Understanding how local crowding regulates progression through the cell cycle is central to explaining tissue growth and contact inhibition. We develop a novel model of density-dependent cell-cycle progression using Universal Differential Equations (UDEs), in which transition rates between stages of the cell-cycle are represented by neural networks. This approach preserves key mechanistic...
I will discuss how to use Bayesian inference approaches to obtain models for biological data in different contexts. In particular, I will discuss two examples: First, I will describe how we can use inference approaches to obtain the underlying differential equations that govern a specific biological process and apply it to the case of bacterial growth. Second, I will describe how we can use...
A common inverse problem in systems biology, biophysics and related disciplines is the inference of model parameters from limited measured data. For cases where the underlying dynamics follow a set of known (or assumed) differential equations, Physics-Informed Neural Networks (PINNs) have been put forward as decent approximators of the inverse equations, offering an alternative way to estimate...
Physics-Informed Neural Networks (PINNs) have developed into a flexible framework for embedding mechanistic knowledge within data-driven models. Our work provides two complementary studies using PC9 lung cancer cell microscopy data.
First, we apply a Biologically-Informed Neural Network (BINN) to spatiotemporal (2D+t) data under a reaction–diffusion model, where diffusion and growth are...
Understanding biological systems requires models that are both grounded in empirical observations and mechanistically interpretable. Recent advances in equation learning provide powerful new tools to infer governing equations directly from biological data, bridging modern machine learning with classical mathematical biology. This minisymposium brings together leading and emerging researchers...
