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Improving biotech production with real-time testing

Marieke Klijn detects changes in biotechnological processes in real-time, with the use of new monitoring techniques. She wants to move biotechnological research away from old-fashioned manual sampling to much more efficient continuous testing: “We can now know at once how many cells are alive during the production process, or how fast they are eating glucose, for example – all without the need to take a sample.” “My research focusses on ways to optimize production processes for biotech companies, by developing methods for continuous testing,” Marieke Klijn, assistant professor at the biotechnology department of the TU Delft, begins. “My team aims to make measurements an automatic part of production processes in biotechnology. I want to integrate sampling directly into the process – real-time testing: that way, we no longer need to rely on time-consuming manual handling in order to know how well the production process is going.” She set up her lab at TU Delft in September 2020. Read more Higher quality and more sustainable Klijn explains that continuous testing will lead to biotechnological products with higher quality and processing efficiency: “Real-time testing improves product quality, because the biotech company has continuous control of the product: the computer can easily detect deviations in the production chain at a much faster pace and change parameters so as to prevent failure, like a loss of product or unhappy cells. Real-time testing is also required if you want to move to continuous manufacturing, which allows companies to reduce their footprint and become more sustainable.” The biopharmaceutical industry is actively searching for more continuous processing setups that can lead to more efficient and affordable production of medicine, such as vaccines or cancer medicine. Klijn: “A continuous process flow already exists in other types of industries, such as the petrol industry. But in the case of biological matter, continuous processing and testing is more difficult: it has more technological, biological and regulatory challenges. You can’t be sure of the result in advance.” Eating habits of cells in real-time “In my lab we have a setup to insert analytical techniques into a bioreactor and extract processing data in real-time,” Klijn explains. “A bioreactor is basically a vessel with nutrients and cells. These cells can produce a specific biotechnological product, for example a food additive or a drug compound, in a highly controlled environment. We would like to monitor different cells to find out how to make these models robust for industrial application: so that when products or cell lines change, the company doesn’t need to go through the whole development phase again.” The main analytical tool that Klijn’s lab uses is Raman spectroscopy: a technique in which laser light is scattered due to molecular vibrations. Each molecule will have a different scattering pattern, making it possible to study all kinds of changes in different molecules in real-time: “With this technique we now know at once how many cells are alive during the production process, or how fast they are eating glucose, for example – all without the need to take a sample. The combination of this analytical technique and machine learning makes it possible to look at many different parameters at the same time.” The main analytical tool that Marieke Klijn uses is Raman spectroscopy, a technique in which laser light is scattered due to molecular vibrations. Blurry lines The biotechnological industry already makes use of Raman spectroscopy: they show a lot of interest in the models that the team can build for the process control systems. “We would like to capture as much information as feasible. For example, in addition to Raman spectroscopy, we want to use real-time imaging data to tell us how the cells are changing during the process of glucose eating.” The sheer amount of data this entails presents a challenge in itself, Klijn says: “Our dilemma here is how to effectively use the huge amount of information that we acquire.” How has this new focus on improvements during bio-production influenced the research field? “Before I started this work, each specialist would focus on their own part of the manufacturing process. Now the lines are blurry and they all work together. This makes the work very diverse. I can combine implementation of data analytical tools, with how the cell works and bioprocess engineering solutions. I work with experts from many different fields on the upstream part of processing, such as cell cultivation, and the downstream part such as modellers: all to make the production process into one single continuous flow.” Dr. ir. Marieke Klijn Assistant Professor +31 15 27 81280 m.e.klijn@tudelft.nl Room C0.550 Building 58 Van der Maasweg 9 2629 HZ Delft linkedin More stories

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New LDE trainee in D&I office

Keehan Akbari has started since the beginning of September as a new LDE trainee in the Diversity and Inclusion office. What motivated him to work for the D&I office, what does he expect to achieve during this traineeship? Read the short interview below! What motivated you to pursue your LDE traineeship in Diversity and Inclusion office of the TU Delft? I completed both bachelor's and master's degrees in Cultural Anthropology and Development Sociology at Leiden University. Within these studies, my main area of interest was in themes of inclusion and diversity. After being hired as a trainee for the LDE traineeship, and discovering that one of the possible assignments belonged to the Diversity and Inclusion office, my choice was quickly made. I saw this as an excellent opportunity to put the theories I learned during my studies into practice. What specific skills or experiences do you bring to the D&I office that will help promote inclusivity on campus? I am someone who likes to connect rather than polarize, taking into account the importance of different perspectives and stakeholders. I believe that this is how one can achieve the most in fostering diversity and inclusion. You need to get multiple parties on board to get the best results. What are your main goals as you begin your role here, and how do you hope to make an impact? An important goal for me this year is to get students more involved in diversity and inclusion at the university. One way I will try to accomplish this is by contributing to the creation of D&I student teams. By establishing a D&I student team for faculties, it will be possible to deal with diversity- and inclusion-related issues that apply and relate to the specific department. How do you plan to engage with different (student) communities within the university? Since I am new to TU Delft, the first thing I need to do is expand my network here. Therefore, I am currently busy exploring the university and getting to know various stakeholders. Moreover, I intend to be in close contact with various student and study organizations to explore together how to strengthen cooperation on diversity and inclusion. Welcome to the team Keehan and we wish you lots of success with your traineeship!

