Welcome to the Biomedical Physics Group!

"Insight must precede application."
Max Planck

Our group focuses on understanding the principles of complex dynamics of biological systems at the cell, tissue and organ level and their application in medicine.

Life-threatening cardiac rhythm disorders such as ventricular fibrillation are associated with complex, self-organizing spatio-temporal electromechanical excitation of the heart. We are developing novel cardiac imaging techniques, numerical simulations and machine learning methods to understand and efficiently control the dynamics of electromechanical waves in the heart muscle (Nature 2018, 2011).

Our translational research group is affiliated with the Max Planck Institute for Dynamics and Self-Organization, the University Medical Center Göttingen and the German Center for Cardiovascular Research (DZHK).

Research

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CaosDB

Additional Information CaosDB is an open source research data management system developed at the research group biomedical physics. It …

Machine Learning

Machine learning tasks in cardiac research include: data classification (e.g., time series, images, evolution of patterns and shapes) …

Digitalization - Global Carbon Cycle

Project Information Project Title: Global Carbon Cycling and Complex Molecular Patterns in Aquatic Systems: Integrated Analyses …

Research Overview

Self-organized complex spatial-temporal dynamics underlies dynamic physiological and pathological states in excitable biological …

Analyzing and Classifying Cardiac Biosignals

The electrocardiogram (ECG) provides a noninvasive transthoracic interpretation of the electrical activity of the heart. Cardiovascular …

Cell Culture Experiments

The mechanism underlying the recruitment of wave emission from heterogeneities in electrical conductance is shown in Fig. 1A. In the …

Characterization of multiple spiral waves

This project aims at characterizing a multiple spiral wave system from a large-scale perspective. Our goal was to develop improved …

Control of Spatio-temporal Chaos in the Heart

Spatiotemporally chaotic wave dynamics underlie a variety of debilitating crises in extended excitable systems including the heart. …

Data Driven Modeling of Cardiac Dynamics and Model Evaluation

The development of detailed physiological models of the heart, the availability of large quantities of high-quality structural and …

Electromechanical Waves and Instabilities in Cardiac Tissue

During cardiac fibrillation, the coherent mechanical contraction of the heart is disrupted by vortex-like rotating waves or scroll …

High-resolution Cardiac Imaging

Physiological cardiac modeling requires detailed structural, functional, and dynamical characterization of the heart. The MPRG …

Imaging Electro-mechanical Waves

The measurement of electro-mechanical waves in cardiac tissue remains a major experimental challenge. Conventional fluorescence imaging …

Low-energy Control of Atrial Fibrillation

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide affecting an estimated 5.5 million people worldwide. …

Mathematical Modeling of the Heart

We are using mathematical models of cardiac tissue with various levels of complexity, ranging from generic to detailed physiological …

Modeling Reaction-diffusion-mechanics

In the heart, electrical excitation propagates through diffusively coupled cardiac cells and subsequently results in contraction and …

Stability Analysis

Two important questions in the investigation of cardiac arrhythmias are how these activation patterns develop and how their complexity …

State and Parameter Estimation

While physical models often can be derived from first principles, they may contain parameters whose values are not or only partially …

Synchronization Patterns in Transient Spiral Wave Dynamics

An important approach for analyzing spatially extended systems is “network analysis” (also called graphical methods) where time series …

Time Series Analysis of Cardiovascular Biodata

Characterization and classification of cardiac dynamics on the basis of measured time series (e.g. electrocardiogram, ECG) is crucial …

Unpinning Spiral Waves

While FF-AFP provides remarkable energy reduction compared to conventional therapeutic approaches, the underlying mechanisms remain …

News

BMPG PhD candidate receives Christiane Nüsslein-Volhard Stipend for Mothers in Science

Justine Wolter has received the competitive Christiane Nüsslein-Volhard stipend for young mothers in science. The award aims to support …

BMPG scientist receives poster award

Dr. Sayedeh Hussaini has received the prize for the best poster at the WE-Heraeus-Seminar “Outstanding Challenges in Nonlinear …

BMPG scientist receives offer for professorship

Dr. Thomas Lilienkamp has received and accepted an offer for professorship in the field of „Applied Computational Physics in den Life …

Recent Publications

Learning from the past: reservoir computing using delayed variables. Frontiers in Applied Mathematics and Statistics 10: 1221051 (2024).

DOI

Winners of 2022 Edward Norton Lorenz Early Career Awards. Chaos: An Interdisciplinary Journal of Nonlinear Science 33: 110402 (2023).

DOI

Attractor selection in nonlinear oscillators by temporary dual-frequency driving. Nonlinear Dynamics 111: 19209–19224 (2023).

DOI

Ordinal pattern-based complexity analysis of high-dimensional chaotic time series. Chaos : an interdisciplinary journal of nonlinear science 33: 053105 (2023).

DOI

Reconstructing in-depth activity for chaotic 3D spatiotemporal excitable media models based on surface data. Chaos: An Interdisciplinary Journal of Nonlinear Science 33: 013134 (2023).

DOI

Contact

  • stefan.luther@ds.mpg.de
  • +49 551 5176 301
  • Am Faßberg 17, Göttingen, D-37077, Germany