Twelve interdisciplinary research groups receive an IRP voucher

02 October 2023 News item

Twelve research groups of the Faculty of Science have received a voucher of €50,000 each to work on an interdisciplinary research project in the field of Green Information Technology, Machine Learning in the Natural Sciences or Experimental Laboratories in Life Sciences.
The project teams received the sum as part of the faculty voucher arrangement of the Interdisciplinary Research Platform (IRP). The IRP voucher will allow researchers of the Faculty of Science to start a pilot project with researchers of other institutes within the faculty and optionally also with external collaborators.

Selection committees

There were 35 submissions in the 2024 call for vouchers of the Interdisciplinary Research Platform. These have been assessed by an external selection committee.

For submissions in the field of Green information Technology, the selection committee consisted of Edwin van Poppel (IKN, Nijmegen), Florian Meirer (Utrecht University) and Ingrid Måge (Nofima, Norway).
Submissions in the field of Machine Learning in the Natural Sciences were assessed by Elena Agliari (La Sapienza, Italy), Alessia Annibale (King’s College, UK) and Reimer Kuehn (King’s College, UK).
The selection committee for submissions in the field of Experimental Laboratories in Life Sciences consisted of Dolf Weijers (Wageningen University), Anna Alemany (Leiden UMC) and Maria Hopman (RadboudUMC).

Awards

The IRP vouchers of 2024 were awarded to:

CellFlucsTEST: Cells Exploit Fluctuations To Enable State Transitions

Hendrik Marks (RIMLS), Maike Hansen (IMM), Yuliya Shapovalova (ICIS), Gabriel Bucur (ICIS)

Cells within the early embryo go through a series of cell-fate decisions. ‘Noise’ in gene expression, a phenomenon that has been hypothesized as driving force in cell-state transitions, arises from fluctuations in the amounts of mRNA that a cell expresses. Noise in gene expression thereby appears to create required plasticity for stem cells during differentiation. Therefore, it is crucial to characterize how cellular noise is regulated. This project will apply single cell profiling technologies to identify the networks that regulate fluctuations throughout cell-state transitions of human embryonic stem cells. Furthermore, we aim to identify how these fluctuations can be used to control, manipulate, and predict the cell-state transitions.

Towards accurate detection of greenhouse gas emission from wastewater treatment plants

Amir Khodabakhsh (IMM), Roderik Krebbers (IMM), Kees van Kempen (IMM), Simona Cristescu (IMM), Annelies Veraart (RIBES), Lisanne Hendriks (RIBES), Ralf Aben (RIBES), Christian Fritz (RIBES), Sarian Kosten (RIBES), Sebastian Lücker (RIBES), Laurens Landeweerd (ISIS), Laurens Sluyterman (IMAPP), Eric Cator (IMAPP), Floor van Schie (Hoogheemraadschap Hollands Noorderkwartier), Eric van der Zandt (Hoogheemraadschap de Stichtse Rijnlanden), Mike Mirov (IPG Photonics Corporation), Ehsan Mohammad Pajooh (Jaeger Umwelt-Technik)

Wastewater treatment plants (WWTPs) contribute to greenhouse gas (GHG) emission such as CO2, CH4, and especially the potent N2O. However, GHG emission datasets from WWTPs with sufficient spatial and temporal coverage are scarce and very few experimental studies report on other climate active gases. To address this need, this project uses a novel multispecies gas sensor capable of open-path gas detection covering large spatial scales of WWTPs. This technique enables detection of several gases and volatiles, retrieving their real-time emission rates, and quantifies the GHG emissions for different WWTPs with unique operational features. This project will enable better quantitative understanding of the current emissions of GHGs and pollutants that impact public health. Improved GHG emission inventories will lead to better decision making on mitigation strategies, thorough evaluation of new green(er) technologies, and improve regional GHG budgets. Hence, providing a long-term benefit in the zero-emission vision and a healthier environment for the society.

Illuminating the ageing proteome: identifying protein-protein interactions of young and old proteins

Suzan Stelloo (RIMLS), Joep Joosten (IMM)

This project aims to develop a novel method to decipher the different protein-protein interactions (PPI) that proteins engage in across their lifespan. Proteins usually engage in complex networks with other proteins and dysregulation of PPI can affect numerous diseases, such as cancer and neurodegenerative disorders. Currently, there is no method to effectively identify PPIs of a target protein during its life journey in living cells. The method that will be developed in this project will involve incorporation of an unnatural amino acid in a protein of interest. This will make it possible to distinguish “young” and “old” proteins. To uncover the protein interactions, we will employ click chemistry and proximity labeling to map the interactions of the target protein bearing the unnatural amino acid. This novel technique might aid in the elucidation of disease mechanism, identification of therapeutic targets and fundamental molecular biology and cellular research.