Researchers from TU Delft and Cambridge University collaborate on innovative methods to combat Climate Change

For over a year and a half, researchers from TU Delft and the Cambridge University Centre for Climate Repair have worked together on groundbreaking techniques to increase the reflectivity of clouds in the fight against global warming. During a two-day meeting, the teams are discussing their progress. Researchers at Cambridge are focusing on the technical development of a system that can spray seawater, releasing tiny salt crystals into the atmosphere to brighten the clouds. The team from TU Delft, led by Prof. Dr. Ir. Herman Russchenberg, scientific director of the TU Delft Climate Action Program and professor of Atmospheric Remote Sensing, is studying the physical effects of this technique. Prof. Russchenberg emphasizes the importance of this research: "We have now taken the first steps towards developing emergency measures against climate change. If it proves necessary, we must be prepared to implement these techniques. Ideally, we wouldn't need to use them, but it's important to investigate how they work now." Prof. Dr. Ir. Stefan Aarninkhof, dean of the Faculty of Civil Engineering and Geosciences, expresses pride in the team as the first results of this unique collaboration are becoming visible. If the researchers in Delft and Cambridge can demonstrate the potential of the concept, the first small-scale experiments will responsibly begin within a year. This research has been made possible thanks to the long-term support from the Refreeze the Arctic Foundation, founded by family of TU Delft alumnus Marc Salzer Levi . Such generous contributions enable innovative and high-impact research that addresses urgent global challenges like climate change. Large donations like these enable the pursuit of innovative, high-impact research that may not otherwise be feasible, demonstrating how our collective effort and investment in science can lead to real, transformative solutions for global challenges like climate change. Climate-Action Programme

How system safety can make Machine Learning systems safer in the public sector

Machine Learning (ML), a form of AI where patterns are discovered in large amounts of data, can be very useful. It is increasingly used, for example, in chatbot Chat GPT, facial recognition, or speech software. However, there are also concerns about the use of ML systems in the public sector. How do you prevent the system from, for example, discriminating or making large-scale mistakes with negative effects on citizens? Scientists at TU Delft, including Jeroen Delfos, investigated how lessons from system safety can contribute to making ML systems safer in the public sector. “Policymakers are busy devising measures to counter the negative effects of ML. Our research shows that they can rely much more on existing concepts and theories that have already proven their value in other sectors,” says Jeroen Delfos. Jeroen Delfos Learning from other sectors In their research, the scientists used concepts from system safety and systems theory to describe the challenges of using ML systems in the public sector. Delfos: “Concepts and tools from the system safety literature are already widely used to support safety in sectors such as aviation, for example by analysing accidents with system safety methods. However, this is not yet common practice in the field of AI and ML. By applying a system-theoretical perspective, we view safety not only as a result of how the technology works, but as the result of a complex set of technical, social, and organisational factors.” The researchers interviewed professionals from the public sector to see which factors are recognized and which are still underexposed. Bias There is room for improvement to make ML systems in the public sector safer. For example, bias in data is still often seen as a technical problem, while the origin of that bias may lie far outside the technical system. Delfos: “Consider, for instance, the registration of crime. In neighbourhoods where the police patrol more frequently, logically, more crime is recorded, which leads to these areas being overrepresented in crime statistics. An ML system trained to discover patterns in these statistics will replicate or even reinforce this bias. However, the problem lies in the method of recording, not in the ML system itself.” Reducing risks According to the researchers, policymakers and civil servants involved in the development of ML systems would do well to incorporate system safety concepts. For example, it is advisable to identify in advance what kinds of accidents one wants to prevent when designing an ML system. Another lesson from system safety, for instance in aviation, is that systems tend to become more risky over time in practice, because safety becomes subordinate to efficiency as long as no accidents occur. “It is therefore important that safety remains a recurring topic in evaluations and that safety requirements are enforced,” says Delfos. Read the research paper .