Christina Kossow

My research interests center around mathematical modeling in time and space to identify the essential rules that govern dynamical systems and their analysis and optimization, especially in connection with interdisciplinary work. In my diploma thesis 'Modeling and simulation of Markov processes to describe chemical reactions', I focused on stochastic processes. During my work at Boehringer Ingelheim, I applied statistical models to describe datasets from clinical studies in consideration of their hierarchical structure.

Dynamics of biochemical networks in pancreatic cancer
"I am interested in the mathematical modeling of experimental data in cancer research at multiple levels to identify key mechanisms that promote cancer progression." Pancreatic cancer (PC) is one of the leading causes of cancer deaths in the Western countries. In this project, we investigate intracellular, intercellular and extracellular processes, which promote the progression of PC, with the goal to treat tumors more efficiently. The investigation of intracellular processes offers a deeper understanding of the chemical kinetics within cells resulting in different cellular phenotypes. Each phenotype has different abilities and, thus, behaves in a different way, for example in cell communication or in interactions with its microenvironment. The investigation of such intercellular and extracellular processes then provides information on the essential mechanisms that promote cancer progression on the tissue level. Importantly, all of these processes do not only occur in time but also in space.

The central question is: How do interactions between pancreatic cancer cells and their microenvironment promote cancer progression?

To investigate interactions between PC cells and their microenvironment, biological hypotheses are translated into biochemical reaction networks. Each network is then translated into a mathematical model, which provides an independent check whether a biological hypothesis is able to reproduce the observed phenomena. The integration of space enables to check whether data patterns are described by the underlying model. The development of novel mathematical approaches is necessary to describe certain phenomena, such as the diffusion of molecules in consideration of excluded volumes leading to efficient simulations, and to analyze simulated patterns. Each mathematical model is always based on simplifications of the biological background, which presents another interesting challenge.

Experimental and theoretical systems biology are combined in this project. Thus, it offers a great chance to be part of interdisciplinary work based on interdisciplinary communication. We intend to understand the inner and outer behavior of different cell types at different levels of organization in PC progression. The investigation and analysis of these complex interactions have the potential to support new strategies in PC treatment.