Luxembourg National Research Fund (FNR)

collaboration with partners in China and Argentina

The aim of this project is to detect and predict heart arrhythmias before they occur. Heart attacks and arrhythmias typically happen without noticeable warning. Predicting heart arrhythmias, such as atrial fibrillation (AF), in real-time and with enough anticipation can allow patients to seek immediate medical attention and prevent health complications. This project searches for subtle changes in heart dynamics, captured via ECG or PPG (via a smartwatch, for example), using state-of-the-art machine learning tools.

Luxembourg National Research Fund (FNR)

Department of Plant Sciences at the University of Cambridge and the Institute of Biology Leiden

The aim of these projects is to understand how networks of genes and other molecules causally interact over time to produce disease related phenotypic behaviours. Reliable models of these regulatory networks allow us to simulate hypotheses that can be used to guide experimental testing, and to identify changes in biological network structures that occur when a system is perturbed in responses to stimulation, pharmacological intervention or genetic mutations. Networks are inferred from time-series or single-cell data. The project contributes to broad theoretical research into mathematical, algorithmic and software tools to help understand causal dynamic network structures and interactions between different omics layers, such as metabolomics and transcriptomics. The developed tools are applied to understand the mechanisms of action of several organisms and time scales such as circadian clocks and the immune system.

Luxembourg National Research Fund (FNR) and JPND Research Joint EU Programme

Centre Hospitalier de Luxembourg (CHL), the University of Oxford, and partners from the Netherlands, Germany and Sweden.

Deep Brain Stimulation (DBS) is a surgical therapy for several movement disorders (e.g. Parkinson’s disease (PD) and essential tremor) and psychiatric diseases. During this procedure, an electrode is implanted into the brain, constantly delivering electrical pulses to specific regions of the brain. This project aims to considerably improve the efficiency of DBS while reducing side effects. In particular, it develops algorithms to deliver personalised stimulations that adapt to the patient’s needs, and computational tools to aid physicians initialize DBS parameters.

Luxembourg National Research Fund (FNR)

Catastrophic events (i.e. sudden changes that affect systems stability) occur in various fields and at various levels. Examples include earthquakes, stock market crashes and population extinction. It is hypothesised that the onset of diseases such as cancer follows similar patterns. If we could understand the critical transitions (CTs) that induce catastrophes, we would be better equipped to prevent them arising or at least to mitigate their effects. The proposed research confronts this problem within a range of disciplines in the areas of clinical science, immunology, biology and finance. It aims to classify CTs according to their general dynamical features and then to provide the foundations for:

a)Identifying early warning signals to enable more timely, reliable predictions of catastrophes.
b)Developing tools to model, analyse, and detect CTs in diverse areas of application.

Ultimately, the overall goal of the project is two-fold: to support more advanced research on CTs within different scientific disciplines and to improve society’s ability to anticipate critical transitions to undesirable states in multiple fields

The project entails eleven doctoral students and ten supervisors.

Luxembourg National Research Fund (FNR)

collaboration with the Luxembourg School of Finance

The project aims to study hidden statistical patterns in financial systems to test potential early warning signals for critical transitions in equity markets. Financial markets are known for their complexity due to the presence of many different types of agents whose actions belong to a very diverse set of strategies. This project studies the causal effect of latency delays on occurrence of mini-flash crashes. This latency delay slows down the trading while reducing price impact, and results in decreased number of mini-flash crashes.

2020-2023

The spreading of the COVID-19 pandemic within a community changes over time. Since March 2020, our dynamical models have been assessing its severity, anticipating future evolutions and providing information to aid the Luxembourgish Government in decision making. We explored a range of models, from simple epidemiological models to detailed individual agent-based models to simulate the effects of different confinement and deconfinement measures and vaccination policies, among others. Models included forecasts of hospital, ICU and deaths and the impact on the local economy. A quantitative model estimated the infected population from viral samples collected in wastewater, providing an alternative for an efficient and robust epidemic tracking. Finally, using critical transitions methodologies, we developed models to anticipate new waves of infections.

2022-

Jesús Fuentes, Maria Moscardó Garcia

Morphogenesis – the process that governs the formation and development of biological shapes and structures – is a central topic in developmental biology. Traditional models in this area often have limitations; for example, they might not adequately capture the dynamic interactions between cells or the influence of underlying tissue structures.

Our research framework addresses these challenges by offering a more integrated approach. It accounts for both mechanical and geometric forces, as well as stochastic events and cellular activities like cell division and death. By considering these factors, our models can simulate interactions between cells in a way that reflects the inherent complexity and variability found in actual biological systems.

Recently, we have introduced a model that incorporates data from both synthetic models and actual biological samples. This makes it a valuable tool for a wide range of applications: from understanding normal physiological development to exploring pathological scenarios. Our future work involves the application of these models for hypothesis generation in therapeutic research, particularly in examining complex tissue structures.

Figure: Morphogenetic Simulations of Synthetic Foil-Knot Structures

• Panel a: Originating from an initial configuration of 100 hypothetical cells arranged spherically, the model drives the cells to a foil-knot structure characterised by a single twist.
• Panel b: By extending the symmetrical properties inherent in the structure from Panel a, the model produces a two-foil-knot configuration, thereby demonstrating its capacity to adapt to enhanced topological complexity.
• Panel c: Upon distorting the initial foil-knot configuration and inducing cellular division up to a final cell count of 450, the model maintains stability, contingent on the uniformity of cell-to-cell adhesion.
• Panel d: This panel illustrates the destabilisation that occurs in the tissue configuration from Panel c when the elastic properties of the cellular ensemble are varied.

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Dynamic Simulation of Foil-Knot Morphogenesis: This video captures the simulated evolution of a structure analogous to that depicted in Panel b of the figure. Commencing from a spherical spatial distribution, the cellular ensemble undergoes transformation to form a hypothetical foil-knot tissue.

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