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    Numerische Modellierung der Steuerung der Motilität des menschlichen Magens durch die Hirn-Magen-Achse
    Many gastric disorders of the highest clinical relevance are assumed to have their origin in the so-called brain-stomach axis, the coupling between the brain and the mechanical motility of the stomach. However, to date the brain-stomach axis remains poorly understood, which severely limits our ability to improve and design efficient therapies against gastric disorders. The aim of this project is to develop the first comprehensive computational model of both the mechanical and biological aspects of the brain-stomach axis. This model will incorporate a wide range of anatomical, morphological, physiological, biochemical, pharmacological, mechanical and neurophysiological data to provide an accurate basis for a multilevel analysis of the interrelated biological processes involved in gastric motility. It is expected that the model will offer unique insights into the pathophysiological foundations of two gastric motility disorders, namely myenteric neuropathy and gastroparesis. The results of this project bear great potential to help improve existing and develop new diagnostic tests, to accelerate drug discovery and to rationalize therapeutic interventions in treating the above gastric motility disorders.
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    Experimental analysis and mathematical modelling of mechano-regulated growth and remodelling processes in urinary bladders of post-pubescent pigs
    The urinary bladder is a central organ of vertebrates. Its primary function, the storage of significant volumes of urine, implies very large deformations, which leads to an important role of mechanics in the urinary bladder. In particular growth and remodeling processes controlled by mechanical stimuli play an important role both in health and disease. For example, partial outlet obstruction of the bladder - from which 50% to 75% of the male population older than 50 years suffer - frequently results in pathological growth and remodeling with consequences such as ischuria. However, the biomechanical foundations of mechano-regulated growth and remodeling in the bladder remain poorly understood to date. This project aims to understand and quantify mechano-regulated growth and remodelling processes in the urinary bladder. To this end, a series of biomechanical and immunohistochemical experiments with urinary bladders of post-pubescent pigs of different ages will be conducted. The results of these experiments will serve as a basis for the development of the world-wide first mathematical/computational model of mechano-regulated growth and remodeling in the urinary bladder. To exclude confounding effects of hormones, their concentrations will be measured and incorporated appropriately in the model. The mathematical model will be based on the theory of constrained mixtures. Its parameters and functional relations will be determined from the experimental data using a sequential Monte-Carlo method. Finally, the model will be used to study for the first time with a very simple computational model mechano-regulated growth and remodeling in case of a partial outlet obstruction of the urinary bladder. This way, the applicability of the results of this project to clinically relevant questions will be demonstrated. Although this project mainly focuses on the relation between mechanical stimuli and growth and remodeling, it will, as a byproduct, also reveal for the first time how hormonal and mechanical factors superimpose in controlling growth and remodeling in the urinary bladder. A particular strength of the model developed in this project will be its strong and quantitative data basis, noting that most mathematical/computational models of mechano-regulated growth and remodeling in soft tissue so far still suffer from an insufficient experimental basis.
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    SFB 986: Subproject B09 - Microstructure-based classification and mechanical analysis of nanoporous metals by machine learning
    In this research project mathematical methods will be developed to characterize precisely and comprehensively the complex microstructure of nanoporous metals. Based on such characterization, on the one hand machine learn-ing will be used to classify nanoporous metals with regard to their microstructure, which will allow quantitative statements how different production processes affect the microstructure and thus the physical properties of nanopo-rous metals. On the other hand, a new approach will be developed that combines the finite element method with machine learning in order to enable substantially acceler-ated micromechanical simulations of nanoporous metals. The scientific objective of this project is to develop ad-vanced computational methods that can help to unravel the relation between processing, microstructure and me-chanical properties in nanoporous metals. These methods can be used in other projects of the SFB 986, in particular in the projects B2 and B4, to better understand and optimize the favorable properties of nanoporous metals.
