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

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.

Availability and implementation: The source code of FUMOSO is available under the GPL 2.0 license on GitHub at the following URL: https://github.com/aresio/FUMOSO.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Interaction network of the model of cell death and survival. Yellow circles represent metabolites and ions, green rectangles represent proteins, red rectangles represent pathways or cellular processes, light blue hexagons represent the system phenotypes related to cell death. Positive and negative regulations are pictured as arrows and blunt-ended arrows, respectively. Glucose, Ras-GTP and PKA are the input variables; survival, autophagy, apoptosis and necrosis are the output variables, while the remaining are inner variables. (Color version of this figure is available at Bioinformatics online.)

Fig. 2.

Assessment of the effects of perturbations (UPR activation) predicted by the global optimization algorithm. (a, b) Simulation outcome of the three main model output (apoptosis, necrosis and survival) upon UPR activation, either in (a) PKA Low state or (b) PKA High state. The perturbation was applied from time t_b = 0 to the end of the simulation, and evaluated after $\Delta =0.13$ a.u. (shaded area, see also Supplementary Section S6). (c) MDA-MB-231 cells, grown in HG, were daily treated with 10 μM FSK mimicking the PKA High state, or 5 μM H89 mimicking the PKA Low state and, upon 24 h, also with 10 nM thap (single treatment). Samples were evaluated for cell death at 48 and 72 h post-treatment by using trypan blue exclusion method. The experimental scheme is shown in Supplementary Figure S9c. All data represent the average of at least three independent experiments (±SD); *P < 0.05 (Student’s t-test). (Color version of this figure is available at Bioinformatics online.)

Fig. 3

Assessment of the effects of perturbations (UPR activation and autophagy inhibition) predicted by global optimization. (a, b) Simulation outcome of the three main model output (apoptosis, necrosis and survival) upon UPR activation and autophagy inhibition, either in (a) PKA Low state or (b) PKA High state. The perturbation was applied from time t_b = 0 to the end of the simulation, and evaluated after $\Delta =0.13$ a.u. (shaded area, see also Supplementary Section S6). (c) MDA-MB-231 cells, grown in HG, were daily treated with 10 μM FSK mimicking the PKA High state and, upon 24 h, also with 10 nM thap and 20 μM chloroquine (CQ) (single treatment of both). Samples were evaluated for cell death at 48 and 72 h post-treatment by using trypan blue exclusion method. The experimental scheme is shown in Supplementary Figure 9d. (d, e) Simulation outcome of the three main model output (apoptosis, necrosis and survival) upon HBP and N-glycosylation inhibition, either in (d) PKA Low state or (e) PKA High state. (f) MDA-MB-231 cells, grown in HG, were daily treated with 10 μM FSK mimicking the PKA High state and, upon 24 h, also with 1 μM aza and 50 ng/ml tuni (single treatment of both). Samples were evaluated for cell death at 48 and 72 h post-treatment by using trypan blue exclusion method. The experimental scheme is shown in Supplementary Figure 9d. (g, h) Simulation outcome of the three main model output (apoptosis, necrosis and survival) upon N-glycosylation and autophagy inhibition, either in (g) PKA Low state or (h) PKA High state. (i) MDA-MB-231 cells, grown in HG, were daily treated with 10 μM FSK mimicking the PKA High state and, upon 24 h, also with 50 nM tuni and 10 μM CQ (single treatment of both). Samples were evaluated for cell death at 48 and 72 h post-treatment by using trypan blue exclusion method. The experimental scheme is shown in Supplementary Figure 9d. All data represent the average of at least three independent experiments (±SD); *P < 0.05, **P < 0.01 (Student’s t-test). (Color version of this figure is available at Bioinformatics online.)