Andrzej Bednarek

Health sciences, Medical sciences, Pharmacology and pharmacy

Department of Molecular Cancerogenesis

Faculty of Medicine

Modern biomedicine faces the challenge of integrating vast Big Data sets from various levels of cellular organization. Neoplastic and metabolic diseases (such as type 2 diabetes) are characterized by high complexity and intricate interconnections at the level of signaling and metabolic pathways. A significant research problem is understanding how metabolic heterogeneity and intracellular communication disruptions influence cell differentiation and disease progression. A specific reference point in this research is the role of multifunctional proteins, such as the tumor suppressor and metabolic regulator WWOX, whose analysis allows for investigating the mechanisms linking oncogenesis with metabolic homeostasis disorders. Since classical statistics often fail to capture the non-linear nature of biological processes, the application of advanced mathematical frameworks and machine learning is essential. Projects carried out at the Department: The primary goal of the projects is to develop an integrated trans-omic model explaining the dynamics of signaling and metabolic pathways involved in the development of selected cancers and type 2 diabetes. Specific objectives include: Identifying universal and disease-specific markers of progression, molecular subtypes, and treatment sensitivity. Elucidating the impact of metabolic alterations on cellular differentiation processes using a systems medicine approach. Functionally analyzing the role of the WWOX gene and protein as a key node integrating pro-oncogenic and metabolic signals. Constructing a mechanistic model to simulate the biological system's response to external and therapeutic stimuli. Research Methodology: The projects are based on a strategy combining advanced computational analysis with experimental verification: In silico Analysis (Large-scale): Exploration and integration of public databases (TCGA, GEO, ProteomeXchange, metabolomics) using R and Python. Mathematical Modeling: Utilizing machine learning (neural networks) for data classification and marker prediction. Employing non-linear algebra to describe process dynamics and fuzzy logic to model data uncertainty and complex regulatory dependencies. Experimental Research: Utilizing the team's proprietary data and conducting targeted in vitro/ex vivo experiments to validate computational findings (e.g., expression change studies, metabolic profiling following WWOX gene modification). Synthesis: Constructing the final mechanistic model based on the obtained results.

trans-omics, systems biology, machine learning, fuzzy logic, non-linear algebra, cancer, type 2 diabetes, WWOX gene, biomarkers, mechanistic modeling