Scientific reasoning on dementia in old age was dominated by the Amyloid Cascade hypothesis for Alzheimer’s disease over the last decades. However, over the last 20 years more than 200 therapeutic trials based on this hypothesis, blocking beta-amaloid at several points in this linear pathway, failed to show a positive therapeutic effect. On the other side, improvement of multiple lifestyle conditions (cardiovascular risk modification, improved education, healthier nutrition, more exercise, better working conditions) convincingly resulted in lowering of the dementia incidence with 20-45% in five large studies across Western countries (USA, UK, Spain, Sweden, Netherlands) over the last 30 years. These very surprising results in an era with rapidly increasing genetic, metabolomic, and proteomic knowledge on dementia, but without a sight of a cure or solution underlines the high complexity of dementia in old age.
To deliver new insights in dementia we will start a PhD project on using multiscale computational modeling methodology to improve our understanding of the highly heterogeneous dementia trajectories in older subjects, taking into account data on cellular processes, but also multimorbidity, functional performances, environmental and social circumstances. This PhD project will importantly contribute to the evidence base of a new dementia paradigm based on the concept of complexity science applied to the mechanisms of (accelerated) brain aging.
Taking into account the complexity of the dementia pathophysiology the PhD candidate will start using data from literature, available databases and consensus meetings on the pathophysiology of dementia / Alzheimer in old age, and apply these with a multiscale systems dynamics modeling approach. We want to start from single scale models on the amyloid cascade, and connect these to linked single scale models on 2. Multimorbidity, 3. Social activity/Lifestyle, and 4. Environmental processes for which we use data on the phenotype and course of dementia in older adults. These models will be used for simulations in comparison to the real life data known from large follow-up studies to quantify the predictive power / uncertainty in the multiscale-models we developed, and refine these.
- Master’s degree computational sciences, or comparable, with biological or medical applications
- Passion for developing and testing mathematical multiscale models, and motivated to apply these as predictors of dementia trajectories in old age
- Affinity with, and if possible experience in clinical research
- Well-developed social skills and team-spirit: strong ability to connect with other research groups
- Skilled in writing academic English, best if evidenced by having published a scientific article(s)
The PhD candidate selected is ambitious and passionate to meet our scientific modeling ideal of dementia, and also connects with our global health improvement goals, enjoys working on the project deliverables, at the same time develops him/herself by acquiring new (research) skills, thereby growing to an excellent researcher with a generic perspective and innovative methodological competencies.
This PhD project is a unique chance for the brightest computational science students who really want to have impact on brain health and health care with their modeling skills. As dementia currently is the most threatening problem for older persons, and a therapy is still lacking, we look for groundbreaking new models to improve our understanding of dementia. When you are genuinely intrigued by the possibility to study human physiology with your computational science skills, and want to connect your multiscale modeling to the science of complex biological systems and their interaction with the environment, you have an important characteristic of our dreamed PhD student. Next, you should be excited on working on large datasets, and also carry out model validation in the multidisciplinary Radboudumc Alzheimer Centre Research group, who have chosen the ‘complexity theme’ as their connecting focus of interest.