e:Med Projektgruppe Modellierung von Krankheitsprozessen

Die Projektgruppe (PG) Modelling of Disease Processes ist eine neue Initiative. Das Ziel der PG ist die Entwicklung von Computermodellen für Krankheitsprozesse. Krankheiten sind oft durch mehrfache Störungen in verschiedenen biologischen Prozessen gekennzeichnet, die von der Genexpression, dem Stoffwechsel, der Signalübertragung bis hin zur Zell-Zell-Kommunikation, der Immunantwort oder der Gewebeorganisation reichen.

Wir werden uns auf die technische Entwicklung von Modellen konzentrieren, z. B. die Integration der Prozesse für eine bestimmte Krankheit, die Modellierung von Störungen, die Einbeziehung der Heterogenität von Patienten oder die Verwendung verschiedener Datentypen. Dieser Ansatz wird die Zusammenarbeit zwischen mathematischen Modellierern, Bioinformatikern, Biologen und Klinikern stärken und den Austausch zwischen Wissenschaftlern, die an verschiedenen Krankheiten arbeiten, ermöglichen, was zu einem besseren Verständnis der Krankheitsmechanismen führt. Weitere thematische Vorschläge können sich aus der PG ergeben. Praktische Aktivitäten sind zunächst Treffen, Erfahrungsaustausch und die Organisation von Workshops.

Alle e:Med-Mitglieder sind herzlich eingeladen, sich an der PG zu beteiligen.


Teilnehmer des e:Med Satellite Workshop am 16. Mai 2022, Heidelberg

Online Seminar Series

The e:Med project group Modeling of Disease Processes started an Online Seminar Series (Zoom) “Modelling approaches for disease processes” in January 2022.
Time: Every 1st Wednesday of the month, 2 p.m. for the winter semester (Jan-Jun 2022)  - Summer break until October 2022.
Aim/ Focus: Introduction, discussion and comparison of different mathematical modelling approaches. For initial list of topics and speakers see below.
Duration: 30’ talk +15’ discussion. We would like to have a focus on discussion. In cases of 2 speakers the duration can be 40 min + discussion.
Targeted audience: PhD and master students, postdocs, group leaders in the modelling field.

Location: ZOOM



The talks start at 2 p.m.

January 12, 2022. Using Disease Maps in Biomedical Research. Olaf Wolkenhauer, Shailendra Gupta, Matti Hoch

Prof. Dr. Olaf Wolkenhauer, Dept of Systems Biology & Bioinformatics, University of Rostock

Dr. Shailendra Gupta, Systems Biology & Bioinformatics, University of Rostock

Matti Hoch, Systems Biology & Bioinformatics, University of Rostock

Location: online ZOOM


Using Disease Maps in Biomedical Research

Olaf Wolkenhauer, Shailendra Gupta, Matti Hoch (Systems Biology & Bioinformatics, University of Rostock)
Disease maps are interactive, web-based representations of the phenotypic, cellular, and molecular processes underlying complex diseases in the form of knowledgebase. Disease maps provide a platform to integrate heterogeneous clinical and experimental data for formulating hypotheses on diagnostic, prognostic, and/or therapeutic markers using systems biology and bioinformatics-based methods.  In the seminar, we shall contrast the disease map approach with small-scale pathway modelling efforts and present examples from cancer systems biology and demoing the Atlas of Inflammation Resolution (AIR) (https://air.bio.informatik.uni-rostock.de) The AIR contains 40 manually curated submaps including > 6T elements each of which are manually annotated. In addition, the AIR is enriched with regulatory layers of microRNAs, lncRNAs, and transcription factors that form a large molecular interaction map (MIM) with >23,000 molecular interactions. We developed workflows that facilitate the integration and analyses of multi-omics data directly on the disease maps and enable users to perform in silico perturbation experiments for formulating data-driven hypotheses. In the seminar, we hope for a discussion in which we compare the “disease map approach” with other pathway and network modelling approaches.

February 2, 2022. Large-scale pathway models for biomedical research. Jan Hasenauer

Prof. Dr. Jan Hasenauer, LIMES Institute, Universität Bonn

Target audience: PhD and master students, postdocs, group leaders in the modelling field.

Location: Zoom


Large-scale pathway models for biomedical research.

