Prof. Adil Mardinoglu
Professor Adil Mardinoglu is an expert in the field of Systems Medicine, Systems Biology, Computational Biology and Bioinformatics. He has been recruited as a Professor of Systems Biology in Center for Host-Microbiome Interactions, King’s College London, UK where he leads a computational group. He also works as group leader in Science for Life Laboratory (Scilifelab), KTH-Royal Institute of Technology in Sweden and led a team of 25 researchers working in the area of computational biology, experimental biology and drug development to develop new treatment strategies for Metabolic diseases, Neurodegenerative diseases and certain type of cancers.
Professor Mardinoglu received his Bachelor’s degree from Istanbul Technical University, Turkey in Electronic and Telecommunication Engineering and his Ph.D. from Waterford Institute of Technology, Ireland in magnetic drug targeting applications. He worked as a postdoctoral researcher at Trinity College Dublin, Ireland and Chalmers University of Technology, Gothenburg, Sweden. His recent research activities include the generation of the context specific genome-scale metabolic models (GEMs) for human cell-types including liver, adipose, muscle, heart, kidney and brain as well as certain types of cancer e.g liver, kidney, colon, prostate and brain (glioblastoma) cancers. His research team also focuses on the integration of GEMs with the other biological networks including regulatory, protein-protein interactions and signaling networks. He employs comprehensive biological networks for revealing the molecular mechanisms of complex diseases, identification of novel biomarkers and drug targets and eventually development of efficient treatment strategies.
Professor Mardinoglu has contributed to the creation of human tissue, subcellular and pathology atlas within the Swedish Human Protein Atlas program and cell atlas within the international Human Cell Atlas program. He has published around 100 research and review papers in different journals including Science, Cell Metabolism, Nature Communications, PNAS, Cell Reports, Molecular Systems Biology and EbioMedicine. He is also co-founder of three different biotech companies focusing on the development of novel drugs for fatty liver disease and different cancer types.
Dr. Martin Steinegger
Dr. Steinegger is an Assistant Professor at Seoul National University, associated with the Department of Biology, Institute of Molecular Biology and Genetics, Artificial Intelligence Institute, and the Bioinformatics Graduate School. His research group is developing algorithms and machine learning methods to analyze large-scale genomic and proteomic sequence data. His group has made notable contributions in the field of bioinformatics through the development of methods for sequence clustering (Linclust), metagenome assembly (Plass), homology searching (MMseqs2), protein structure prediction (AlphaFold2/ColabFold) and protein structure search (Foldseek). These software packages enjoy worldwide use and have been installed hundreds of thousands of times.
Dr. Steinegger studied bioinformatics and computer science at the Technical University Munich and Ludwig Maximilian University of Munich. During this period, he worked closely with Professor Burkhard Rost, focusing on developing methods to predict protein mutation effects. He earned his Ph.D. from the Technical University Munich, under the guidance of Dr. Johannes Söding at the Max Planck Institute for Biophysical Chemistry. His Ph.D. research involved computational assembly, clustering, and annotation of metagenomic sequencing data. As a postdoctoral scholar at the Johns Hopkins University School of Medicine under the supervision of Professor Steven L. Salzberg, he developed methods for identifying pathogenic agents in infectious diseases, detecting assembly contamination in public datasets, and annotating missing exons in the human proteome.
As a prominent figure in the field of large-scale sequence data analysis and computational method development, Dr. Steinegger is a strong advocate for open science and open source.