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Dynamic Modelling of Pathways to Cellular Senescence Reveals Strategies for Targeted Interventions

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A dynamic model for cellular senescence.

(A) Graphical model integrating the insulin-TOR (IIS-TOR) signalling pathway (left), the DDR-oxidative stress responses (top-right) and mitochondria (centre). The insulin-TOR signalling pathway regulated the anabolic process of mitochondrial biogenesis, FoxO3a translocation to the cytoplasm followed by ubiquitination, and inhibition of cell cycle arrest (black reactions). AMPK signalling promoted the catabolic pathway of mitophagy, but was also modelled to promote mitochondrial biogenesis (magenta reactions). Mitochondrial membrane potential (ψm) increased cellular energy levels (red reactions) deregulating AMPK, and enhanced intracellular ROS (blue reactions). DNA damage and ROS triggered a stress response and consolidated cell cycle arrest. (B) Representative western blot data used for calibrating the model. (C) Representative imaging data used for measuring co-localisation between mitochondria (COXIV) and lysosomes (LC3) at day 0 and 12. These data were used for creating a time course for mitophagy activation as indicated in panel E.(D) Additional controls for mitophagy data using Pink 1 and mitochondrial biogenesis using PGC-1α indicating correlation between mTOR-pS2448 and PGC-1α. (E) In silico versus in vitro time courses. The model (red lines) was calibrated over experimental time course data (blue points) collected for 14 readouts in the network up to 21 days. The inputs are amino acids/insulin (constant inputs) and irradiation (pulse input of 5 min which simulates 20 Gy X ray irradiation over 5 min). Experimental time points (blue points) are mean +/−1 standard deviation collected from 5 independent repetitions. N = number of data points used for fitting each readout time course. The goodness-of-fit statistical measures χ2 [61] and AIC [68] for this model are 70.4278 and 381.838, respectively. (F) Simulated time-courses of the internal states for mitochondrial mass, ψm and turnover.

Figure 1

doi: https://doi.org/10.1371/journal.pcbi.1003728.g001