Systems modeling and uncertainty quantification of AMP-activated protein kinase signaling

Abstract

AMP-activated protein kinase (AMPK) plays a key role in restoring cellular metabolic homeostasis after energy stress. Importantly, AMPK acts as a hub of metabolic signaling, integrating multiple inputs and acting on numerous downstream targets to activate catabolic processes and inhibit anabolic ones. Despite the importance of AMPK signaling, unlike other well-studied pathways, such as MAPK/ERK or NF-{kappa}B, only a handful of mechanistic models of AMPK signaling have been developed. Epistemic uncertainty in the biochemical mechanism of AMPK activity, combined with the complexity of the AMPK pathway, makes model development particularly challenging. Here, we leveraged uncertainty quantification methods and recently developed AMPK biosensors to construct a new, data-informed model of AMPK signaling. Specifically, we applied Bayesian parameter estimation and model selection to ensure that model predictions and assumptions are well-constrained to measurements of AMPK activity using recently developed AMPK biosensors. As an application of the new model, we predicted AMPK activity in response to exercise-like stimuli. We found that AMPK acts as a time- and exercise-dependent integrator of its input. Our results highlight how uncertainty quantification can facilitate model development and address epistemic uncertainty in a complex signaling pathway, such as AMPK. This work shows the potential for future applications of uncertainty quantification in systems biology to drive new biological insights by incorporating state-of-the-art experimental data at all stages of model development.