Summary
Can systems with life-like properties be built from scratch with only a minimal set of components? While progress has been made experimentally in the development of such minimal systems, there is a lack of theoretical underpinnings that could provide mechanistic principles. My goal is to discover such principles by combining theoretical modeling and in-silico evolution to explore the potential for life-like functions of minimal systems consisting of only two core elements of cells: reaction compartments enclosed by lipid membranes (liposomes) and equipped with a protein reaction network.
Towards this goal, we will develop new multi-scale approaches to investigate the mechanistic interplay between the ability of protein networks to form spatiotemporal patterns by decoding information about the membrane geometry and to reshape the membrane through mechano-chemical feedback. Using methods from differential geometry we will develop projection techniques that reduce the model to the two-dimensional manifold of the membrane. Building on my expertise with protein pattern formation I will design coarse-graining methods using machine learning concepts to link scales. These theories will give unprecedented insights into the relative role of reaction networks, membrane elasticity, and mechanochemical feedback in forming different types of protein patterns and membrane morphologies.
Moreover, they will provide an efficient computational platform, which I will use to in-silico explore the potential of supported lipid bilayers with adhering liposomes as a platform to generate functions such as cell migration, cell division, and collective cell-cell communication. This will lead to theoretical insights into the mechanistic principles of the emergent behavior of these systems, make specific predictions for established bottom-up experimental model systems, and provide innovative suggestions for the rational design of systems with targeted functionalities.
Towards this goal, we will develop new multi-scale approaches to investigate the mechanistic interplay between the ability of protein networks to form spatiotemporal patterns by decoding information about the membrane geometry and to reshape the membrane through mechano-chemical feedback. Using methods from differential geometry we will develop projection techniques that reduce the model to the two-dimensional manifold of the membrane. Building on my expertise with protein pattern formation I will design coarse-graining methods using machine learning concepts to link scales. These theories will give unprecedented insights into the relative role of reaction networks, membrane elasticity, and mechanochemical feedback in forming different types of protein patterns and membrane morphologies.
Moreover, they will provide an efficient computational platform, which I will use to in-silico explore the potential of supported lipid bilayers with adhering liposomes as a platform to generate functions such as cell migration, cell division, and collective cell-cell communication. This will lead to theoretical insights into the mechanistic principles of the emergent behavior of these systems, make specific predictions for established bottom-up experimental model systems, and provide innovative suggestions for the rational design of systems with targeted functionalities.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101097810 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 2 498 813,00 Euro - 2 498 813,00 Euro |
Cordis data
Original description
Can systems with life-like properties be built from scratch with only a minimal set of components? While progress has been made experimentally in the development of such minimal systems, there is a lack of theoretical underpinnings that could provide mechanistic principles. My goal is to discover such principles by combining theoretical modeling and in-silico evolution to explore the potential for life-like functions of minimal systems consisting of only two core elements of cells: reaction compartments enclosed by lipid membranes (liposomes) and equipped with a protein reaction network.Towards this goal, we will develop new multi-scale approaches to investigate the mechanistic interplay between the ability of protein networks to form spatiotemporal patterns by decoding information about the membrane geometry and to reshape the membrane through mechano-chemical feedback. Using methods from differential geometry we will develop projection techniques that reduce the model to the two-dimensional manifold of the membrane. Building on my expertise with protein pattern formation I will design coarse-graining methods using machine learning concepts to link scales. These theories will give unprecedented insights into the relative role of reaction networks, membrane elasticity, and mechanochemical feedback in forming different types of protein patterns and membrane morphologies.
Moreover, they will provide an efficient computational platform, which I will use to in-silico explore the potential of supported lipid bilayers with adhering liposomes as a platform to generate functions such as cell migration, cell division, and collective cell-cell communication. This will lead to theoretical insights into the mechanistic principles of the emergent behavior of these systems, make specific predictions for established bottom-up experimental model systems, and provide innovative suggestions for the rational design of systems with targeted functionalities.
Status
SIGNEDCall topic
ERC-2022-ADGUpdate Date
31-07-2023
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