A hybrid mathematical modeling approach of the metabolic fate of a fluorescent sphingolipid analogue to predict cancer chemosensitivity

Abstract

Sphingolipid (SL) metabolism is a complex biological system that produces and transforms ceramides and other molecules able to modulate other cellular processes, including survival or death pathways key to cell fate decisions. This signaling pathway integrates several types of stress signals, including chemotherapy, into changes in the activity of its metabolic enzymes, altering thereby the cellular composition of bioactive SLs. Therefore, the SL pathway is a promising sensor of chemosensitivity in cancer and a target hub to overcome resistance. However, there is still a gap in our understanding of how chemotherapeutic drugs can disturb the SL pathway in order to control cellular fate. We propose to bridge this gap by a systems biology approach to integrate i) a dynamic model of SL analogue (BODIPY-FL fluorescent-sphingomyelin analogue, SM-BOD) metabolism, ii) a Gaussian mixture model (GMM) of the fluorescence features to identify how the SL pathway senses the effect of chemotherapy and iii) a fuzzy logic model (FLM) to associate SL composition with cell viability by semi-quantitative rules. Altogether, this hybrid model approach was able to predict the cell viability of double experimental perturbations with chemotherapy, indicating that the SL pathway is a promising sensor to design strategies to overcome drug resistance in cancer.