SP 8

Mathematical Modelling III: Global Neurotransmitter Dynamics and Target Predictions

In silico pharmacology designates highly efficient and robust computational approaches in drug design and discovery as well as optimization of therapeutic solutions. In this project we aim to utilize state-of-the-art multi-scale in silico pharmacological methods in order to determine the chronic effects of ethanol on the neurochemical systems involved in mediating addictive behavior and the identification of effective and individualized pharmacological treatment strategies. These two goals will be pursued in parallel. In a series of previous studies, we have already demonstrated the appropriateness of our mathematical model comprised by a system of non-linear delay differential equations describing the alterations of extracellular neurotransmitter concentrations due to pharmacological manipulations, to address the acute effects of ethanol. In the first part, we will extend this model to capture the changes of the brain’s neurochemistry caused by chronic exposure to ethanol by different drinking patterns. The different drinking patterns have been discovered in our animal model of alcohol addiction and we have large data sets of alcohol drinking with high time resolution; i.e. in 1 min bins over several months of drinking from addicted and non-addicted rats. For each drinking pattern, the simulations will provide quantitative measures of neurochemical adaptations within the alcohol-addicted brain. In the second part of the study we aim to identify the necessary and sufficient conditions, mechanisms and strategies to reverse the characterized ethanol-induced neurochemical alterations for each drinking pattern. Subsequently, we will apply non-linear parameter estimation techniques (using adaptive Gauss-Newton method) to validate the outcomes with in vivo microdialysis measurements in rats. In conclusion, this SP will lead to in silico driven new mechanisms on the neurochemical and neuroanatomical level that are involved in mediating compulsive drinking and relapse behavior and will allow us to make prediction on novel treatment targets.