Roeder Lab: Medical systems biology and biometry

The research focus of the Roeder Lab comprises topics from the areas: systems biology / medicine (in particular mathematical modelling and computer simulation), medical biometry / statistics, as well as data integration and study design. A major objective of the research activities in the group is the combination of basic research strategies with clinical applications. This aim leads to a high degree of interdisciplinary, which is reflected by the different research foci listed above. The overarching research theme of the group is the use of theoretical methods and mathematical / statistical modelling approaches to foster evidence-based clinical decision-making and the efficient design and analysis of experimental studies and clinical trials ("model-based medical research“).

Research areas

Systems biology, systems medicine, mathematical modelling

This research focus deals with the development and the application of mathematical models in different areas of the life sciences. Beside basic research questions in biology (e.g. clonal competition at the level of stem cells, stem cell – niche interactions or cell migration), we specifically address topics with an explicit clinical relation (e.g. disease and treatment models of different leukemia types and other malignancies). It is our objective to better understand and predict mechanisms underlying dynamic physiological and pathophysiological processes through analytical approaches and/or computer simulation (including "digital twins") and, thus, to support clinical decision-making.

Medical Biometry and Bioinformatics

This research area focuses on the development and application of innovative data analysis methods, including the fields of experimental and clinical trial design. Beside methods from “classical” biometry / statistics, we are also working on machine learning and bioinformatics methods including techniques for the integrative analysis of molecular high-throughput data and for the analysis of imaging data.
The scientific focus is complemented by services, which are provided in the context of project-related counselling as well as training offers.

Further research topics

Modern diagnostic procedures and/or measurement methods are providing ever-increasing amounts of data (e.g. individual genomic profiles, multi-modal image data, etc.). In order to utilise the information contained in these data for more efficient diagnostics and/or the development of improved therapeutic procedures, a number of steps are required. Beside data safety/integrity, also efficient data storage and data management solutions as well as the integration of different data types are in the focus of current research activities. In our group, we are for instance concerned with the development of decision support systems. The aim of our work is to support users in extracting quantitative information from (clinical) studies, analysing it in a targeted and efficient manner and testing the resulting findings in new (clinical) studies or using the findings in the context of medical/clinical decision-making.

In addition, the working group is also concerned with the use of modern media in teaching, especially in methodological training (e.g. in medical biometrics). The results of this work can also be found in the media pool section.

Projects

current projects (alphabetical order):

DMA/NCT — Different activities concerning data integration, bioinformatic analyses and model-based design of clinical trials within the Core Unit for Data Management and Analytics (NCT/DKFZ)
Mathematical modeling of Chronic Myeloid Leukaemia (CML)
Mathematical modeling of epidemiological / infectious disease-related phenomena
- Modeling of antimicrobial resistance (AMR)
- Modelling of disease burden in the common cold
Mathematical modeling of spatial organisation of haematopoietic stem cells
Statistical modeling and biometrical methods
trialGPT - an assistance system to foster the design of clinical trial

finished projects (selection):

Analysis of cell migration patterns in the zebrafish embryo (TU Dresden)
CHOICE — Characterization of the osteo-hematopoietic niche during malignant evolution (DKTK)
Control-T — Mathematical modeling of homeostasis and oncogenesis in mature T cells (DFG, Research group FOR1961)
ERANET-PLL — Implementation of (epi)genetic and metabolic networks in the targeting of T-cell prolymphocytic leukemia (BMBF, EU - Era-Net / TRANSCAN-2)
HaematoOPT — Model-based optimization and individualization of treatment strategies in hematology (BMBF, e:Med Demonstratoren)
Mathematical Modelling of Stem Cell / Niche Interactions (TU Dresden)
Machine Learning-based tumour classification in mass-spectrometry data (TU Dresden, NCT)
MEDiC —Modellstudiengang Medizin (Bund, Freistaat Sachsen)
Network-based identification of candidate genes in oligodendroglioma (TU Dresden, NCT)
prediCt — Mathematical modelling of TKI effects and immune response to predict patient-specific treatment dynamics in CML (BMBF, EU - ERACSysMed)
Statistical modelling of early engraftment kinetics after stem cell transplantation in AML (TU Dresden)
SyTASC — Systems-based Therapy of AML Stem Cells (Deutsche Krebshilfe e.V.)