Pathway models are powerful tools in modern life sciences. They are based on knowledge of individual biological pathways and can be integrated into large-scale models to capture crosstalk. In this seminar, I will briefly introduce the modeling approach and methods used to create large-scale models. I will then present a large-scale model of cancer signal transduction with thousands of biochemical species and reactions as an example application. Variants of this model have been used for multi-omics analyses in murine model systems and human cell lines. We have developed workflows for parameterization and analysis of these models. I hope to discuss these approaches and future directions. 



March 2, 2022. Response-Time Modelling of Cell-Cell Interaction Dynamics . Kevin Thurley

Prof. Kevin Thurley, Biomathematik, Institut für Experimentelle Onkologie, Universitätsklinikum Bonn, https://www.thurleylab.org 

Location: Zoom 


Response-Time Modelling of Cell-Cell Interaction Dynamics

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. In this talk, I will introduce a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships (Thurley K, Wu LF, Altschuler SJ, Cell Systems 2018, DOI: 10.1016/j.cels.2018.01.016). The approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps. Here, response-time modeling will be presented in the context of data-driven analysis of T cell differentiation and expansion in chronic viral infections. 


April 6, 2022. Multi-scale modelling. Martin Falcke, Lutz Brusch.

Multi-scale modelling

Dr. Martin Falcke, Max-Delbrück-Centrum für Molekulare Medizin (MDC)
Dr. Lutz Brusch, Research Group Leader,Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden

Location: ZOOM


Biological systems comprise many structural levels represented by molecules, cell organelles, cells, tissues, organs, organisms, populations and ecological levels. Each structural levels has its typical time and length scale from fs and nm on molecular level to km and years on ecological level. Multiscale modelling refers to mathematical models describing dynamics on different structural levels. Here, several structural levels and several scales may be accounted for either by a single equation or by multiple coupled equations each comprising only one scale. We present examples of both approaches.
In the first case, we present reaction diffusion systems for cardiac myocytes taking behaviour of individual ion channels into account and simulating whole cells at the same time. The mathematical problems and methods addressing them will be explained. For the second case, we introduce coupling of processes between intracellular, cellular and tissue scales. Such coupling requires that parameters of model processes on one scale get driven by the dynamical variables of model components at another scale. We demonstrate such model integration across multiple scales in the software framework Morpheus (https://morpheus.gitlab.io) and discuss the emergence of complex 'morphodynamic' behaviour that cannot be addressed by simpler models at a single scale.



May 4, 2022 Easy implementation and acceleration of spatial agent-based modelling of biological processes using rule-based modelling. Gavin Fullstone, Markus Morrison.

Dr. Gavin Fullstone, University of Stuttgart, Stuttgart Research Centre Systems Biology

Prof. Dr. Markus Morrison, University of Stuttgart, Institute of Cell Biology and Immunology

Location: ZOOM


Easy implementation and acceleration of spatial agent-based modelling of biological processes using rule-based modelling

Agent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent- based applications but generally requires specialist coding knowledge and extensive optimisation. In this seminar, we will demonstrate how we implemented and utilise a rule-based modelling approach to define particle-based models that can be flexibly parallelised on either CPU or GPU-architecture. In comparison with an established particle-based simulator, we achieve speed ups of >650-fold on modern GPUs whilst maintaining robust simulation. Additionally, we will demonstrate how we apply stochastic hybrid approaches to model the activity of multi-protein complexes and their contribution to cell signalling outcomes. In introducing these approaches, we will simultaneously present specific examples where we have successfully applied these approaches to help us understand fundamental biological processes, predict outcomes in biologically relevant scenarios and in the design of targeted therapeutics.


June 15, 2022 How mathematical models support clinical drug development: an industry perspective. Bernhard Steiert

Dr. Bernhard Steiert, Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland

Location: ZOOM



How mathematical models support clinical drug development: an industry perspective

Mathematical modelling is a key component of drug development and is applied at all stages of a molecule progressing along the value chain. In clinical development, it is essential to find the right dose for every patient, which is composed of the right amount of drug and the right treatment interval for the right patient population. Modelling is also used to leverage and extrapolate the available data, informing decisions on which programs to accelerate and which ones to stop.
In this talk, an overview of the Roche Pharma Research and Early Development (pRED) setup for modelling will be provided. Typical models used in the pharmaceutical industry will be exemplified and, based on those, more general modelling themes are outlined and discussed. Involvement of key internal and external stakeholders is crucial for impacting decisions with modelling. I will summarize who they are and which communication strategies are most suitable for building trust in modelers and their models. The talk will close with examples where modelling was impactful and helped to bring safe and efficacious medicines to patients faster.


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