In clinical practice, a plethora of examinations is conducted to assess the state of a certain pathology. These span from blood sample analysis, clinical imaging (e.g. CT, MRI) and biopsy sampling are among the most important diagnostic and prognostic tools. Such medical data correspond to snapshots in time of the patient’s state, since current standard of care (SoC) is not based on emergent technologies of real-time measurements, such as liquid biopsies or biosensors. Moreover, clinical data refer to different biological scales (Fig. 1), since imaging, such as MRI, typically provides a organ level picture of a disease (macroscopic), biopsies represent cellular patterns at a tissue (mesoscopic) level and -omics, FACS or molecular markers allow for sub-cellular insights. Finally, the biophysical mechanisms that regulate phenomena in all these scales are not completely known. For instance, we still do not fully understand the molecular mechanisms through which a cell encodes and decodes its microenvironmental information into phenotypic decisions and how these decisions impact the macroscopic tumour progression dynamics.
Therefore, current clinical care faces the following challenges: (C1) data collection is sparse in time since it relies on patient’s clinical presentation, (C2) we lack the knowledge/uncertainty of the mechanisms involved in regulating these data variables across different scales (structural uncertainty), and (C3) medical data are multiscale. Therefore, integrating these data to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable task. The overall goals of my research is improving the SoC by:
(1) Understanding the principles of cell decision-making in multicellular systems (e.g. pathophysiological tissues, bacterial colonies etc)
(2) Developing a methodologies combining multiscale modelling and machine learning that allows for data integration and enable predictions under structural uncertainty.