Dr. Laura Helleckes: Machine learning meets biotechnology

Dr Laura Helleckes studied Molecular and Applied Biotechnology at RWTH Aachen University and completed her PhD at the Jülich Research Centre. She is now working as a postdoctoral fellow at the ‘I-X Centre for AI in Science’ at Imperial College London, where she is working on applying AI methods from chemical engineering to biotechnology.

Ms Helleckes, the research centre effectively served as a springboard for your scientific career. What led you to the Jülich Research Centre?

It was a lecture by Prof. Dr Marco Oldiges at RWTH Aachen University – where I was doing my Master’s degree at the time – that led me to the Research Centre. His research at the Institute of Biotechnology (IBG-1) at the Research Centre really stood out: in his laboratory automation group, his team uses robots to carry out experiments automatically. I was absolutely fascinated by that! After all, this approach allows you to offload many simple and repetitive tasks and focus on the actual scientific questions. I therefore seized the opportunity straight away and asked him about a three-month research placement as part of my Master’s degree. During this placement, I had the fantastic opportunity to do a lot of programming and move more firmly into the field of data science. I was hugely enthusiastic about high-throughput data analysis, so I also wrote my Master’s thesis in Marco Oldiges’s group.

What happened next?

When the opportunity arose to apply for a PhD place in Oldiges’s group, I was very happy to stay there. A fundamental question in biotechnology is: how can microorganisms, enzymes and catalysts found in nature be used to produce chemical products, pharmaceuticals and everyday items from renewable raw materials? The challenge here lies in the difficulty of selecting from the vast array of microbial ‘cell factories’ that could be used for such a process. Which one is the right one – for example, for a new enzyme in a detergent or a chemical product? What process conditions are required? It is virtually impossible to test all combinations. Experimental designs are therefore needed to find the best solution for the process. My PhD thesis had two strands: on the one hand, the development of automated experiments; on the other, the creation of algorithms that use current data to predict which experiment should ideally be carried out next.

A great success: for your doctoral thesis, you were awarded the 2024 Helmholtz Doctoral Prize in the ‘Earth and Environment’ research area.

The question at hand was: How can experiments be carried out more efficiently in order to develop bioprocesses for industry? In other words: How can we extract knowledge from high-throughput data to optimise the experiments? My work has been recognised both for its scientific excellence and for the high industrial relevance of the algorithms in accelerating these processes.

How did your research lead you to London?

Even whilst working on my PhD, I found the field of machine learning – a sub-field of artificial intelligence – extremely fascinating. At that time, however, AI was only used to a limited extent in the development of bioprocesses. The situation was quite different in chemical process engineering or in chemical processes, where AI was already being used by many research groups. Yet there are quite a few parallels. My motivation was to explore the field of chemical process engineering and apply the machine learning approaches used there to biotechnology. So I looked for groups that were particularly strong in AI and came across the I-X Centre for AI in Science at Imperial College London. Here, I hold a postdoctoral fellowship – a role that sits between a postdoc and a junior group leader, with independent funding. I’ve secured funding for this research project and am now working with the research group led by my mentor, Dr Antonio del Rio Chanona.

What is your long-term career goal?

I would very much like to remain in academia and, as a next step, apply for junior lectureships. I am currently in a transitional phase, during which I am establishing my independence in my own field of research and hope to become a professor in the long term.

In what ways do you see differences between Jülich and London when it comes to research?

At the Jülich Research Centre, as someone specialising in algorithms, I worked in an experimentally oriented laboratory automation group. Now I work in a theoretical group – there isn’t a laboratory. That’s obviously a huge difference, because the approaches are completely different. On the one hand, I enjoy being less reliant on experiments; on the other hand, it was precisely this feedback loop between myself and the experimental researchers that was particularly exciting: that’s an aspect I sometimes miss a little.

Imperial College is an internationally renowned university with an excellent team – and I’m learning an incredible amount about methodology in my research group. I’m also benefiting greatly from the international environment in London. It’s very enriching that, because of people’s different academic backgrounds, I’m faced with a variety of questions and challenges.

Do you want to return to Germany permanently, or would you prefer to stay in England?

I can imagine both. In England, it’s very easy to carry out interdisciplinary research – people tend to be a bit more daring when it comes to machine learning and AI, so it’s perhaps easier to pursue new ideas. Germany is sometimes a bit more conservative, but offers an excellent foundation, particularly in engineering, and also produces outstanding graduates by international standards, especially in bioprocess engineering. So both countries have their advantages.

What advice would you give to younger people?

Research thrives on diversity and different ways of thinking – this is particularly true of interdisciplinary research. Whilst the Jülich Research Centre is exceptionally well-positioned in terms of diversity, I would still highly recommend going elsewhere from time to time to experience something different. For instance, whilst working on my PhD: I spent half a year on a research placement in Switzerland, for example. Even though the research landscape in Germany is fantastic, it’s helpful to come back with a new perspective and fresh ideas. So: make the most of opportunities to go abroad, and bring your experiences back to the Research Centre!