Projects linked to HDS-LEE

IRTG 2379 - Hierarchical and Hybrid Approaches in Modern Inverse Problems

The International Research Training Group (IRTG 2379) „Hierarchical and Hybrid Approaches in Modern Inverse Problems“, is funded by the German Research Foundation (DFG) and started on October 2018, and in November 2022 was approved for a second phase. It is a cooperation between RWTH Aachen University and the University of Texas at Austin with the following HDS-LEE PIs involved in this program: Prof. Marek Behr, Prof. Alexander Mitsos, Jun.-Prof. Benjamin Berkels, Prof. Julia Kowalski, Prof. Raúl Tempone, and the associated PIs Prof. Abigail Morrison, Prof. Michael Herty, Prof. Stefanie Elgeti. The doctoral theses address modern inverse problems and introduce a new innovative approach to the education of future scientists and engineers. They focus on the challenges posed by the interplay of four specific topics: Geometry, Data, Models, and Applications. The training program is open to HDS-LEE doctoral researchers and a regular exchange takes place.

Data Plant

The HDS-LEE PI Prof. Björn Usadel and his working group at IBG-4 is involved in the recently initiated research consortium ‘DataPLANT’ (NFDI4Plants). The consortium is one of a total of nine in the National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur - NFDI) initiative, which focuses on the systematic indexing, editing, sustainable storage and accessibility of science and research data resources and (inter)national networking. ‘DataPLANT’ aims to develop a service and data infrastructure enabling modern plant research to collect and provide large amounts of data in compliance with the FAIR data principles. With their tools that are currently being developed, ‘DataPLANT‘ is striving to use existing services and make them accessible rather than to create a completely new infrastructure.

‘DataPLANT’ is coordinated by the Albert-Ludwigs-University of Freiburg (Spokesperson: Dr. Dirk von Suchodoletz). Co-applicant institutions are the Eberhard Karls University Tübingen (Co-spokesperson: Dr. Jens Krüger), the Technical University of Kaiserslautern (Co-spokesperson: Jun. Prof. Timo Mühlhaus) and the Forschungszentrum Jülich. Prof. Björn Usadel acts as one of the co-spokespersons of ‘DataPLANT’. His group will focus on the development of plant research (meta)data standards (Standardization), ensuring data quality including the completeness of the metadata (data quality), and interoperability to ensure the (re)usability of the research data (data/workflow interoperability). These efforts will be conducted to strengthen and coordinate standardization efforts in plant research-related data and workflow annotation and will be closely linked with other relevant NFDIs nationally and internationally projects. Prof. Björn Usadel’s research work for ‘DataPLANT’ will be strongly connected to HDS-LEE project ‘Framework for Meta Data Analysis and Interactive Exploration of large Plant FAIR data sets’ to use synergies of both projects and to connect them.

SDL DIGITAL PATIENT @NHR4CES

HDS-LEE PIs Prof. Gulia Rossetti and Prof. Andreas Schuppert lead the Simulation Data Lab (SDL) ‘Digital Patient’ together with their colleague Prof. Dorit Merhof within the big consortia of NHR4CES. The aim is to develop a personalized model of a patient for diagnostics, prediction of disease progression and therapy outcome by using integrate HPC resources with ML algorithms and simulations to develop model-based medical applications, ranging from molecular modeling of disease mechanisms up to patient scale system medicine models. The SDL will provide the full range of expertise from simulations of biochemical processes on a molecular scale, integration of heterogeneous data sources from genomics to clinical monitoring data with biomedical knowledge into hybrid models up to integrated image analysis on the patient scale.

In NHR4CES (NHR for Computational Engineering Science), RWTH and TU Darmstadt cooperate to foster computational engineering sciences in joint projects, graduate schools and study programs. The focus is on engineering applications, especially in complex flow scenarios or energy conversion, materials design, engineering physics and chemistry. In recent years, research competencies in parallel computing, data management and artificial intelligence have also been developed.

The National High Performance Computing (Verbund für das Nationale Hochleistungsrechnen – NHR) Alliance includes university computing centers of national importance as NHR centers, which are financially supported by the federal and state governments, as agreed by the Joint Science Conference (Gemeinsame Wissenschaftskonferenz - GWK) in November 2018. This form of collaboration ensures that scientists at German universities receive the best possible support for high-performance computing. Eight computing centers were included in the NHR funding.

SATOMI -Tackling the segmentation and tracking challenges of growing colonies and microbial diversity

The project “Tackling the segmentation and tracking challenges of growing colonies and microbial diversity (SATOMI)” combines the expertise of PIs from two HIDSS: Dr. Katharina Nöh (part of the group of HDS-LEE PI Prof. Wolfgang Wiechert, FZJ, project coordinator), Prof. Hanno Scharr (part of the group of HDS-LEE PI Prof. Ira Assent, FZJ), Dr. Dietrich Kohlheyer (FZJ) and Prof. Ralf Mikut (KIT, HIDSS4Health PI and director). It is funded by the Helmholtz Initiative and Networking Fund from May 2021 - April 2023 and part of the Helmholtz Imaging Platform (HIP). The main goal of SATOMI is to provide the first benchmark data set and the deep learning-based framework SATOMI for the segmentation and DL-assisted tracking of diverse microbes with different morphologies. Such framework should be able to reach good accuracy already for new use cases, ideally without any additional annotation effort. Such a framework is aimed at avoiding the recurring development of highly specialized “one-off” tools for new use cases from different domains such as biotechnology, environment, and health. Johannes Seiffarth, a doctoral researcher of this project, was associated in HDS-LEE in 2021.

EMSIG - Event driven Microscopy for Smart mIcrofluidic sinGle-cell analysis

The project EMSIG (Event driven Microscopy for Smart mIcrofluidic sinGle-cell analysis) is a follow-up project of SATOMI that deals with microfluidic live-cell imaging (MLCI) that provides spatio-temporal insights into population heterogeneity ensuing from a single cell. For becoming a versatile screening tool, MLCI must master the challenge of robust real-time analysis, capable to autonomously detect rare events in hundreds of parallel experiments and to follow their temporal evolution. Using deep-learning empowered image analysis, EMSIG brings live-event detection capabilities to MLCI, to facilitate the reactive optimization of biological event resolution in real-time. EMSIG is funded by the Helmholtz Initiative and Networking Fund, part of the Helmholtz Imaging Platform (HIP), and will start in May 2023 with the following PIs at FZJ: Dr. Dietrich Kohlheyer, Dr. Hanno Scharr (HDS-LEE PI), Dr. Erenus Yildiz (FZJ), Dr. Katharina Nöh (part of the group of HDS-LEE PI Prof. Wolfgang Wiechert, project coordinator), and Prof. Ralf Mikut from KIT who is HIDSS4Health PI and director.