Prof. Dr. Christof Schütte

Deputy Chair

Zuse Institute Berlin (ZIB)
Takustraße 7
14195 Berlin
+49 (0) 30 +49 (0)30 84185101
schuette@zib.de

Scientist in Charge for Application Area Clinical Research and Health Care



Research focus

Modelling Simulation and Optimization for Multiscale Processes, especially for Biomolecular, Cellular, and Network Dynamics; Transfer Operator Approach to Metastable Processes; Model Reduction; Time Series Analysis; Sparse Data Analysis; Stochastic Optimal Control; Uncertainty Quantification; Bayesian Inverse Problems

Projects as a project leader

  • CH2

    Sparse compressed sensing based classifiers for -omics mass-data

    Prof. Dr. Tim Conrad / Prof. Dr. Gitta Kutyniok / Prof. Dr. Christof Schütte

    Project heads: Prof. Dr. Tim Conrad / Prof. Dr. Gitta Kutyniok / Prof. Dr. Christof Schütte
    Project members: Nada Cvetkovic / Martin Genzel
    Duration: -
    Status: running
    Located at: Freie Universität Berlin / Technische Universität Berlin

    Description

    Tumor diseases rank among the most frequent causes of death in Western countries coinciding with an incomplete understanding of the underlying pathogenic mechanisms and a lack of individual treatment options. Hence, early diagnosis of the disease and early relapse monitoring are currently the best available options to improve patient survival. In this project, we aim for the identification of disease specific sets of biological signals that reliably indicate a disease outbreak (or status) in an individual. Such biological signals (e.g. proteomics or genomics data) are typically very large (millions of dimensions), which significantly increases the complexity of algorithms for analyzing the parameter space or makes them even infeasible. However, these types of data usually exhibit a very particular structure, and at the same time, the set of disease specific features is very small compared to the ambient dimension. Such a high-dimensional setting naturally calls for the application of the concept of sparse classifiers, which has been extensively studied in the fields of compressed sensing and statistical learning during the last decade. Our research focuses on both algorithmic improvements of available methods as well as theoretical results such as recovery guarantees for general data models.

    http://medicalbioinformatics.de/research/projects/ecmath-ch2
  • CH6

    Uncertainty quantification for Bayesian inverse problems with applications to systems biology

    Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte

    Project heads: Prof. Dr. Susanna Röblitz / Prof. Dr. Christof Schütte
    Project members: Dr Ilja Klebanov
    Duration: -
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    In biotechnology, systems biology, or reaction engineering one is faced with large systems of ordinary differential equations (ODE) that are used to describe the kinetics of the reaction network of interest. These ODE models contain a large number of mostly unknown kinetic parameters that one needs to infer from usually sparse and noisy experimental data. Typically, inverse problems like classical parameter identification are associated with ill-posed behaviour. However, Bayesian approaches can be used to recover joint parameter distributions and allow for the quantification of uncertainty and risk in a way demanded by the applications. In this project, we want to overcome the computational limitations of classical Markov-chain Monte-Carlo methods by developing new algorithmic approaches to Bayesian inverse problems using, e.g., sparse approximation results or empirical Bayes methods. The methods will directly be applied to large-scale networks in systems biology.

    http://www.zib.de/projects/UQ-systems-biology
  • CH7

    Network-of-Network based -omics data integration

    Prof. Dr. Tim Conrad / Prof. Dr. Christof Schütte

    Project heads: Prof. Dr. Tim Conrad / Prof. Dr. Christof Schütte
    Project members: -
    Duration: -
    Status: running
    Located at: Freie Universität Berlin

    Description

    Project Background

    Pancreatic cancer is the fifth leading cause of cancer death in Germany (see DKFZ Report, 2010). It is estimated that in 2030 it will be the second leading cause of cancer death incurring a cost of about 15,8 Billion US-Dollar worldwide to the public health systems.

    Cancer is a systems disease

    "Cancer is no more a disease of cells than a traffic jam is a disease of cars. A lifetime of study of the internal-combustion engine would not help anyone to understand our traffic problems.'" (Smithers1962). It is accepted that gene mutations are part of the process of cancer, but mutations alone are not enough. Cancer involves an interaction between neoplastic cells and surrounding tissue on many different levels, e.g. interaction of RNA molecules, proteins, and metabolites. But most available models are limited to only one or very few levels of interactions and describe a rather static view.

    From single to multi source: data integration on a systems level

    Current high-throughput -omics technologies have dramatically eased the production of part lists for a variety of organisms. What is still missing are the dynamic interactions among an organism's molecular parts, and the interactions between different biological levels, such as transcriptomics and proteomics. This is pivotal to better understanding of an organism's biology, and - in our case - to understand pancreas cancer.

    Therefore, the aim of this project is two-fold: (1) use data acquired in our earlier projects to create a holistic integration of the aforementioned sources and levels for modeling pancreas cancer, which we call Network-of-Networks or short: NoN (in our context networks of different -omics levels, such as genomics, transcriptomics, proteomics and metabolomics. (2) A NoN is a very large and complex object and its structure differs significantly from other biological networks. Thus, new methods for complexity reduction and analyzing NoNs will be developed in this project.

    http://medicalbioinformatics.de/research/projects/ecmath-ch7
  • CH-AP8

    Probing scales in equilibrated systems by optimal nonequilibrium forcing

    Prof. Dr. Carsten Hartmann / Prof. Dr. Christof Schütte / PD Dr. Marcus Weber

    Project heads: Prof. Dr. Carsten Hartmann / Prof. Dr. Christof Schütte / PD Dr. Marcus Weber
    Project members: -
    Duration: 01.10.2014 - 30.06.2018
    Status: running
    Located at: Freie Universität Berlin

    Description



  • CH-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
  • CH-AP16

    Projection theory of transfer operators

    Prof. Dr. Christof Schütte / PD Dr. Marcus Weber

    Project heads: Prof. Dr. Christof Schütte / PD Dr. Marcus Weber
    Project members: -
    Duration: 01.01.2014 - 31.12.2017
    Status: running
    Located at: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Description

    The main object is analysing sufficent ways to compute a galerkin approximation of the transfer operator. This includes to study the theoretical properties of a galerkin approximation of specific systems and to development algorithms which guarantee those properties for the numerical approximation. Furthermore, one is interested in making this computation as cheap as possible.

    http://www.zib.de/projects/projection-theory-transfer-operators