ICAI interview with Renger Jellema: Deploying AI to develop sustainable food processes to feed the world
The modern world is facing a number of converging megatrends: population growth, increasing scarcity of natural resources, and a need for the sustainable production of nutritious food. Through biotechnology DSM develops sustainable products, using nature’s toolbox, such as microorganisms. The AI Lab for Bioscience (AI4b.io) aims to accelerate this innovation process using AI technology. Renger Jellema: ‘More time to use our human creativity is going to be the most important thing we will gain from AI.’
Renger Jellema is program manager of AI4b.io, the ICAI AI Lab for Bioscience, and he is Senior Data Scientist at the Biodata & Translation group at DSM Science & Innovation.
The AI Lab for Bioscience is a collaboration between Delft University of Technology and DSM.
The lab’s first press release stated that you are the first lab in Europe to apply AI to life science and bioproduction. Why hasn’t this been done before?
‘Engineers have already been applying mathematical models in life science for decades, but now there are rapid developments in computing power and breakthroughs in AI. The combination of methods and techniques has become a unique playing field to take biotechnology, process technology, food science and even health and nutrition to the next level.’
Is this approach being used now by other researchers as well?
‘Yes, more and more biotech scientists and engineers worldwide are now launching initiatives similar to what we are doing. What is unique about AI4b.io is that we scale down, from cubic meters to nanoliters and from months to milliseconds, and not follow the more common reverse order which brings scale-up issues. We have defined five lines of research: starting with scheduling in factories, to unit operations, to automated labs, to microbial strain developments and screening to microbial cultures and health relationships in the gut.’
What can AI mean for bioscience?
‘Developments can go much faster. Because a lot of the patterns in data is already stored in the AI-models, researchers can directly go to the core of the problem. And then there will be more time for the researchers to be creative. That’s also how I explain it to colleagues who are a bit hesitant: we free up time to interpret results and come up with novel ideas. Right now about 80 percent of our time goes into managing data and doing things repeatedly.’
What kind of questions can you try to answer with the help of AI that you couldn’t answer before?
‘We want to reduce the cost of innovation while accelerating our development cycles. Mathematical models already play a good role in reducing experimental work by calculating possible scenarios in advance. What we expect is that with the help of AI we can develop better models, leading to so-called Digital Twins of microbes, processes, and factories. At DSM, for example, we produce food and feed ingredients using the process of fermentation. We grow microorganisms on sustainable, plant-derived sources such as sugar and carbohydrates. The microorganisms convert the sugar into valuable products in large steel vessels. Using advanced simulation models, we can then predict the behavior of microorganisms and interaction with their environment in such large vessels. Based on that, we can optimize these processes to become more energy efficient and produce fewer by-products.’
Can you give an example of a typical application?
‘We have developed advanced process models that can be used for large-scale fermentation vessels with a scale of 100 m3 and above. The problem with this is that calculating a few minutes of the behavior of such a vessel quickly takes a few days of computational time on a multi-core computing platform. This makes it impossible to track or monitor the process in real time. For this application, AI can be trained to represent these models – easily speeding up the calculations by a factor of 100 – acting as Digital Twins of the real fermentation vessel. The Digital Twin becomes a sophisticated digital copy of the real process.’
What can this research eventually mean to the world?
‘At DSM, we develop novel ways to produce healthy nutritional ingredients to feed the world in a more sustainable way. The Digital Twins I mentioned before, help us in the development of such processes and products, working for example toward meat alternatives using plant-based material. We combine different protein materials with ingredients such as vitamins and other micronutrients to create food solutions that taste good, have an appealing texture and keep you healthy.’
We have just set up a Launch Pad program to coach PhD students entering the job market. You have been working as a researcher in industry for quite some time. What advice would you give them?
‘Connect with scientists in companies that inspire you. If you get a chance to present your work at a company, seize that opportunity. It’s easy to shy away and stay behind your computer. But know that companies are interested in your research and are willing to help you further. Also, exploring how your research findings can be applied in practice, will improve your thought process.
Personally, I did my PhD in collaboration with Hoogovens, the steel giant now called Tata Steel. I could have stayed behind my computer and emailed them regularly to pick up the samples I needed for my modelling activities. But often I chose to visit the plant and talk to the operators who had to collect the samples. There I saw how difficult it was to take those industrial samples from the extremely hot processes and I learned to understand why the samples were sometimes not that good. As a result, I was able to change the procedures to improve my research. You have to get your hands dirty to get the best insights.’
On February 17, 2022, the AI Lab for Bioscience will talk about their current work during the lunch Meetup of ‘ICAI: The Labs’ on AI for Food in the Netherlands. Want to join? Sign up!