Speaker
Description
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 or 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 around 300 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.
Bibliography
@article{Konrath_Denker_Schmitt_Chapuy_Wolf_2026, title={Patient-specific B-cell lymphoma modeling identifies cooperating genetic alterations and the critical influence of patient context}, rights={https://creativecommons.org/licenses/by/4.0/}, url={https://www.researchsquare.com/article/rs-9022766/v1}, DOI={10.21203/rs.3.rs-9022766/v1}, abstractNote={Abstract Diffuse large B-cell lymphoma (DLBCL) is a molecularly heterogeneous disease with high genetic complexity and interpatient variability. Sequencing studies of representative patient cohorts have identified a comprehensive set of genetic driver alterations, enabling patient stratification for personalized treatment strategies. To date, it remains insufficiently understood how these ncogenic driver alterations operate in concert and shape malignant cell states. Here, we use a computational approach that embeds patient data and experimentally characterized molecular perturbations in mechanism-based mathematical modeling to study the effect of genetic alterations in a network context. Based on a detailed pathway model capturing key cellular processes, including apoptosis, cell division, and B-cell differentiation, we created personalized models for a cohort of 284 patients, of which 90.5% reflect an aberrant cell state. Systematic assessment of the functional effects of individual and combinatorial alterations within these models identified previously not appreciated cooperating alterations that operate in synergy, such as mutated NFKBIE and BCL6 structural variant. Notably, we identify a strong context dependency of functional effects, as identical alterations exert varying effects in different patient models. Incorporation of the network context is therefore essential for understanding DLBCL heterogeneity and selecting therapeutic targets for personalized and more efficient treatment strategies.}, author={Konrath, Fabian and Denker, Sophy and Schmitt, Clemens and Chapuy, Björn and Wolf, Jana}, year={2026}, month=mar }
@article{Simon_Konrath_Wolf_2024, title={From regulation of cell fate decisions towards patient-specific treatments, insights from mechanistic models of signalling pathways}, volume={39}, ISSN={24523100}, url={https://linkinghub.elsevier.com/retrieve/pii/S2452310024000295}, DOI={10.1016/j.coisb.2024.100533}, journal={Current Opinion in Systems Biology}, author={Simon, Mareike and Konrath, Fabian and Wolf, Jana}, year={2024}, month=dec, pages={100533}, language={en} }
@article{Thobe_Konrath_Chapuy_Wolf_2021, title={Patient-Specific Modeling of Diffuse Large B-Cell Lymphoma}, volume={9}, ISSN={2227-9059}, url={https://www.mdpi.com/2227-9059/9/11/1655}, DOI={10.3390/biomedicines9111655}, abstractNote={Personalized medicine aims to tailor treatment to patients based on their individual genetic or molecular background. Especially in diseases with a large molecular heterogeneity, such as diffuse large B-cell lymphoma (DLBCL), personalized medicine has the potential to improve outcome and/or to reduce resistance towards treatment. However, integration of patient-specific information into a computational model is challenging and has not been achieved for DLBCL. Here, we developed a computational model describing signaling pathways and expression of critical germinal center markers. The model integrates the regulatory mechanism of the signaling and gene expression network and covers more than 50 components, many carrying genetic lesions common in DLBCL. Using clinical and genomic data of 164 primary DLBCL patients, we implemented mutations, structural variants and copy number alterations as perturbations in the model using the CoLoMoTo notebook. Leveraging patient-specific genotypes and simulation of the expression of marker genes in specific germinal center conditions allows us to predict the consequence of the modeled pathways for each patient. Finally, besides modeling how genetic perturbations alter physiological signaling, we also predicted for each patient model the effect of rational inhibitors, such as Ibrutinib, that are currently discussed as possible DLBCL treatments, showing patient-dependent variations in effectiveness and synergies.}, number={11}, journal={Biomedicines}, author={Thobe, Kirsten and Konrath, Fabian and Chapuy, Björn and Wolf, Jana}, year={2021}, month=nov, pages={1655}, language={en} }
