Jana Wolf

Cellular signaling networks generate dynamic responses that regulate how cells respond to external stimuli, perturbations or drugs. These responses can result in changes of cell fate, for examples, transitions from apoptosis to survival or from quiescence to proliferation. Mathematical modeling combined with targeted experimentation have successfully elucidated the underlying mechanisms and functional consequences for individual pathways, e.g. the role of feedbacks and cross-talk. The models and insights are now being used to create large-scale mechanism-informed models that can integrate various data sets and drive the development of disease- and patient-specific models. Here I introduce an approach to model aggressive B cell lymphoma based on detailed network model capturing key cellular processes and the mapping of perturbations data of a cohort of lymphoma patients. The resulting personalized patient models capture patient heterogeneity and are used to analyze how the genetic alterations together shape cancer cell states. A systematic study of individual and combinatorial alterations identifies known and so-far-unknown cooperation effects and elucidates a strong context dependency of individual network alterations.