My research sits at the intersection of biology, medicine, and artificial intelligence. I originally come from a background in Pathogenic Microbes, where I became interested in host–microbe interactions, inflammation, and how microorganisms influence human health. During my Master's studies in Life Science Informatics at the University of Bonn, this biological interest developed into a computational research direction: how can we use AI, ontologies, and knowledge graphs to make complex biomedical evidence more structured, traceable, and reusable?

In my PhD, I focus on building evidence-aware knowledge graphs for biomedical reasoning, with a special interest in healthy ageingaging, nutrition, the microbiome, and brain health. These fields generate large amounts of scientific data, but the evidence is often fragmented across publications, clinical studies, biological databases, and different terminology systems [1, 2]. As a result, it remains difficult to systematically connect dietary factors, molecular pathways, biomarkers, ageingaging mechanisms, and disease contexts.

This project is shaped by an interdisciplinary supervision team that connects biomedical AI, nutrition, and microbiome research, and clinical neurology. At Fraunhofer SCAI, the work is embedded in the group of Prof. Dr. Martin Hofmann-Apitius, with additional supervision from Jun. Prof. Dr. Marie-Christine Simon and Prof. Dr. med. Ullrich Wüllner at the University of Bonn.

My work aims to address this challenge by transforming scattered biomedical evidence into structured, algorithmically usable knowledge. Instead of treating scientific literature as isolated documents, I use ontology-driven modelling and knowledge graph technologies to represent biological concepts and their relationships in a more interpretable way [3, 4]. This allows mechanisms, interventions, biomarkers, and evidence sources to be connected and explored together.

One important focus of my research is the link between nutrition, the gut microbiome, and brain health (see Figure 1). The tryptophan–kynurenine pathway provides a biologically meaningful case study because it connects dietary amino acid metabolism, microbial regulation, inflammation, and neurological processes [5, 6].

Disruption of this pathway, reflected in altered kynurenine/tryptophan ratios and shifts in the balance between kynurenic acid and quinolinic acid, has been associated with depression, neuroinflammation, and neurodegeneration [7, 8]. The gut microbiome actively modulates this pathway through microbial tryptophan catabolism and indole production, linking dietary intake to neuroactive metabolite profiles [9]. At the same time, my broader work on healthy aging aims to organize evidence around ageingaging-related mechanisms and nutritional factors in a structured, evidence-based framework [10].

The long-term goal is to support a more mechanistic and transparent way of building computational precision nutrition on traceable evidence. By combining biomedical AI, ontology engineering, and knowledge graphs, my research aims to move beyond keyword-based search and isolated associations toward systems that can support evidence-based reasoning and future translational research [3, 11].

References

[1] Chandak, P., Huang, K., & Zitnik, M. (2023). Building a knowledge graph to enable precision medicine. Scientific Data, 10, 67. https://doi.org/10.1038/s41597-023-01960-3

[2] Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017). Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife, 6, e26726. https://doi.org/10.7554/eLife.26726

[3] Karki, R., Gadiya, Y., Zaliani, A., Pokharel, B., Babaiha, N. S., Ostaszewski, M., Hofmann-Apitius, M., & Gribbon, P. (2025). Knowledge Graph Generator (KGG): a fully automated workflow for creating disease-specific knowledge graphs. Bioinformatics, 41(7), btaf383. https://doi.org/10.1093/bioinformatics/btaf383

[4] Mohamed, A. H., Shalaby, K. S., Kaladharan, A., Güvenilir, H. A., & Kodamullil, A. T. (2026). KG-Orchestra: An Open-Source Multi-Agent Framework for Evidence-Based Biomedical Knowledge Graphs Enrichment. bioRxiv. https://doi.org/10.64898/2026.02.18.706536

[5] Schwarcz, R., Bruno, J. P., Muchowski, P. J., & Wu, H.-Q. (2012). Kynurenines in the mammalian brain: when physiology meets pathology. Nature Reviews Neuroscience, 13(7), 465–477. https://doi.org/10.1038/nrn3257

[6] Cervenka, I., Agudelo, L. Z., & Ruas, J. L. (2017). Kynurenines: tryptophan's metabolites in exercise, inflammation, and mental health. Science, 357(6349), eaaf9794. https://doi.org/10.1126/science.aaf9794

[7] Arnone, D., Saraykar, S., Salem, H., Teixeira, A. L., Dantzer, R., & Selvaraj, S. (2018). Role of kynurenine pathway and its metabolites in mood disorders: a systematic review and meta-analysis of clinical studies. Neuroscience & Biobehavioral Reviews, 92, 477–485. https://doi.org/10.1016/j.neubiorev.2018.08.011

[8] Lovelace, M. D., Varney, B., Sundaram, G., Franco, N. F., Ng, M. L., Pai, S., Lim, C. K., Guillemin, G. J., & Brew, B. J. (2016). Current evidence for a role of the kynurenine pathway of tryptophan metabolism in multiple sclerosis. Frontiers in Immunology, 7, 246. https://doi.org/10.3389/fimmu.2016.00246

[9] Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., Claes, S., Van Oudenhove, L., Zhernakova, A., Vieira-Silva, S., & Raes, J. (2019). The neuroactive potential of the human gut microbiota in quality of life and depression. Nature Microbiology, 4, 623–632. https://doi.org/10.1038/s41564-018-0337-x

[10] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). Hallmarks of aging: an expanding universe. Cell, 186(2), 243–278. https://doi.org/10.1016/j.cell.2022.11.001

[11] Babaiha, N. S., Guru Rao, S., Klein, J., Schultz, B., Jacobs, M., & Hofmann-Apitius, M. (2024). Rationalism in the face of GPT hypes: benchmarking the output of large language models against human expert-curated biomedical knowledge graphs. Artificial Intelligence in the Life Sciences, 5, 100095. https://doi.org/10.1016/j.ailsci.2024.100095