Abstract
The mathematical modeling of synaptically connected neuronal networks is an established instrument for gaining insights into dynamics of neuronal ensembles and information processing in the nervous system. Recently, calcium signaling in astrocytes—glial cells controlling local tissue metabolism and synapse homeostasis—and its corresponding downstream effect on synaptic plasticity and neuromodulation appeared in the limelight of modeling studies. Here, we used mechanism-based mathematical modeling to disentangle signaling pathways and feedback loops in the astrocytic calcium response to noradrenaline, an important neuromodulator marking periods of heightened alertness and arousal. The proposed model is based on an experiment-based 2D representation of astrocyte morphology, discrete random glutamate synapses with placement probability defined by the morphology pattern, and spatially heterogeneous noradrenaline sources, reflecting axonal varicosities of the adrenergic axons. Both glutamate and noradrenaline drive Ca2+ dynamics in the astrocyte in an additive or synergistic manner. Our simulations replicate the global activation of astrocytes by noradrenaline and predict the generation of high-frequency Ca2+ waves in a dose-dependent manner and the preferred Ca2+ wave origination near noradrenaline release sites if they colocalise with high-density clusters of glutamate synapses. We tested positive feedback loops between noradrenaline release and glutamate spillover directly or mediated by gliotransmitter release from the activated astrocyte. The simulations suggest that the coupled stochastic drive by glutamate and noradrenaline release converges on the graded modulation of the IP3 level, which is translated into whole-cell Ca2+ waves of different frequencies. Thus, the proposed approach is supported by experimental data and can be used to address situations inaccessible directly by experiment, and is a starting point for a more detailed model that includes other signaling mechanisms providing negative feedback.
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