My primary research goal is to investigate the fundamental molecular mechanisms by which GPCRs are sensing and binding signaling metabolites and how these interactions control metabolic functions. I aim to utilize this knowledge to rationalize the design of novel drugs by combining advanced Molecular Dynamics Simulations and genetic models.

My main interest is the development and application of new mass spectrometry-based proteomics strategies and the computational analysis of proteomics and other high-throughput data. In my PhD project, I’m working on single-cell proteomics in normal and malignant hematopoiesis.

I am primarily interested in the bioengineering of whole-cell microbial biosensors.  In particular I hope to improve and expand the ability of microbes to sense their environments, in order to facilitate the development of better, more precise microbial therapies.

How does a stem cell know what to become? The push in the right direction, called “priming” is known to be directly regulated on the level of enhancer activity. My main interest is grounded in understanding the molecular basis of enhancer regulation, especially how specific promoter-enhancer interactions are facilitated and how these interactions are induced by an Erk1/2 signal. This is very interesting as we know that the induction of the Erk-pathway in the early mouse embryo is important for embryonic stem cells to differentiate and to give rise to the primitive endoderm.

I have always been curious about the ability of protein structures to display such diversity and adaptability even though their building blocks (amino acids) are the same. For instance, CRISPR-Cas systems are all based on the same principles but different classes and types have distinct architectural features. Because of this, my main interest is the structure and functional mechanisms of novel CRISPR-Cas complexes and their possible adaptation to biotechnological applications.

I am fascinated by cell fate decisions, how cell lineages and organs arise in a precise spatio-temporal manner during development, and also how different cell types retain their identity when surrounded by other cell types. Transcription factors and signaling pathways play a crucial role in cell fate decisions and elucidating the precise regulation of key factors required for fate choices is of importance to understand development. During my PhD I will be focusing on Notch signaling mediated fate decisions in the pancreas and investigating the precise regulation of key transcription factors in this context.

I am focused on programming, statistics and machine learning, including models from the field of Deep Learning. Coming from a Master in Statistics – covering several multivariate statistical methods, causal analysis, linear and non-linear generalized regressions, survival models, random forests, neural networks and Bayesian nets – and an industry trainee program, I am always looking for new opportunities to efficiently apply and extend machine learning models and methods for explaining their decisions. At CPR I will contribute to the field of proteomics.

Protein post-translational modifications (PTMs) are chemical modifications that regulate protein activity, localization, and interaction with other molecules. PTMs increase the functional diversity of the proteome, and understanding them is critical to the study of cell biology in health and disease. I am interested in using Mass Spectrometry to characterize and understand the function of obscure PTMs, for example, ADP ribosylation in the context of DNA damage repair.

I find gastrointestinal epithelium fascinating because it is constantly renewing throughout our lives with a steady, controlled pace. I would like to investigate the mechanisms that govern the cell fate decisions behind this well-established cellular hierarchy. Moreover, understanding regional differences in homeostasis maintenance between small intestine and colon might give us a clue to why they have different susceptibility towards certain diseases, like cancer.

The genetic code plays a vital role in determining status of health or disease. However, the final expression patterning is directed, in part, by the epigenome, which can be remodeled based on lifestyle modifications such as nutrition, physical activity, and environmental exposures. In my work, I study the role of nutrition in altering epigenetic patterning, and the implications for phenotype and fitness.

Crucial events in development, e.g. fate decision, are not only directed by gene expression or soluble signals but also by mechanosignalling both from inside and outside the cell. I am drawn to understanding how these mechanical cues interact with other developmental guidance signals to form functional tissue from initially pluripotent cells.

Brown adipose tissue (BAT) is a thermogenic organ that has recently gained big relevance due to its potential for preventing obesity related metabolic disorders. I am particularly interested in the secreted factors released by BAT – the so called batokines – and how these cross-talk with other cells in the tissue like immune cells or neurons. Unravelling the functions of novel batokines will help to better understand how BAT regulates physiological processes and how this contributes to whole-body metabolism.

I am interested in understanding how multiple biological layers are working together to make a cell behave in a certain way in a given situation. For that I am trying to combine information found in different ‘omics data and try to see how much we can predict about the behaviour of a cell given a defined modification.

A bio-based economy needs competitive bioprocesses. I focus on the engineering of cell factories for them to work optimally with their heterologous load and within bioprocess conditions. To do so, I want to implement the design-build-test cycle based on the predictions of computational tools and with the help of high throughput experiments.

My research interests revolve around using the many available bioengineering tools to create microbial cell factories that can produce sustainable organic compounds. By combining experimental and computational biotechnology on a whole-organism scale, I want to contribute to the development of economically viable alternatives to present-day non-renewable industrial processes.

I am interested in machine-aided design and optimisation of microbial cell factories. In particular, the development yeast platform strains for the sustainable production of biopharmaceuticals. This involves training machine learning algorithms to make predictions that augment the rational design of proteins and metabolic networks that can process non-natural substrates.

My research interests are within bioinformatics and computational biology fields. I hope to use various computational tools to elucidate trends in Denmark’s Electronic Health Record (EHR) data.  I ultimately would like to develop algorithms capable of predicting disease onset and trajectories and/or adverse drug effects at the patient level.