One of the research activities of IAS-5/INM-9 at Forschungszentrum Jülich focuses on the mesoscale modeling of the complex network of molecular interactions responsible for the transmission of information through neuronal cells. This network of interactions regulates very complex tasks such as memory, learning, motivation or motion, and involves many different partner molecules (e.g. proteins and neurotransmitters), located in different cellular compartments, which have to diffuse, meet and interact at the correct time in the correct place. In our institute we develop stochastic models borrowed from statistical physics as well as computational tools to describe diffusion, encounter, and partners recognition processes occurring at subcellular level on time and special scales of the order of millisecond and micrometer. The phenomenological parameters used as input to such mesoscopic models are derived in a mean field-type approach by higher resolution atomistic simulations. The aim is to understand how physico-chemical features such as cellular membrane composition, electric fields, long-range electrodynamic interactions, shape signaling transduction and induce specific cellular responses to external stimuli.
Scope of the Master Thesis:
Within this context the master student will tackle the problem of the existence and possible role of long-rage electrodynamic interactions between partner proteins. Dealing with cellular signaling indeed raises the fundamental question on which interactions efficiently and selectively guide molecular recognition between interacting particles. These can possibly involve attractive and selective interactions of (quasi-) electrostatic nature (chemical-, and hydrogen-bonds, Coulomb, Van-der-Waals London (hydrophobic) forces, etc.) besides the traditional Brownian motion (random diffusion) due to thermal fluctuations. However, these interactions, even if relevant for stereo-specific and "key-lock" interactions at short distances, are limited by Debye screening and by strong water absorption. They do not expand above few tenths of Angstrom, whilst the spatial dimensions of biomolecular systems are much larger than these short-range interactions and the association rates are faster than those expected by thermal diffusion alone. Using MD simulations, the student will verify whether, under stationary out-of-equilibrium conditions, vibrations of the dipole moments of the macromolecules (proteins) in the THz frequency range (due to collective molecular oscillations) can activate electrodynamic forces. If proved, these electrodynamic forces wouldn't suffer from Debye screening. Water dielectric constant drops down at these frequencies. These electrodynamic forces would thus be effective over spatial scale of the order of hundreds/thousands of Angstrom thus possibly providing an efficient way to guide attractive and selective recruitment of the molecular partners.
- Very good Bachelor degree in Physics
- A good knowledge in statistical physics, numerical methods is recommended
- Interest in bio/soft matter and simulation
- Experience in programming languages and Linux-based clusters usage is a plus
- Fluent in English (spoken and written)
- Highly motivated scientists of different subject areas working together
- Interdisciplinary work applying statistical physics and computational methods to biological problems
- Intensive supervision by one or more experienced and helpful colleague(s)
- Friendly and welcoming work environment
Institute of Advanced Simulations & Institute of Neuroscience and Medicine (IAS-5/INM-9)
More information about this research activity: https://www.fz-juelich.de/SharedDocs/Personen/IAS/IAS-5/EN/Staff/Calandrini.html