Speaker
Description
To inform decisions rather than replicating noise, low-dimensional and identifiable models calibrated with multiscale data can serve as reliable tools through separating host-level constraints from lesion-level properties.
As an example for a simple, yet structured approach, we present a mechanistic model formulating systemic cancer dynamics that links primary and metastatic growth through a shared carrying capacity. Building on Gompertzian growth, lesions are coupled via a host-level constraint on total tumor burden and extend the same idea to a metastatic size-distribution framework, enabling joint calibration to longitudinal primary-tumor measurements and metastatic nodule data. Model selection identifies the most parsimonious formulation that captures cross-lesion interdependence from more classic alternatives. Identifiability analysis shows that key drivers can be practically constrained from limited datasets, supporting robust inference with few parameters. Counterfactual simulations result in competitive metastatic release after primary tumor resection, generating testable hypotheses for prospective experiments. Beyond oncology, the work illustrates a general theoretical-biology principle: simple, yet structured models can denoise multiscale data by separating host- from lesion-level traits, yielding computational biomarkers and efficiently informing experimental designs across interacting population systems.
