TP2 - SASKit
Unveiling functional features of senescence, pancreatic cancer and stroke and their comorbidity through ODE-based and Boolean modeling
In systems biology, networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information. Dynamical models including ordinary differential equations (ODEs) and Boolean models are well established tools to understand non-linear behavior of networks, which allows to unravel disease mechanisms. Dynamical models will be developed, expanded from public curated data with a focus on stroke and pancreatic cancer in the context of cellular senescence. Models will be simulated and correlated with patient, animal and in-vitro data. Within the end of the first year, experimental data from subproject 3 and subproject 6 will be integrated. Given their role in PDAC, stroke and senescence, models will include specifically PAI-1, CDK5 and p16/p21. Next, we will expand and parameterize these models using public databases and include causal interactions among known markers with other potential candidates proposed in the literature, or highlighted from meta-analyses of publically available datasets. We will also perform steady-state analysis and metabolic control theory to obtain sensitivities that can be used for biomarker selection. We will employ a novel method that we developed to identify disease-phenotype specific core-regulatory networks. Boolean models (BMs) are more tractable analytically, scalable and do not require detailed kinetic parameters compared to ODE models and they have successfully been used in biomedical research. We will encode the core network into BM and calibrate with data from subproject 3 and subproject 6. The different work packages all provide different approaches to the problem of biomarker ranking via sensitivity analysis, contributing to the efforts of subproject 1 and subproject 2 to generate an optimal list of biomarkers for the “biomarker + software” toolkit. The most promising biomarkers that emerge from this effort will then be measured by our clinical and experimental partners in greater detail and with higher precision. Finally, the software/app for the biomarker kit will be developed in close cooperation with subproject 1.