In clinical practice, a wide array of examinations is conducted to evaluate the condition of a specific pathology. These range from blood sample analysis and clinical imaging (e.g., CT, MRI) to biopsy sampling, which are crucial for diagnosis and prognosis. Such medical data provide snapshots in time of the patient’s condition, as the current standard of care (SoC) does not rely on emerging real-time measurement technologies like liquid biopsies or biosensors. Additionally, clinical data encompass various biological scales, with imaging techniques like MRI offering an organ-level view of a disease (macroscopic), biopsies revealing cellular patterns at the tissue level (mesoscopic), and -omics, FACS, or molecular markers providing sub-cellular insights. However, the biophysical mechanisms governing phenomena across these scales are not fully understood. For instance, the molecular mechanisms by which a cell interprets and responds to its microenvironment, influencing macroscopic tumor progression dynamics, remain unclear.

Current clinical care faces several challenges: (C1) data collection is intermittent, sparse in time, relying on the patient’s clinical presentation; (C2) there is a lack of knowledge or certainty regarding the mechanisms that regulate data variables across different scales (structural uncertainty); and (C3) medical data are multiscale. Therefore, integrating these data to predict disease progression and recommend appropriate treatment (e.g., selecting a drug that targets proteins expressed in the tumor) is a complex task. The primary objectives of my research are to improve the SoC by:

(1) Understanding the principles of cell decision-making in multicellular systems (e.g., pathophysiological tissues, bacterial colonies, etc.).

(2) Developing methodologies that combine multiscale modeling and machine learning to facilitate data integration and enable predictions under structural uncertainty.