Data-driven model discovery has become a powerful approach for identifying governing equations of dynamical systems using temporal data. The Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, initially developed for ordinary differential equations (ODEs) \cite{bpk16}, has been extended to more general classes of problems, recently including also deterministic and stochastic delay...
The automated discovery of dynamical digital twins from time series data, known as model learning, is a central challenge in systems biology, particularly in the presence of noise, partial observability, and limited data availability.
We recently conducted a comprehensive review of available methods for data-driven discovery of dynamical systems, and identified 117 algorithms based on...
Mechanistic ordinary differential equation models are central to systems biology, pharmacology and emerging dynamical digital twins, but they are still commonly built by hand through extensive literature review, manual specification of reaction mechanisms, and repeated parameter fitting. As biological datasets become larger and more diverse, this process becomes difficult to scale, motivating...
Global modelling such as GPoM \cite{Mangiarotti2012} is a tool for constructing systems of polynomial differential equations from time series. We present one use of this tool for variable selection. We study the bloom of the toxic algae (\textit{Ostreopsis} cf. \textit{ovata}) \cite{FabriRuiz2024} and look for strong relationships between the alga and its environment (from COPERNICUS datasets)...
Dynamical digital twins stand out as powerful mechanistic tools to advance biomedical challenges. However, their design involves the integration of numerous multimodal time-resolved datasets calling for a change of practice, from manual design to automated discovery. The development of data-driven model learning approaches is constrained by the realities of biological data: scarcity and...