Small-scale input, large-scale data: Compartment- and organelle specific proteomics in neurons

Koen Kole (DCN), Balaji Srinivasan (RIMLS), Pascal Jansen (RIMLS)

The endoplasmic reticulum (ER) is the largest organelle in mammalian cells. In neuronal cells, the ER is found as a continuous network throughout all of their projections, i.e. axons and dendrites. Because axons and dendrites greatly differ in function, the ER likely also has different functions in each subcellular compartment. Insight into the ER protein content will help understand how ER function differs between projections. However, the compartment-specific ER-protein composition remains elusive. This project will use microfluidic chambers to obtain protein samples specifically from axons or dendrites. Next, proximity biotinylation is applied to obtain ER-specific proteins. This combination of techniques has not been previously applied to study the ER-specific proteome of axons and dendrites. Through the examination of the ER proteome within specific neuronal compartments, this project aims to provide insights into ER function within different neuronal compartments under normal conditions, or when the ER is dysfunctional.

Elucidating bacterial paracetamol biodegradation

Cornelia Welte (RIBES), Floris Rutjes (IMM), Marjan Smeulders (RIBES), Robert Jansen (RIBES), Peer Timmers (KWR Water Research Institute), Wiktoria Czumaj (RU Biology honors student)

Water pollution is a key environmental problem that needs to be solved to preserve the health of our planet. This project aims to contribute to the discovery of the microbial paracetamol degradation pathway. Paracetamol, a key organic micropollutant (OMP), accumulates in surface waters due to inadequate OMP removal. Paracetamol is a widely used pain killer and a major micropollutant in the environment today. Interestingly, it can be biodegraded in some wastewater treatment plants but the responsible microorganisms and respective metabolic pathway(s) for its biodegradation remain unknown. The in-depth knowledge about paracetamol’s biodegradation is urgently required for improving its removal. This knowledge will help to devise bioremediation strategies for paracetamol contaminated waters.

Role of Foxe1 and alcohol exposure in the development of craniofacial abnormalities

Juriaan Metz (RIBES), Sophie Raterman (RIBES), Gert Jan Veenstra (RIMLS), Klaas Mulder (RIMLS) Hans von den Hoff (RUMC)

Craniofacial malformations are associated with mutations in genes involved in skeletal development pathways as well as environmental factors, such as drug use, smoking and drinking during pregnancy. The aim of this project is to understand how interactions between a genetic and environmental risk factor lead to an abnormally developing craniofacial skeleton. FOXE1 is a transcription factor involved in proper palate formation during craniofacial development. The researchers have created a foxe1 mutant zebrafish and also exposed wildtype and mutant larvae to ethanol. In this project, they will study mechanisms governing the interactions between a genetic (foxe1) and an environmental (ethanol) risk factor in craniofacial developmental processes.

ORLEANS: Offline Reinforcement Learning for Sustainable Transportation at Sea

Nils Jansen (ICIS), Thom Badings (ICIS), Sacha Caron (IMAPP), Thiago D. Simão (ICIS), Gijs Slijpen (Alfa Laval)

The exhaust gas emissions from ships are a large source of environmental pollution, with sulfur oxides and carbon dioxides being two of the main pollutants. Thus, cleaning the exhaust gases of ships is crucial for reducing the environmental footprint of sea transportation. Alfa Laval produces exhaust gas cleaning systems for ships under the name PureSOx. This system removes sulfur oxides, and enables ships to comply with international regulations on exhaust gases, by scrubbing the exhaust gas with water. The ORLEANS project aims to further improve the cleaning performance (and thus the environmental benefits) of PureSOx by optimizing the current control policies using specialized algorithms, especially from the field of safe offline reinforcement learning. With such methods, it is possible to use historical sensor data to learn new control policies that provably outperform the policies currently used. The project can contribute to an improved understanding of the gas-cleaning system, thus enabling Alfa Laval and ship operators to make better operating decisions in the scope of sustainability.

