Model-based Data Analysis in the Life Sciences

15.01.2020, 14:00  –  Campus Golm, Haus 9, Raum 2.22
Institutskolloquium

Jan Hasenauer (Universität Bonn), Sebastian Sager (Universität Magdeburg)

14:00 Jan Hasenauer (Universität Bonn) On the mathematical modelling of biochemical reactions networks

15:00 Tea and Coffee Break

15:30 Sebastian Sager (Otto-von-Guericke Universität Magdeburg)

Abstracts:

Jan Hasenauer (Universität Bonn): On the mathematical modelling of biochemical reactions networks

Experimental approaches such as single-cell and bulk sequencing, mass spectrometry and imaging provide nowadays an immense amount of quantitative data. This renders data analysis, including integration and interpretation, more relevant than ever before. However, data analysis is often computationally intensive and many approaches are not scalable. In this talk, I will provide an introduction to model-based data analysis. I thin focus on the modelling of biochemical reaction networks using ordinary differential equation and parameter estimation. Furthermore, I will present some of our recent work: (1) methods for the analysis of multi-omics data from cancer cell lines using large-scale cancer pathway models; and (2) approaches for the interpretation of single-cell imaging and sequencing data using population models. In these application areas, I will outline (reusable) methodological advancement which significantly improved the scalability and applicability of approaches.

Sebastian Sager (Otto-von-Guericke Universität Magdeburg): Mathematical Optimization for Clinical Diagnosis and Decision Support

Physicians need to make many important decisions per day. One clinical example is the scheduling and dosage of chemotherapy treatments. A second example is the discrimination of atrial fibrillation from atypical atrial flutter, based on ECG data. Such important and complex decisions are usually based on expert knowledge, accumulated throughout the life of a physician and shaped by subjective (and sometimes unconscious) experience. It is not readily transferable and may be unavailable in rural areas. At the same time, the available imaging, laboratory, and basic clinical data is abundant and waits to be used. This data is not yet systematically integrated and often single data-points are used to make therapy decisions. We concentrate on some applications in oncology and cardiology and highlight the role of mathematical optimization in the modeling as well as in the decision making process, resulting in a patient- and circumstance-specific personalized medicine.