Mechanistic ordinary differential equation (ODE) models are a powerful tool to study dynamic biological systems. However, their predictive power is constrained by gaps, biases, and inconsistencies in the literature. They typically also require quantitative time-lapse data for training, which is time-consuming to collect. At the same time, machine-learning approaches can capture complex patterns from data, but they are often harder to interpret and typically require large training datasets. Hybrid scientific machine learning (SciML) models offer a promising way to combine the strengths of both approaches by integrating mechanistic models with flexible data-driven modules. 
Despite this promise, the use of SciML in biology remains limited by insufficient infrastructure. Dedicated software is needed because coding end-to-end differentiable workflows for gradient-based training of hybrid models is technically challenging. In addition, model exchange is hindered by the lack of a standardized, reproducible format for specifying SciML training problems, analogous to the PEtab standard for ODE models. To address these challenges, we developed PEtab-SciML, an extension of the PEtab format, and implemented support for it in the state-of-the-art modeling toolboxes PEtab.jl and AMICI. In this seminar, I will introduce the PEtab-SciML format. Using real-data examples, I will show how PEtab-SciML enables the integration of diverse data modalities into dynamic model training; such as learning the kinetic parameters of an ODE model from omics and protein sequence data. I will also show how it supports machine-learning-based black-boxing of complex model components, such as quarantine strength in an SIR model. Finally, I will show how PEtab-SciML enables the use of efficient training strategies, such as curriculum learning, that make SciML models easier to train and apply in practice. 

Many cells exhibit exponential growth not only at the population level but also at the single-cell level. However, single-cell growth rates fluctuate over time. We distinguish between two conceptually distinct sources of growth rate fluctuations: intrinsic continuous fluctuations resulting from intracellular processes, and fluctuations that originate at division events, which we refer to as kicks. We use a simple model to describe single-cell growth and identify the signatures of continuous noise and division kicks. To infer the true biological behavior reliably from experiments, it is crucial to account for measurement noise. We derive analytical expressions for the statistics of meaningful observables, accounting for continuous fluctuations, division kicks, and measurement noise. Importantly, we find that ignoring measurement noise can lead to incorrect biological conclusions. Our results provide insights into how different sources of growth rate variability and measurement errors influence observed cell size dynamics, offering an interpretable framework for analyzing experimental data in cellular biology. 

Heterogeneity is a fundamental feature of biological systems. Oncology is one of the fields in which this feature is most evident, as its key players are characterised by mutability, plasticity, and often “uncontrolled” dynamics. Whether heterogeneity arises from spatial structure, environmental variability, or cellular traits, effective therapeutic strategies must explicitly account for it in order to eradicate or control tumours.

From a modern perspective, this requires balancing the hit-hard / keep-it-sensitive trade-off, while also considering not only medical but also broader patient-related side effects of treatments. Contemporary medicine is increasingly exploring ways to exploit the very characteristics that have historically made cancer so dangerous, turning them into potential advantages for therapy.

The multiscale nature of tumour systems, together with the need to predict the combined effects of multiple, non-parallelisable processes, makes the development of optimised mathematical tools particularly compelling. Such tools can address questions that are both scientifically challenging and highly relevant from a clinical and humanitarian perspective.

In this seminar, we will analyse tumour masses from a structured population perspective, focusing on the role of heterogeneity in shaping therapeutic strategies. We will first discuss how heterogeneity in phenotypic composition and nutrient distribution influences the eco-evolutionary dynamics of tumour growth. We will then consider more specifically its impact on radiotherapy.

In particular, we will highlight the advantages of mathematically rigorous modelling in bridging theory and biology. We will also adopt a more exploratory perspective, using these models to illustrate how mathematics can serve as a potential decision-support tool for the selection and optimisation of treatment protocols, within an image- and model-driven framework.

The final part of the seminar will focus on potential future developments, with the aim of fostering an open and collaborative discussion on novel perspectives to improve understanding, prediction, and therapeutic optimisation.

Cells make decisions across developmental biology, immunology, and synthetic biology. These processes are typically described using systems of ordinary differential equations, where mathematical analysis focuses on steady-state solutions. However, understanding how the timing of cell decisions is controlled requires moving beyond this paradigm. In this talk, I will discuss two complementary molecular mechanisms for controlling dynamical speed. First, I will show how timing can be regulated through critical slowing down, and how combining different bifurcations can generate emergent temporal behaviours even in small gene regulatory networks. Secondly, I will address developmental tempo, where embryos from different species execute remarkably similar genetic programmes at different speeds. I will present a mathematical framework based on orbit invariance that allows us to explore potential molecular mechanisms underlying species-specific differences in developmental timing.

Prostate cancer progression and therapeutic resistance present significant clinical challenges, particularly in the transition to castration-resistant disease. Although androgen deprivation therapy and second-generation drugs have improved patient outcomes, resistance frequently develops, reflecting tumour heterogeneity and the influence of its microenvironment. This talk presents two interdisciplinary studies that address these issues through data-driven mathematical approaches. We show how integrating experimental data with mathematical and statistical modelling can improve our understanding of prostate cancer dynamics and inform more effective, context-specific therapeutic strategies. The first study examines drug resistance and tumour evolution under treatment. We develop a multi-scale hybrid modelling framework to capture processes occurring across different temporal scales. Partial differential equations describe the behaviour of drugs and other chemicals in the tumour microenvironment (over the ‘fast’ timescale), while a cellular automaton captures the dynamics of tumour cells (over the ‘slow’ timescale). Through computational analysis of the model solutions, we examine the spatial dynamics of tumour cells, assess the efficacy of different drug therapies in inhibiting prostate cancer growth, and identify effective drug combinations and treatment schedules to limit tumour progression and prevent metastasis. The second study focuses on the role of host–microbiome interactions in obesity-associated prostate cancer. Using data from experiments with the TRAMP mouse model, we apply statistical and machine learning methods, including generalised linear models, Granger causality, and support vector regression, to characterise microbial dynamics and their responses to treatment. These findings are incorporated into a dynamical systems framework that captures microbiome–tumour co-evolution under therapeutic and dietary perturbations, providing insight into how dietary fat and combination therapies involving enzalutamide and phytocannabinoids influence tumour progression and gut microbiota composition.

Reaction systems are continuos-time dynamical systems with polynomial right-hand side, and are very common in biochemistry, cell signaling, population dynamics, and many other biological applications. We discuss global stability (i.e., the existence of a globally attracting point) and persistence (i.e., robust absence of extinction) for large classes of reaction systems. In particular, we describe recent progress on the proof of the Global Attractor Conjecture (which says that vertex-balanced reaction systems are globally stable) and the Persistence Conjecture (which says that weakly-reversible reaction systems are persistent), and how these results can be extended outside their classical setting using the notion of “disguised reaction systems". We will also discuss analogous results for the case where reaction systems are replaced by generalized Lotka-Volterra systems of arbitrary degree.