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ScaleCell SIGNED

Scalable Kinetic Models: From Molecular Dynamics to Cellular Signaling

Total Cost €


EC-Contrib. €






 ScaleCell project word cloud

Explore the words cloud of the ScaleCell project. It provides you a very rough idea of what is the project "ScaleCell" about.

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Project "ScaleCell" data sheet

The following table provides information about the project.


Organization address
city: BERLIN
postcode: 14195

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Germany [DE]
 Project website
 Total cost 1˙987˙500 €
 EC max contribution 1˙987˙500 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-COG
 Funding Scheme ERC-COG
 Starting year 2018
 Duration (year-month-day) from 2018-05-01   to  2023-04-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    FREIE UNIVERSITAET BERLIN DE (BERLIN) coordinator 1˙987˙500.00


 Project objective

Biological processes are inherently multi-scalar: exchanging a single amino acid of a protein can affect the macroscopic behavior of a cell. A computational model that can cover these scales and simulate the time evolution of locations, interactions, and atomistic structures of biomolecules in a cell would be transformative for the understanding of biology and the optimization of biotechnological processes. This ERC project will lay the methodological groundwork for such a model. Recent breakthroughs in the long-standing problem of sampling rare transition events in molecular dynamics (MD) simulation have enabled us to simulate biomolecular processes such as folding and binding with atomistic models. The PI has co-pioneered the widely-used Markov State Models (MSMs) that combine extensive distributed MD simulations towards models of the molecular kinetics. Using these methods, we have demonstrated that protein-protein association can be simulated and timescales of seconds can be reached in all-atom models of small protein systems. However, these methods have fundamental limitations to scale to the large biomolecules and the long length-scales involved in cellular signaling. To address these limitations, we will develop the following key technologies and disseminate them in open software:

1. A model that describes protein kinetics as a network of local switches which will overcome scaling limitations of MSMs that suffer from an exponential increase of parameters for large systems. 2. An “effective force field for cells” that predicts structure and kinetics of multi-body protein interactions based on simulations of relatively few protein interactions. 3. A multi-scale method to embed atomistic kinetic models in whole-cell reaction-diffusion simulations.

We will employ these methods and, in collaboration with leading experimentalists, investigate how the mechanochemical protein dynamin couples atomic-detail structure changes to membrane constriction in endocytosis.


year authors and title journal last update
List of publications.
2018 Christoph Fröhner, Frank Noé
Reversible Interacting-Particle Reaction Dynamics
published pages: 11240-11250, ISSN: 1520-6106, DOI: 10.1021/acs.jpcb.8b06981
The Journal of Physical Chemistry B 122/49 2020-01-29
2019 Manuel Dibak, Christoph Fröhner, Frank Noé, Felix Höfling
Diffusion-influenced reaction rates in the presence of pair interactions
published pages: 164105, ISSN: 0021-9606, DOI: 10.1063/1.5124728
The Journal of Chemical Physics 151/16 2020-01-29
2019 Jeffrey K. Noel, Frank Noé, Oliver Daumke, Alexander S. Mikhailov
Polymer-like Model to Study the Dynamics of Dynamin Filaments on Deformable Membrane Tubes
published pages: 1870-1891, ISSN: 0006-3495, DOI: 10.1016/j.bpj.2019.09.042
Biophysical Journal 117/10 2020-01-29
2019 Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé
Kernel methods for detecting coherent structures in dynamical data
published pages: 123112, ISSN: 1054-1500, DOI: 10.1063/1.5100267
Chaos: An Interdisciplinary Journal of Nonlinear Science 29/12 2020-01-29
2019 Frank Noé, Edina Rosta
Markov Models of Molecular Kinetics
published pages: 190401, ISSN: 0021-9606, DOI: 10.1063/1.5134029
The Journal of Chemical Physics 151/19 2020-01-29
2019 Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noé, Cecilia Clementi
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
published pages: , ISSN: 2374-7943, DOI: 10.1021/acscentsci.8b00913
ACS Central Science 2020-01-29
2019 Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
published pages: eaaw1147, ISSN: 0036-8075, DOI: 10.1126/science.aaw1147
Science 365/6457 2020-01-29
2019 Brooke E. Husic, Frank Noé
Deflation reveals dynamical structure in nondominant reaction coordinates
published pages: 54103, ISSN: 0021-9606, DOI: 10.1063/1.5099194
The Journal of Chemical Physics 151/5 2020-01-29
2018 Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
Deep Generative Markov State Models
published pages: , ISSN: , DOI:
Proceedings of Neural Information Processing Systems (NeurIPS) 32nd Conference on Neural Infor 2020-01-29
2019 Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé
Variational selection of features for molecular kinetics
published pages: 194108, ISSN: 0021-9606, DOI: 10.1063/1.5083040
The Journal of Chemical Physics 150/19 2020-01-29
2019 Fabian Paul, Hao Wu, Maximilian Vossel, Bert L. de Groot, Frank Noé
Identification of kinetic order parameters for non-equilibrium dynamics
published pages: 164120, ISSN: 0021-9606, DOI: 10.1063/1.5083627
The Journal of Chemical Physics 150/16 2020-01-29
2018 R. Schulz, Y. von Hansen, J. O. Daldrop, J. Kappler, F. Noé, R. R. Netz
Collective hydrogen-bond rearrangement dynamics in liquid water
published pages: 244504, ISSN: 0021-9606, DOI: 10.1063/1.5054267
The Journal of Chemical Physics 149/24 2020-01-29
2019 Moritz Hoffmann, Christoph Fröhner, Frank Noé
Reactive SINDy: Discovering governing reactions from concentration data
published pages: 25101, ISSN: 0021-9606, DOI: 10.1063/1.5066099
The Journal of Chemical Physics 150/2 2020-01-29
2019 Robin Winter, Floriane Montanari, Andreas Steffen, Hans Briem, Frank Noé, Djork-Arné Clevert
Efficient multi-objective molecular optimization in a continuous latent space
published pages: 8016-8024, ISSN: 2041-6520, DOI: 10.1039/c9sc01928f
Chemical Science 10/34 2020-01-29
2019 Moritz Hoffmann, Christoph Fröhner, Frank Noé
ReaDDy 2: Fast and flexible software framework for interacting-particle reaction dynamics
published pages: e1006830, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1006830
PLOS Computational Biology 15/2 2020-01-29
2019 Simon Olsson, Frank Noé
Dynamic graphical models of molecular kinetics
published pages: 15001-15006, ISSN: 0027-8424, DOI: 10.1073/pnas.1901692116
Proceedings of the National Academy of Sciences 116/30 2020-01-29
2019 Martin Lehmann, Ilya Lukonin, Frank Noé, Jan Schmoranzer, Cecilia Clementi, Dinah Loerke, Volker Haucke
Nanoscale coupling of endocytic pit growth and stability
published pages: eaax5775, ISSN: 2375-2548, DOI: 10.1126/sciadv.aax5775
Science Advances 5/11 2020-01-29

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