Aurélie Carlier bridges biology and AI at Maastricht University’s Faculty of Science and Engineering
After more than a decade at MERLN, the FHML institute where she developed mechanistic models for biological systems, Aurélie sought an environment better aligned with her ambition to integrate AI into her research. “At MERLN, I worked in an experimental setting, but I missed colleagues to discuss methodology and exchange new ideas,” she explains. A vacancy at the Maastricht Centre for Systems Biology and Bioinformatics prompted her move to the Faculty of Science and Engineering (FSE). She is now making the transition alongside three colleagues from her group.
Building a bridge between AI and biology
In her new role, Aurélie not only advances her own research but also takes on the challenge of connecting research groups applying AI in biology. “Whether in cardiovascular or neuroscience research, or in biological studies related to agriculture and nutrition in Venlo, many researchers want to use similar AI methods but aren’t sure how. I want to create an ecosystem where people connect, share knowledge, and collaborate. We might even organise an annual event to strengthen those links.” Her new environment at FSE gives her the freedom to fulfil this connecting role. “Everything here is more open and accessible, which makes it easier to bring new ideas to life.”
Modelling biological systems
Aurélie’s own research focuses on building biological models. She compares her work to weather forecasting: just as meteorologists use mathematical equations to predict the weather, she uses mathematical models to describe how cells or tissues function or how drugs are processed in the body. The ultimate goal? Creating a digital twin to simulate biological processes. “Suppose you increase a drug dosage in your model, what happens? Or what if a patient has a specific condition? We can test different scenarios without needing a lab or patients,” she says.
Developing weather models is relatively straightforward compared to biological ones. Meteorologists often rely on a limited set of parameters: wind speed and direction, temperature, air pressure, and humidity. Biological systems, however, are infinitely more complex, involving thousands of parameters related to genes, proteins, cells, and environmental factors that interact in countless ways.
Aurélie’s team patiently and precisely identifies the most relevant parameters for each model. “You must carefully formulate all the mathematical equations. Finding them is a huge amount of work, but once you have them, you can refine your model and compare it with experimental data. So far, we’ve mostly built small models because of the detail required. But with AI, we aim to scale them up to describe more complex processes.”
Faster, but reliable, with AI
One of her projects focuses on automatically extracting parameters from scientific data. “For example, we use AI to analyse research papers and extract relevant information to improve our models. But we don’t want a black box. Our AI must reflect reality so we can trust the answers.” In her work on reliable AI, Aurélie collaborates with other AI groups, such as those in the Department of Advanced Computing Sciences.
A fresh start
Aurélie isn’t arriving alone, she’s bringing three team members with her: PhD candidates Yağmur Doğay and Laurence Nickel, and postdoc Leyla Noroozbabaee are joining her. Laurence Nickel was pleasantly surprised when he heard about Aurélie’s new role: “I am really happy for her. It sounds like a wonderful new step, and I am pleased to hear she will be working within FSE.” In the end, he as well made the move to MaCSBio: “My first impression is very positive. Since I studied at FSE myself, I was already familiar with the faculty and knew some people, that made the transition to MaCSBio easier for me. I was welcomed so openly that I quickly felt at home.”
Aurélie agrees: “I also feel at home here. Everyone is open, and people introduce themselves spontaneously. FSE is a small, dynamic faculty where there’s room for initiative, and that’s exactly what we need to push the boundaries of research.”
Text: Patrick Marx