Anders Hafren

A striking global outlook change that manifested during the last decade is the reality of a changing climate and uncertainty of future. Sustainability and resilience are among the top priorities in plant science today, and for a reason. Despite that plants are master adapters to harsh environments and diseases through evolution and selection, this constellation does not apply to our food producing crops within an accelerated climate change frame. Thus, methods and knowledge of molecular mechanisms by which we can feasibly engineer our crops to meet these future challenges are urgently needed. Within this large global sustainability purpose, our predicted way to essential discoveries lies within the largely unknown lives of viruses and more precisely how they have learned through their amazing capacity for evolution to engineer their plant host. In particular, we hope that our focus on viral manipulations of plant resilience mechanisms may pave the way to climate, disease and virus resilient crops.

Maria Westerholm

Biomethane (biogas) production is a waste-to-energy technology with outstanding climate, environmental and societal benefits. The aim of the present project is to improve the activities of a ubiquitous and important microbial group called ‘syntrophs,’ that work in tight cooperation to form methane in these biotechnology systems. Initially, cultivation, molecular and visualization approaches will be used to address key knowledge gaps within the area. This will enable us to reconstruct metabolic models to predict nutrient requirements and rationally design new culture conditions for these microbes. Thereafter, the outcome will be practically assessed in cultivation systems that mimic the habitat in large-scale facilities. The prospect is to contribute to the development of effective strategies to increase the methane forming capacity of syntrophs and by that improve productivity in biomethane production systems with decisive importance for the transition to a sustainable society.

Magnus Kjærgaard

Microorganisms such as yeast can produce natural chemicals from other other organisms such as plants in a sustainable manner. This involves genetically engineering the yeast to produce the right plant enzymes but does not always work. Microorganisms have different internal environment than plants which may interfere with the enzymes. Here, we engineer a new compartment in the microorganism – a membrane-less organelle. The membrane-less organelles should act as a reaction chamber than isolate the new enzymes from the life processes of the host and vice versa. This organelles should concentrate the right enzymes and their substrates and exclude interfering enzymes. We hypothesize that such structures will accelerate the reaction and reduce formation of by-products and can be used to produce many different chemicals. We will focus on enzymes producing natural colourants as a test case to explore general principles governing enzyme containing membrane-less organelles.

Sisse Njor

Sisse Njor says: “The purpose of this project is to improve existing analytical tools used to estimate the major benefits and harms of cancer screening.

Researchers are globally striving to produce reliable estimates on the major benefits and harms of cancer screening and to agree upon which methods that produce reliable estimates. However, the existing analytical tools are increasingly obsolete and require updating. This project will suggest and validate a new method based on existing analytical tools from other research areas. A method that will hopefully enable researchers to produce reliable estimates on the major benefits and harms of cancer screening, both for the entire population and for subgroups.

With the increasing moves to use individualized screening it is extremely important to know if there are subgroups that only have a very small reduction in cancer mortality when participating in screening or subgroups who have a particular high risk of overdiagnosis. The new method may provide these answers”.

Sisse Njor holds a Senior Researcher position at Randers Regional Hospital, Department of Public Health Programmes since 2017, and is furthermore affiliated to Aarhus University, Institute of Clinical Medicine as an Associate Professor and the Danish Clinical Quality Program, National Clinical Registries as an epidemiologist.

Hiren Joshi

Hiren Joshi says: “The “third language of life” after genes and proteins is that of complex sugars. This language (the glycocode) describes myriad ways that organisms have fine-tuned proteins and cellular functions to allow complex life to thrive. We know how 100s of enzymes generate sugars in cells, but we do not know how the individual cell regulates its enzymes and glycosylation network to make specific sugars required in health and diseases. The goal of this project is to learn how the glycocode is regulated, and in doing so reveal its functions. Using data science, the project team will build a foundational machine learning model for in silico glycoscience: GolgiNet. Capturing the regulatory patterns of cellular glycosylation, GolgiNet will be used to predict biological functions, reveal the sugars of a single cell, and predict the sugar-coated proteins a cell is programmed to make. GolgiNet will transform our ability to understand the third language of life, providing a Rosetta stone to decipher how sugars can mediate biological interactions”.

Hiren Joshi came to University of Copenhagen in 2012, and has since 2021 been an Associate Professor at the Copenhagen Center for Glycomics.

