Digitalization - Global Carbon Cycle

Project Information Project Title: Global Carbon Cycling and Complex Molecular Patterns in Aquatic Systems: Integrated Analyses Powered by Semantic Data Management Project Duration: 04/2021 – 12/2024 Funding Agency: Volkswagen Foundation Lead-PI: Prof. Dr. Thorsten Dittmar Abstract Over the past decade, a wealth of highly diverse molecular data regarding dissolved organic matter and marine microbes have been collected. To gain deeper insights into the global carbon cycle it is necessary to combine these very complex, heterogeneous data in the oceanographic context.

Research Overview

Self-organized complex spatial-temporal dynamics underlies dynamic physiological and pathological states in excitable biological systems including the heart and brain [1-3]. Nonlinear dynamics and statistical physics provide novel analytical concepts to enhance the understanding of these complex spatial-temporal systems. Based on these concepts, the Max Planck Research Group Biomedical Physics (MPRG BMP) aims to develop highly innovative experimental and theoretical approaches towards modeling, analysis and control of electrical-mechanical forms of heart disease.

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. Current strategies for controlling these dynamics employ global, largeamplitude perturbations acting indiscriminately on the system as a whole. For example, the automated external defibrillator commonly found in airports gives via two electrodes a high-energy (1000 V, 30 A, 12 ms) electric shock to terminate the chaotic and ultimately lethal wave dynamics underlying ventricular fibrillation.

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 functional experimental data, and ever-increasing computational power have significantly enhanced the understanding of cardiac dynamics and hold the promise of new clinical applications for diagnosis and treatment of heart disease. However, the systematic integration of experimental data into high-dimensional, multi-scale models and their subsequent evaluation, validation and analysis remains a major challenge. Therefore, we are developing a data driven, integrative strategy that combines high-resolution imaging techniques with state of the art numerical modeling through innovative state estimation methods.

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 waves of electrical activity, which share topological analogies to point vortices and vortex filaments in hydrodynamic turbulence [1]. The dynamics of these filaments and their electro-mechanic instabilities due to the nonlinear interaction with the anisotropic, heterogeneous substrate and the complex boundaries of the heart result in self-organized disordered dynamics. Furthermore, it has been shown that tissue deformation itself may affect electrical wave propagation and its stability, where both pro-arrhythmic and anti-arrhythmic effects have been observed [2].

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 for distinguishing physiological from pathological states with potential applications for diagnosis and risk assessment. Since the heart is an extended system, its spatiotemporal electro-mechanical activities in combination with additional control loops of the cardiovascular system form the basis of most observed signals and any (nonlinear) response to perturbations. In particular, complex spatiotemporal dynamics in terms of rotating action potential waves called spiral waves and their subsequent breakup into additional spiral waves has a strong impact on heart rate variability and may cause many cardiac disorders.