Unravelling the key drivers of animal movement

Marlee Tucker, Mark Huijbregts (RIBES), Tom Claassen (ICIS)

We are currently experiencing a biodiversity crisis. As human activities expand, animal behaviour is being altered with consequences for individuals and species. This project focuses on animal movement, which determines the fate of individuals, and plays a key role in the structure and dynamics of populations, communities, and ecosystems. Current methodological approaches used to understand what shapes animal movement patterns are based on correlation – the relationship between movement and variables such as food availability. However, correlative approaches are limited, ignoring potential cause-effect relationships and confounding effects between variables and even unmeasured variables. The project will go beyond this approach: it will employ novel causal discovery algorithms to identify causal effects by analysing the statistical patterns of observational data. This is not only important for understanding why animals move, but also, for understanding the links between animal behaviour, the environment and human impacts.

AI for broadband spectroscopy: How machine learning can help to find biomarker fingerprints to determine kidney haemodialysis efficiency?

Eric Cator, Laurens Sluijterman (IMAPP), Simona Cristescu, Amir Khodabakhsh, Roderik Krebbers (IMM), Peter Merkus (RadboudUMC), Mike Mirov, Sergey Vasilyev (IPG Photonics, USA)

Loss of kidney function is a life-threatening condition typically treated with haemodialysis or a kidney transplant. Chronic kidney disease often goes unnoticed until there is significant impairment in kidney function. Therefore, early detection of kidney failure is crucial. Unfortunately, there are currently no established biomarkers that allow early identification of deteriorating kidney function. However, evidence suggest that certain compounds associated with deteriorating kidney function are present in exhaled breath. As breath analysis is non-invasive, this approach is particularly feasible in children. This project aims to develop a machine learning model capable of accurately obtaining the concentrations of various compounds in exhaled breath samples, by using the absorption spectrum. The model is expected to work even in the presence of noise and spectral interference.

Probing the bittersweet stress response of Mycobacterium tuberculosis

Robert Jansen (RIBES), Thomas Boltje (IMM), Jona Merx (IMM), Jakko van Ingen (RUMC), Pieter van der Velden (RIBES)

Tuberculosis (TB) ranks first on the list of infectious causes of death and kills over 1.5 million people each year. Therefore, new tools for TB diagnosis and drug resistance are urgently needed. Mycobacterium tuberculosis (Mtb), the bacterium that causes TB, has a specific lifestyle. Mtb has specialized into an obligatory pathogen with humans as only reservoir. Thus, Mtb has evolved metabolic adaptations to cope with the immune-imposed stresses. Researchersin this project have recently discovered a new Mtb-specific stress response metabolite: a conjugate of a bitter and a sweet molecule. This molecule has never been described before in any organism and its function, biosynthesis, and breakdown, is completely new territory. This project aims to explore this exciting new territory and is expected to lead to high impact translational results.

Multi-scale dynamics and structural complexity of cortical neuronal activity

Federico Stella (DCN), Andrey Bagrov (IMM), Anna Kravchenko (IMM)

Many cognitive functions require constant combination and rearrangement of information present at different locations of the cerebral cortex, and continuous interactions between widespread neural networks. Among the functions that rely on such distributed dynamics, the most prominent example is memory consolidation, consisting in the integration of newly acquired experiences and in the updating of memories of different ages. This project will investigate the characteristics of neural activity patterns across multiple spatial scales by deploying methods from statistical mechanics of collective phenomena, and by applying it to an animal data set of cortical imaging. The proposal is based on the availability of extensive cortical data, made possible by the rapid advancement in large-scale neural recordings achieved over the last decade.

Can Question-Answering Systems make new discoveries in physics? An investigation into collaboration dynamics and epistemic authority

Sascha Caron (IMAPP), Henk de Regt (ISIS), Tom Claassen (ICIS/DaS), Kristian Gonzalez Barman (University of Ghent)

This project investigates avenues for the integration of Question-Answering Systems (QAS) into fundamental physics research. QAS are AI-powered interfaces executing a broad range of informational tasks, primarily in natural language – think of ChatGPT or GPT-4. These technologies have the potential to augment human cognitive abilities as described in extended cognition theories and pave the way for innovative interactions with complex data. The interdisciplinary consortium will focus on the synergistic dynamics between future researchers and QAS, emphasising the collaborative nature of human-AI teams. In this manner, the project wants to departure from the current focus on autonomous systems. Their investigation addresses the ways in which scientific discovery interrelates with the collaboration dynamics and distribution of epistemic authority in human-AI teams.

Contact information

Contact: I. Rijk (Isabel) MSc , A. Jurackova (Anna) , Dr E.J. Domínguez Vázquez (Eduardo)
Organizational unit: Interdisciplinary Research Platform, Faculty of Science