Understanding the regulatory mechanisms governing tumour-immune interactions is crucial for advancing targeted therapies in cancer. In this seminar, I will present my research on the mathematical modelling of the tumour microenvironment, with a focus on data-driven model inference. Using systems biology approaches, I have developed multi-scale models to investigate regulatory interactions at molecular and cellular levels across different temporal scales. More recently, I have integrated data-driven methodologies, leveraging patient-derived longitudinal data and machine learning techniques to infer the context-specificity of these interactions.More specifically, I will discuss the regulatory interactions underlying Chronic Lymphocytic Leukaemia (CLL) by integrating time-series RNA sequencing with data-driven GRN inference, thus capturing temporal regulatory interactions and highlighting patient-specific regulatory mechanisms. Performing network analysis, I have identified distinct gene modules, revealing critical pathways influenced by immune interactions, such as cytokine signalling and metabolic reprogramming. These findings emphasise the role of patient heterogeneity in shaping regulatory networks, underscoring the importance of personalised approaches in cancer research. In addition, the computational framework applied in this study offers a workflow of integrating time-series transcriptomics with GRN inference to uncover context-specific regulatory mechanisms. I will discuss how these approaches contribute to a better understanding of disease progression and drug response and highlight the challenges and future directions in bridging mechanistic modelling with high-throughput data analysis.
Paper: https://www.biorxiv.org/content/10.1101/2025.04.20.649300v1.full
Paper code: https://github.com/VeraPancaldiLab/CLL_GRN_paper
YouTube and Slides.