Research

Phd research project: A radical approach to Alzheimer’s Disease via next-generation computational and experimental methods

The proposed PhD project takes on a radical new path towards the study of Alzheimer’s Disease (AD), which will open novel avenues for the development of breakthrough drugs with high-therapeutic efficacy against AD. This research path builds upon accumulating, compelling and independent lines of evidence, which demonstrate that a family of microorganism are associated to AD (https://www.nature.com/articles/d41586-020-03084-9). This has established the Infectious Hypothesis (IH) of AD. Indeed, we (and others) have identified DNA of a family of microorganisms in the brain of diseased AD patients. Moreover, a certain peptide released by neurons, so called Aβ, was previously thought to be the culprit of AD (so called Amyloid Cascade Hypothesis), however, we (and others) have established that in fact it is anti-microbial peptide which attempts to fight microorganisms! Thus, Aβ fights a tag of war with microorganisms and when it fails it leads to AD. Thus, the aim project is to determine (for the first time) via advanced mathematical and computational methods the molecular mechanisms by which Aβ inhibits infections, as well as, its weakness that leads to AD. This research direction provides a tantalizing hope for breakthrough preventive measures and treatments for AD. The importance of this research can be recognized by the fact that public funding bodies and private institutions are awarding prizes for whoever solves associated questions (e.g. https://idsafoundation.org/alz-research-grant/; https://alzgerm.org/).

The candidate will be hosted within the interdisciplinary research team Mathematical, Computational and Experimental Neuroscience (MCEN) at the Basque Center for Applied Mathematics (BCAM), where he/she will have access to diverse expertise and resources. Moreover, the project will take place in the context of an international consortium (Spain, Poland, France, Canada) involving Mathematicians, Physicists and Chemists, Molecular Biologists and Clinicians.

5. Job position description (max. 2.000 characters)

The candidate will be supervised by Prof. Rodrigues (BCAM) and by Dr. Rodrigo A. Moreira da Silva (Institute of Fundamental Technological Research – Polish Academy of Science).

The candidate is expected to have an MSc in either Mathematics, Physics, Computational Chemistry. It is desirable (but not expected) that the candidate is knowledgeable
in Computational Physics. The envisaged project methodologies will involve, all-atom molecular dynamics simulations, multiscale modeling, topological and geometrical data analysis and possibly Machine-learning and big-data analysis.

The candidate will first review AD literature, the infectious hypothesis literature, which include the consortium papers [1,2,3,4,5], as well as, recent computational methodologies developed by Dr. Moreira da Silva [6,7,8]. The candidate will be trained on the aforementioned mathematical and computational methods and will further develop these methodologies for the specific problem at hand. The candidate will also interact with the consortium (e.g. INRS Institute Armand Frappier, Canada; Inria/France) and in particular experimentalists who will provide experimental data.

BCAM is a world-class and a Severo Ochoa Excellence Centre in Applied Mathematics and has state-of-the-art logistics and computational facilities and scientific expertise. Moreover, international collaborations will leverage the candidates learning curve and ensure career progression.

[1] Bourgrade K et al. (2016). J Alzheimers Dis, 54, 859-878. (2016)
[2] Fülöp T et. al (2021). Alzheimer’s Disease. Neuropsychiatr Dis Treat.; 4;17:1311-1339.
[3] Munawara U et. al (2021). Alzheimer’s disease. Immun Ageing. 21;18(1):29.
[4] Fülöp T et. al. (2020). Mech Ageing Dev.;192:111390
[5] Fülöp T et. al (2020). CNS Drugs;34(7):673-695
[6] Edgar C et. Al. bioRxiv 871632; doi: https://doi.org/10.1101/871632
[7] Moreira, R. A et al. (2020). Materials, 13(23), 5362.
[8] Moreira, R. A et al. (2020). Nanoscale, 12(31), 16409-16413.