Title : Computational models and biophysics for understanding neuronal dysfunctions in Alzheimer’s disease
Alzheimer’s disease (AD) as the leading cause of dementia affects millions of families across the world. AD causes huge physical social, psychological and financial impacts not only on the patients but also on their families and the whole society. Numerous research has been conducted worldwide on understanding the pathogenesis of AD and seeking a cure for AD.
Computational modelling based on biophysics has been successfully applied to neuroscience. It provides a strong foundation for applying the modelling approach to AD research. Computational modelling offers great opportunities on investigating the underlying mechanisms of the disease and its progression, from molecular/cellular level to system level. Using conceptual models, hypotheses on AD can be investigated individually or comprehensively through well-designed computational experiments. With the development of technology, a great number of simulation software or platforms, either for general or biological/neuronal-specific purposes, are wildly available, which largely advance the modelling research on AD.
In this presentation, we discuss how the computational modelling approach can help scientists understand the dysregulation in AD, with examples of context-driven biophysics and computational modelling approaches related to AD. We focus on modelling studies on the inter-and intra-neuronal signalling pathways that are involved in the degeneration of AD, particularly on the dysfunction of the signal transduction at the synapse as well as the axon.
We briefly discuss neuron and its environment as complex dynamic systems; initiation of action potential and its relevance to AD; role of calcium in AD, related pathways, and hypotheses; and biophysics of Aβ and computational modelling.
What will audience learn from your presentation?
- How computer models can be used to understand AD
- Develop ways of therapeutic interventions for AD
- New ideas for different research directions