis allowed to identify new combinations of stimuli that maximize pro-apoptotic processes. Namely, our results provide different evidences of an important protective role for protein kinase A in cancer cells under several cellular stress conditions mimicking tumor behavior. The predictive power of this method could facilitate the assessment of the response of other complex heterogeneous systems to drugs or mutations in fields as medicine and pharmacology, therefore paving the way for the development of novel therapeutic treatments.
Articolo in rivista - Articolo scientifico
fuzzy logic modeling; K-ras; cancer cells; apoptosis; cell death
English
21-nov-2019
2020
36
7 (1 April 2020)
2181
2188
open
Nobile, M., Votta, G., Palorini, R., Spolaor, S., De Vitto, H., Cazzaniga, P., et al. (2020). Fuzzy modeling and global optimization to predict novel therapeutic targets in cancer cells. BIOINFORMATICS, 36(7 (1 April 2020)), 2181-2188 [10.1093/bioinformatics/btz868].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/255148
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