Different research fields employ very different data and methods for modelling, and in some cases there is little overlap between disciplines. However, biomolecular modelling and simulations are the basic building blocks with which we design molecules, determine structures, create drugs, and improve products. This makes them applicable as a complementary tool to many other research areas that investigate phenomena occurring on higher levels of abstraction – the field is both mature with several excellent HPC applications and concrete method needs from applications both in academia and industry.
It is already possible to simulate systems with millions of atoms such as viral capsids, fusing vesicles, drug delivery systems or other multi-molecular assemblies. With the upcoming Exascale compute capability systems this limit will be pushed even further up, making molecular modelling a sort of super-scale “glue” that will be able to tackle a much wider scale of bio- and non-bio systems.
Here are several fields in computational Life Science where BioExcel’s expertise is highly relevant.
Integrative structural biology
Proteins and their intricate network of interactions are the mainstay of any cellular process. Dissecting their interaction networks at atomic detail is therefore invaluable, and researchers have to rely increasingly computational techniques, in addition to experiments. BioExcel can provide the necessary expertise to guide experimentalists in using the best-suited approaches to their problems.
Biomarkers design
Biomarkers are traceable substances that indicate various physiological states, in particular related to disease conditions. They are often used in different stages of the drug development processes or as diagnostics. Since the biomarker-design process is similar to drug development, molecular simulation methods are an integral part of this process.
Nanotechnology and materials science
Biopolymers are often used for the development of materials with novel functions. Understanding the underlying structure and properties of these materials requires at least atomistic level modelling – an area where molecular simulations excel.
Personalized medicine
Personalized medicine adapts medical treatment methodologies to reflect the physiological and genetical specifics of the patients. It involves also pre-clinical studies and clinical trials in order to evaluate the efficacy of the drugs and potential side effects. One important component of personalized medicine is to identify mutations in genes responsible for disease, at which point it becomes a very interesting overlap to use molecular modelling and simulation methods to rationalise the effect of those mutations at a molecular level and design drugs that can restore or prevent function. For this, our centre will be an excellent complimentary partner to other centres and organizations who will focus on applications of drug design in clinical settings.
Physiology
Physiology studies the physical and chemical functions of organisms. It spans several levels from small bio-molecules through cells and tissues up to organs. The expertise offered by BioExcel will be complimentary to the former, lowest levels of organization, i.e. studies of biomolecular and cell function. It will provide solid foundation for atomic level understanding of molecular behaviour as basis for studies of the higher levels.
Neuroinformatics
Neuroinformatics is concerned with analysis of experimental data from neuroscience research, and development of computational methods for modelling of the nervous system. Biomolecular simulations are heavily used in studies of critical bio-chemical events at the basis of neuronal function, e.g. the function of various receptors of neurotransmitters. For example, there are ongoing efforts in coupling sub-synaptic molecular level simulations with modelling of neuron spiking events, and using simulations to predict how gene mutations will influence nerve signalling properties.
Multi-scale QM/MM modelling
Electronic structure research employs quantum mechanical (QM) modelling in order to understand chemical reactions. QM methods are the main computational research tool for investigating catalysis, photophysics and spectroscopic properties of molecules. Moreover, calculations based on this approach have been so far among the largest HPC applications in molecular biophysics. Hybrid methods combining QM and MM (molecular mechanics) are rapidly increasing in their applications, as emphasized by the recent Nobel Prize in Chemistry in 2013. The CoE specifically includes competence on hybrid QM/MM methods as applied to biological systems.
If your research is in a complimentary area and our expertise might be of use, please get in touch with us.
