Self-Organisation: From Molecules to Matter
Description: This research proposal concerns the theory and computer simulation of self-organisation to predict properties and to design systems with specified characteristics. The key computational challenge is to explore the energy landscape for complex systems and make predictions to characterise efficient self-organisation on experimental time and length scales. Novel methodology is required to overcome the problems of broken ergodicity and rare events. The theoretical framework exploits stationary points of the potential energy landscape to access the required time and length scales. Applications include self-assembly of mesoscopic structures from coarse-grained building blocks and all-atom simulations of conformational changes in specific proteins and nucleic acids.We aim to establish design principles for efficient self-assembly by developing novel tools for visualising and exploration of the corresponding landscape. Here, a key issue is how the interactions between the constituent particles determine the organisation of the energy landscape. Identifying which features lead to successful self-assembly and which disrupt such ordering will lead to a wide range of important applications, ranging from design of new materials to identifying new anti-viral drugs. The same methodology will be applied to detailed models of specific biomolecules, where self-organisation into alternative structures is associated with disease. Global optimisation will be employed in structure prediction for variable pathogens, such as human influenza virus. Pathways for folding and misfolding of specific proteins and nucleic acids will be characterised using novel rare events methodology, providing insight into intermediates that could serve as potential drug targets.
Amount: €2,069,000
Funding type: research
Funder scheme: ERC-AG
Start date: 2011-03-01
End date: 2016-02-29
Institutions: University of Cambridge, THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Subfield: Molecular Biology
Domain: Life Sciences
Funded outputs count: 3
Funder award ID: 267369
OpenAlex ID: G4798317841