At the intersection of systems biology and artificial intelligence, we view the cell not just as a biological entity, but as a complex, computable network. Our mission is to transform raw "omic" data into high-fidelity digital twins of cellular metabolism.
Biological systems are governed by thousands of interconnected reactions. To truly understand life, we must map these connections. Through Genome-Scale Metabolic Modeling (GSMM), we reconstruct the metabolic blueprints of organisms, allowing us to simulate, predict, and optimize cellular behavior with unprecedented accuracy.
While traditional bioinformatics provides the map, AI-driven modeling provides the engine. We leverage advanced machine learning and constraint-based reconstruction to:
In an era of big data, the challenge is no longer just gathering information—it is finding the meaning within it. By integrating AI with structural metabolic networks, we move beyond simple observation. We are decoding the logic of life.
🧬 Transcriptomics
⚡ Metabolic Modeling
📊 Functional Genomics
Dr. Ömer Faruk Bay holds a degree in Molecular Biology and Genetics from Atatürk University, and earned his master's and Ph.D. in Systems Biology & Bioinformatics from the University of Manchester, UK. His doctoral research centered on the metabolism of Trichuris muris, where he pioneered the first genome-scale metabolic model for this parasitic whipworm to uncover novel therapeutic targets.
As a Postdoctoral Researcher in Lars Kuepfer's group, his work revolved around reconstructing genome-scale metabolic models for the synthetic mouse intestinal bacterial community (OMM19). By mapping their metabolic capabilities and interactions, he successfully decoded their collective phenotype and resilience to perturbations.
Currently, Dr. Bay serves as a Lecturer at AGÜ teaching Bioinformatics, and works as a consultant leveraging his deep expertise in functional genomics, systems biology, and metabolic modeling.
Genome-scale metabolic network reconstruction, manual curation, and constraint-based modeling for eukaryotic and prokaryotic systems.
Data analysis pipelines for bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics to extract meaningful biological networks.
Custom bioinformatics tool development and high-throughput biological data analysis tailored for your project's needs.
2024 – Present
Leading an international genomic collaboration focused on integrating RNA-seq data to improve genome annotations and construct genome-scale metabolic models for Trichuris muris and Trichuris trichiura.
Jan 2023 – Apr 2024
Reconstructed and analyzed genome-scale metabolic models for the synthetic mouse intestinal bacterial community (OMM19), investigating metabolic interactions and mechanisms driving dysbiosis.
2023
Bay, Ö.F., Hayes, K.S., Schwartz, JM. et al. A genome-scale metabolic model of parasitic whipworm.
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Undergraduate & Graduate Lecturer
Currently teaching core bioinformatics curriculums, including Computational Biology and Functional Genomics. Focused on bridging theoretical sequences to practical data science.
Graduate Teaching Assistant
Led practical modules for MSc students including Programming Skills, Computational Approaches to Biology, and Bioinformatics Tools and Resources.
Wellcome Sanger Institute & Cambridge Univ.
Collaborating closely on integrating multi-omics data for Trichuris metabolic modeling.
Uniklinik RWTH Aachen
Worked together on reconstructing and analyzing the OMM19 microbiome metabolic capabilities.