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    Computational Multiphysics Modeling of the Postprandial Human Stomach
    Health problems related to the mechanics of the stomach (gastric mechanics) are among the most important causes of morbidity in industrialized countries. For example, around 10% - 20% of the population in Western countries suffers from gastro-esophageal reflux disease (GERD), which results from a mismatch between the intragastric pressure and the closing pressure of the esophagus. Around 10% - 45% of the general population suffers from dyspepsia, that is, difficult digestion, which is often linked to the motility of the stomach. Altogether, healthcare costs related to the stomach and gastrointestinal tract are roughly comparable to the ones associated with cardiovascular diseases. Interestingly, this tremendous importance of the stomach is not at all reflected by current research efforts. In the program of the 7th World Congress of Biomechanics in 2014 with more than 4000 presentations common cardiovascular keywords (cardio, cardiac, vascular, heart, coronary, aneurysm, artery, arteries, arterial, athero/arteriosclerotic) are found 424 times, keywords related to the stomach (stomach, gastric, oesophagus, esophageal, esophagus) only 3 times. Three-dimensional computational fluid mechanics was first applied to arteries around 1990, to the stomach only in 2007. Computational models incorporating fluid-structure interactions were developed for the vasculature already in the mid-1990s but no such model has been proposed for the stomach so far. In short, computational modeling of the stomach lags around 20 years behind modeling of the cardiovascular system. This project aims at closing this important gap in biomechanical research by developing the first computational multi-physics model of the human stomach. This major objective will be achieved in four steps: first, tissue patches from the human stomach will be harvested in the hospital and subjected to biaxial mechanical testing in order to determine the mechanical constitutive properties of human gastric tissue; second, data about the dynamic deformation of the human stomach during digestion and emptying will be collected by magnetic resonance imaging (MRI); third, on the basis of these mechanical and imaging data, a computational multiphysics model of the human stomach will be developed that incorporates the mechanics and electrophysiology of the gastric wall, the fluid mechanics of the digesta and also fluid-structure interactions; fourth, using this computational model in-silico studies will be performed that will enable for the first time a mechanistic understanding of the complex interplay between the different physiological processes and parameters in the stomach and the way how they affect digestion of food. The computational model developed in this project can be expected to provide a valuable basis for future research projects examining the link between gastric mechanics and disorders and pathologies like dyspepsia, gastro-esophageal reflux disease, or morbid obesity.
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    Emmy Noether: Vascular Growth and Remodeling in Aneurysms
    The emerging field of mechanobiology, which examines the effect of mechanical stimuli on physiological processes on both cellular and tissue scale, has attracted rapidly increasing attention during the last two decades. An important application of this discipline is growth and remodeling of biological tissue especially in the vasculature. These play an important role not only during adolescence, but for example also in aneurysms, pathological local dilations of blood vessels, which often keep growing and finally rupture causing significant morbidity and mortality. So far the rupture risk of aneurysms is mainly estimated based on their diameter or the mechanical stresses in their walls, while future growth and remodeling is largely neglected despite its obviously high relevance. An important reason for this is certainly the still fairly limited accuracy of quantitative models developed in this field during the last decade.This research project thus pursues four main goals:First, significantly enhanced quantitative models for growth and remodeling in the vasculature will be developed by a sufficiently precise incorporation of the most important underlying microscopic processes and the derivation of a mathematically more rigorous foundation.Second, computer simulations based on these enhanced models will be conducted by means of the finite element method in order to examine so far only poorly understood phenomena in the growth of aneurysms such as the frequent occurrence of intracranial saccular aneurysms behind bifurcation points in the vasculature.Third, risk indices for aneurysms will be developed which allow for a diagnosis no longer almost exclusively on the basis of their diameter or wall stress, but rather based on mathematically well-founded models of their future growth and remodeling.Fourth, a new class of computer aided diagnosis methods for aneurysms will be developed which incorporate patient-specific physiological factors (beyond just the vessel geometry) as precisely as possible and utilize not only simple scalar risk indices, but rather complex decision making algorithms trained by machine learning.Generally, this research project is intended to contribute to the extension of the principles and methods of engineering mechanics - a discipline which is so far mainly employed in mechanical and civil engineering - in such a way that also the behavior of biological systems can be increasingly well understood and predicted.
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