Prof. Dr. Frank Noé

Head of Computational Molecular Biology group

FU Berlin
Arnimallee 6
14195 Berlin
+49 (0) 30 838 75354
frank.noe@fu-berlin.de
Website


Research focus

Molecular Dynamics
Cellular dynamics
Reaction-diffusion simulation
Scientific computing
High-performance computing
Time series analysis
Machine learning

Projects as a project leader

  • A-AP7

    Efficient calculation of slow and stationary scales in molecular dynamics

    Prof. Dr. Frank Noé

    Project heads: Prof. Dr. Frank Noé
    Project members: -
    Duration: 01.10.2014 - 30.06.2018
    Status: running
    Located at: Freie Universität Berlin

    Description



  • A-AP10

    Multiscale modeling and simulation for spatiotemporal master equations

    Prof. Dr. Frank Noé / Prof. Dr. Christof Schütte

    Project heads: Prof. Dr. Frank Noé / Prof. Dr. Christof Schütte
    Project members: -
    Duration: 01.10.2014 - 30.06.2018
    Status: running
    Located at: Freie Universität Berlin

    Description

    Accurate modeling of reaction kinetics is important for understanding the functionality of biological cells and the design of chemical reactors. Depending on the particle con­centrations and on the relation between particle mobility and reaction rate constants, different mathematical models are appropriate. In the limit of slow diffusion and small concentrations, both discrete particle numbers and spatial inhomogeneities must be taken into account. The most detailed root model consists of particle-based reaction-diffusion dynamics (PBRD), where all individual par­ticles are explicitly resolved in time and space, and particle positions are propagated by some equation of motion, and reaction events may occur only when reactive species are adjacent.
    For rapid diffusion or large concentrations, the model may be coarse-grained in dif­ferent ways. Rapid diffusion leads to mixing and implies that spatial resolution is not needed below a certain lengthscale. This permits the system to be modeled via a spa­tiotemporal chemical Master equation (STCME), i.e. a coupled set of chemical Master equations acting on spatial subvolumes. The STCME becomes a chemical Master equa­tion (CME) when diffusion is so fast that the entire system is well-mixed. When particle concentrations are large, populations may be described by concentrations rather than by discrete numbers, leading to a PDE or ODE formulation.

    Many biological processes call for detailed models (PBRD, ST-CME or CME), but these models are extremely costly to solve. Ef.cient mathematical and computational methods are needed in order to approximate the solutions of these models with some guaranteed accuracy level. An approach to optimal or ef.cient switching between different models is, as yet, missing.
    In this project, we will set out to develop a multiscale theory for reaction kinetics processes, starting from a consistent and well-de.ned formulation of PBRD models, and including spatial scaling (PBRD <-> ST-CME <-> CME) coupled to population scaling (CME <-> ODE). In particular, we aim at providing solutions for the problematic cases of having particles at diverse copy numbers (CME . ODE) and at least some slowly diffusing particles (PBRD <-> CME <-> STCME). The cascades of scales in these scenarios and efficient approximation strategies will be explored.

    http://sfb1114.imp.fu-berlin.de/research/index.php?option=com_projectlog&view=project&id=12
  • A-AP12

    pcCells - physicochemical principles of cellular information processing

    Prof. Dr. Frank Noé

    Project heads: Prof. Dr. Frank Noé
    Project members: -
    Duration: 01.01.2013 - 31.12.2017
    Status: running
    Located at: Freie Universität Berlin

    Description

    Biological cells are able to perform complex signal transduction tasks quickly, energy-efficiently and yet in a robust and noise-tolerant manner. These signal transduction tasks rely on intracellular information processing mechanisms in which chemical signals are sent, transmitted and received and the state of the overall machinery is stored in chemical or conformational switches. The physicochemical principles of information processing in cells is still not well understood, owing to fundamental restrictions in resolution in experiments and in sampling of molecular dynamics simulations. Here, we will develop new simulation methods based on adaptive molecular dynamics and Markov models. These methods, together with new statistical mechanical theories and single-molecule experimental analyses will be employed to investigate the molecular basis of intracellular signal processing mechanisms. Central to our proposal is the hypothesis that intracellular signal processing relies on spatiotemporal order of molecules arising from dynamical sorting. This hypothesis will be tested using examples of protein-ligand and protein-protein sorting in neuronal signalling. The proposed project is highly multidisciplinary, involving physical chemistry, computer science, mathematics and biology.

    http://compmolbio.biocomputing-berlin.de/index.php/projects/80-erc
  • A-AP13

    Adaptive Konformationsdynamik mit Anwendung auf Rhodopsinaktivierung

    Prof. Dr. Frank Noé

    Project heads: Prof. Dr. Frank Noé
    Project members: -
    Duration: 01.07.2012 - 30.06.2015
    Status: completed
    Located at: Freie Universität Berlin

    Description

    Rare molecular events such as folding of proteins or nucleic acids, ligand binding, conformational changes or macromolecular aggregation are the basis of all life processes. Besides experimental techniques, molecular dynamics (MD) simulation is an established tool to analyze such processes. However, the usefulness of MD for investigating biological processes is limited by the sampling problem: Due to the high computational effort involved in simulating biomolecules at atomistic resolution, the accessible simulation times are much too short to find the biologically relevant conformations and make statistically reliable statements about transition rates. This problem also hinders the improvement of molecular models towards the reliable prediction of experimental observables. In the proposed work we will develop an adaptive conformation dynamics (ACD) which facilitates the simulation of slow biomolecular processes on small CPU clusters using atomistic models. This method will be applied in order to elucidate the detailed structural mechanism of the activation of the G-protein coupled receptor Rhodopsin.

    http://compmolbio.biocomputing-berlin.de/index.php/projects/93-dfg825-3-1
  • A-AP14

    Conformational dynamics of biomolecules: Reconciling simulation and experimental data

    Prof. Dr. Frank Noé

    Project heads: Prof. Dr. Frank Noé
    Project members: -
    Duration: 01.01.2012 - 31.12.2018
    Status: running
    Located at: Freie Universität Berlin

    Description

    We develop methods for constructing kinetic models of biomolecular conformation dynamics from single-molecule experimental data, or by reconciling kinetic experimental data and molecular dynamics simulation data. In the present funding period we aim at developing an approach to directly compute MSMs from single-molecule experiments, with the following main objectives:
    1. Estimate conformation dynamics (eigenvalues, eigenvectors of the underlying Markovian dynamics) directly from single-molecule trajectories.
    2. Quantify the estimation errors of 1.
    3. Applications to optical tweezer data (collaboration with Susan Marqusee).
    4. Use 1. to compute improved Markov state models; Applications to simulation data.
    5. Provide publicly available software implementation.


    http://compmolbio.biocomputing-berlin.de/index.php/projects/91-dfg825-2-2