Based on data from life sciences and medicine, the Workshop introduces predictive mechanistic modelling using Python. We start with the concepts of quantitative changes over time and expand it to include changes in time and space. Our focus will be the understanding and prediction of unexpected, dramatic and catastrophic changes, like a new qualitative shape during differentiation, a fever after infection, having a seizure or migraine attack. We will look at mathematical formalism to describe biochemical reactions and transport; endocrine feedback loops; dynamic switching between physiological states and the origin of temporal oscillations. We will work with simple pre-written functions which we will study, modify, and simulate to create testable predictions.

We will work with invasive recordings from patients with epilepsy to understand the dynamics of normal and abnormal brain dynamics on the macroscopic scale. We will see how the combination of excitation and inhibition together with spatial communication can create spatio-temporal patterns of almost any complexity and how this approach can help to model various known observations of human brain activity. We will then use the approach to make testable predictions about pharmacological and neuro-modulatory perturbations of brain activity in the context of various neurological disorders. The focus will be on the modelling of epileptic seizures and the possibilities to dynamically stop or prevent them.