Dopamine is an important neurotransmitter responsible for regulating various brain functions such as learning and cognition. Dysfunctions within the dopaminergic system are implicated in many neurological and neuropsychiatric disorders. To understand such a complex system, biologically realistic multiscale computational models are necessary. Such models require the extraction of relevant and important factors or processes from one scale to bridge and interact with systems at other scales. In this paper, we analyze an influential computational model of dopamine synthesis and release within a pre-synaptic terminal by systematically perturbing its variables/substrates. Based on the relative changes in steady states and the time to reach the new perturbed steady states, we found that the substrates within the cascade of intracellular biochemical reactions can vary widely in terms of influence and timescale. We then categorize the substrates according to their relative timescales and changes in steady states. The perturbation results are then used to guide our selection for the most appropriate equations and functions to be approximated in developing reduced models of the original model. Our preliminary simulation results show that either a slow or fast version of the reduced model can be simulated significantly faster than the original model. Our work demonstrates, through perturbation analysis, the feasibility of reduced models of the dopaminergic presynaptic terminal to improve computational efficiency, implement in multiscale modelling, and in silico neuropharmacology.
|Title of host publication||Unknown Host Publication|
|Number of pages||7|
|Publication status||Published (in print/issue) - 2014|
|Event||IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Empowering Systems Medicine Through Optimal Computational Modelling Workshop - Belfast, Northern Ireland, UK|
Duration: 1 Jan 2014 → …
|Workshop||IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Empowering Systems Medicine Through Optimal Computational Modelling Workshop|
|Period||1/01/14 → …|