Seven IRP projects with IMM research groups involved, 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 of radboud University to start a pilot project with researchers of other institutes within the faculty and optionally also with external collaborators.
The awardees:
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.
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.
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.