Hidden states and currents in biological systems
This project aims to revolutionize the understanding of hidden dynamics in various systems by developing new statistical methods for analyzing time series data, enhancing insights in biophysics and beyond.
Projectdetails
Introduction
The ability to infer information about hidden degrees of freedom from time series would revolutionize experiments on single molecules, mesoscale assemblies, and tissues, as well as financial and climate systems. Hidden dynamics are often essential, as they reflect the approaching of a critical transition or describe its mechanism, e.g., the folding of a protein or RNA, or an abrupt shift in climate.
Project Goals
With the project proposed here, I plan to push our quantitative understanding of experiments, ranging from single-molecule spectroscopy to observations of migrating cells and developing tissues, to a new level. This will be achieved by exploiting how the properties of a high-dimensional landscape and current imprint onto the time ordering of projected states along individual trajectories.
Methodology
I will introduce functionals of projected paths that are easily inferred from data and analyze their statistics and measure concentration by combining:
- The theory of functionals of stochastic paths
- Concentration inequalities
- Semiclassical analysis
These methods will be applied to:
- Single-molecule force spectroscopy
- Plasmon ruler experiments
- Cell tracking
- Molecular Dynamics simulations
Distinctive Characteristics
A distinctive characteristic of the project is the focus on non-asymptotic measure concentration, i.e., on “occurs with high probability” results that will be addressed for the first time in the context of non-equilibrium physics.
Expected Outcomes
Providing a new framework for interpreting experiments—using the information readily encoded in the data but inaccessible to existing approaches—the project will generate new knowledge that will:
- Resolve the long-standing debate about intermediates in DNA, RNA, and protein folding
- Address controversies about non-converging dynamics of folded proteins
- Shed new light on the operation of nanomachines and self-assembly far from equilibrium
- Illuminate cell movements during tissue regeneration
Impact
This project will lead to a paradigm shift in soft matter and biophysics and may reshape actuarial and climate science.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.000.000 |
Totale projectbegroting | € 2.000.000 |
Tijdlijn
Startdatum | 1-5-2023 |
Einddatum | 30-4-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EVpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
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Super-resolved stochastic inference: learning the dynamics of soft biological matterDevelop algorithms for robust inference of stochastic models from experimental data to advance data-driven biophysics and tackle key biological problems. | ERC Starting... | € 1.477.856 | 2023 | Details |
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The Spectrum of Fluctuations in Living MatterThis project aims to develop a theoretical framework for predicting active fluctuations in living matter by analyzing subcellular and tissue-scale dynamics, enhancing our understanding of biological processes. | ERC Starting... | € 1.499.575 | 2024 | Details |
Nanoprobes for Nonequilibrium Driven SystemsThis project aims to develop fluorescent nanosensors to quantify energy dissipation in nonequilibrium biological systems, enhancing understanding of molecular motors and thermodynamic constraints. | ERC Starting... | € 1.500.000 | 2022 | Details |
Hidden metastable mesoscopic states in quantum materialsThis project aims to develop tools for investigating mesoscopic metastable quantum states in non-equilibrium conditions using advanced time-resolved techniques and theoretical models. | ERC Advanced... | € 2.422.253 | 2024 | Details |
Super-resolved stochastic inference: learning the dynamics of soft biological matter
Develop algorithms for robust inference of stochastic models from experimental data to advance data-driven biophysics and tackle key biological problems.
A holistic approach to bridge the gap between microsecond computer simulations and millisecond biological events
This project aims to bridge μs computer simulations and ms biological processes by developing methods to analyze conformational transitions in V1Vo–ATPase, enhancing understanding of ATP-driven mechanisms.
The Spectrum of Fluctuations in Living Matter
This project aims to develop a theoretical framework for predicting active fluctuations in living matter by analyzing subcellular and tissue-scale dynamics, enhancing our understanding of biological processes.
Nanoprobes for Nonequilibrium Driven Systems
This project aims to develop fluorescent nanosensors to quantify energy dissipation in nonequilibrium biological systems, enhancing understanding of molecular motors and thermodynamic constraints.
Hidden metastable mesoscopic states in quantum materials
This project aims to develop tools for investigating mesoscopic metastable quantum states in non-equilibrium conditions using advanced time-resolved techniques and theoretical models.
Vergelijkbare projecten uit andere regelingen
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Computation driven development of novel vivo-like-DNA-nanotransducers for biomolecules structure identificationThis project aims to develop DNA-nanotransducers for real-time detection and analysis of conformational changes in biomolecules, enhancing understanding of molecular dynamics and aiding drug discovery. | EIC Pathfinder | € 3.000.418 | 2022 | Details |
Computation driven development of novel vivo-like-DNA-nanotransducers for biomolecules structure identification
This project aims to develop DNA-nanotransducers for real-time detection and analysis of conformational changes in biomolecules, enhancing understanding of molecular dynamics and aiding drug discovery.