Fernando Racimo

Fernando Racimo says: “The genomes of organisms contain information about their past history: migrations, displacements and expansions of populations can be discerned from the footprints they left in genetic sequences – including our own genomes. Space is thus a crucial dimension of evolution: organisms interact, mate and compete with organisms that are closest to them in their landscape. Yet, tools for analyzing genomes in space are scarce or highly limited in scope.

Which types of genetic patterns are most informative of spatial aspects of the history of a species? And how can we best harness them to better understand the movement and past distribution of those species? To answer these questions, our research program will generate an array of computational tools for simulating, analyzing and modelling genomes on real geographic landscapes. These tools will be applicable to genetic data from both present-day living organisms and from extinct populations, allowing us to better understand population processes with unprecedented detail.

We will then apply our newly developed methods to a specific case-study: ancient epidemics in recent human prehistory. We will infer the spatial distribution and expansion of ancient pathogens and their hosts, using a combination of present-day and ancient genomic data. We will seek to understand how past epidemics have affected human populations over the last 50,000 years, how humans – in turn – have responded to these epidemics, and how future epidemics might unfold over time, as a consequence of climate change and ecological breakdown”.

Fernando Racimo came from UC Berkeley to University of Copenhagen in 2017 and is now an Associate Professor at Globe Institute.

Mikkel Schmidt

Mikkel Schmidt says: “Designing and creating new molecules and materials with specific tailored properties can lead to huge progress in medicine, solar cells, catalysts, and many other scientific areas. But identifying which compounds have the properties we desire is not easy. While quantum mechanical computations can determine many properties in a matter of minutes or hours, the number of possible molecules is so huge that search by trial and error is futile. Based on databases of known compounds and their properties, deep neural networks have proven extremely efficient in predicting properties of new compounds. These neural networks can guide our search, but have no notion of uncertainty and can give misleading results that are difficult to diagnose. To be used efficiently, the neural networks need to know what they don’t know. In this project we will develop methods for uncertainty quantification in deep neural networks aimed at the search for new and exciting materials and molecules”.

Mikkel Schmidt has been an Associate Professor at DTU Compute since 2013.

Erin Gabriel

Erin Gabriel says: “The project aims to develop statistical methods to improve personalized treatment decision-making while considering patient burden and accounting for the shortcomings of the data being used.

As medical data and treatment options grow, a vital question becomes how to use the information available to make the best treatment decision. Methods exist to help select the best treatment for each patient based on patient and disease characteristics. However, these methods often do not consider the patient’s burden for collecting those characteristics, nor do they account for the potential shortcomings of the data used in the selection. Both issues can lead to the selection of sub-optimal treatments and potential harm to patients. To avoid this, selected decisions should be tested in clinical trials, and the patient burden should always be considered. Randomized clinical trials can be costly, untimely, or simply impossible. The use of validated surrogate endpoints can make randomized clinical trials feasible, but in the setting of personalized treatment, improved statistical evaluation methods are needed. Regardless of the data collection type, there is also a need for statistical tools that account for patient burden in treatment selection. Finally, when observational data must be used, improved methods are needed for treatment selection that account for biases that may occur due to the lack of randomization”.

Erin Gabriel joined the Biostatistics Section of University of Copenhagen, Department of Public Health as an Associate Professor in 2022.

Stefan Stender

Stefan Stender says: “This project aims to identify genetic factors that influence progression of five common diseases: type 2 diabetes, ischemic heart disease, chronic kidney disease, fatty liver disease, and chronic obstructive pulmonary disease. We hypothesize that there are distinct ‘onset-promoting’ and ‘progression-promoting’ genetic factors for any given disease. To test this, we will scan the genomes of 600,000 persons from the British and Danish population for genetic variants that affect disease progression. The genetic variants identified in the project have the potential to yield new insights into the factors that drive human disease. Such insights may ultimately inspire the development of new drugs targeting the causal pathways, aiming to forestall or even reverse disease progression.”

Stefan Stender has been a staff specialist at the Department of Clinical Biochemistry, Rigshospitalet, since 2021.

Jakob Christensen

Jakob Christensen says: “Epilepsy is a common brain disease that can affect children, young people and the elderly. The disease has a great impact on the people affected by it and their families, and epilepsy is associated with a high morbidity and a high mortality rate. This study will try to find the causes of epilepsy in different age groups – and look at how environmental and genetic causes play together. The study uses data from Danish registers and Danish biobanks and is expected to be able to provide completely unique results that can only be produced using Danish data and the results are expected to receive great international attention.”

Jakob Christensen is Consultant in Neurology at the Department of Neurology, Aarhus University Hospital, and associate professor at Department of Neurology, Aarhus University.