Universitetslektor, biträdandeDepartment of Chemistry & Molecular
About Daniel Bojar
Glycan-Focused Machine Learning and Systems Biology
Our overall goal is to use computational and experimental resources to better understand the intricate roles of glycans in biology and integrate glycobiology into commonly used high-throughput systems biology methods. Glycans, or complex carbohydrates, are a fundamental biopolymer next to DNA, RNA, and proteins and adorn other biomolecules or occur by themselves. Among biological sequences, glycan exhibit the highest diversity and the distinction of being the only non-linear biological sequence that is, furthermore, outside the central dogma of molecular biology. Glycans exercise crucial - yet insufficiently understood - roles in development, immunity, pathogenesis, cancer, and many more areas. Combining the best of both worlds, glycans have the complexity of a biological language comprising monomeric building blocks and the dynamicity of a post-translational modification, making them largely responsible for phenotypic plasticity.
The two main difficulties facing glycobiology today are the inability of extracting generalizable, mechanistic, or actionable insights from these highly diverse glycan sequences as well as the shortage of known glycan sequences due to the experimental difficulties of working with glycans. We are working on overcoming these difficulties to reap the rich rewards promised by the omnipresence of glycans in biological mechanisms and nearly all diseases. For this, we have developed deep learning models for glycobiology that, together with other bioinformatics approaches, can extract functional insights from glycan sequences for a more holistic understanding of molecular biology. We are continuing the development and application of new and improved analysis methods for glycobiology at scale, both computationally as well as experimentally. Additionally, we are constructing a platform to transform glycobiology into a true high-throughput discipline by interweaving it with current systems biology methods, lifting the sequence bottleneck that is currently limiting the scope of glycobiology. Our expertise in mammalian synthetic biology and protein engineering then allows us to use the insights gained by our deep learning models to modify glycans in situ and capitalize on their important roles in new therapeutic modalities in biomedicine.
Research tools and resources
We apply a wide range of methods, both computationally as well as experimentally. Our computational repertoire extends to the analysis of systems biology data, bioinformatics techniques, machine learning / deep learning, and the emerging area of glycobioinformatics. Experimentally, we engage in synthetic biology / genetic engineering in mammalian cells and bacteria, including techniques such as CRISPR/Cas9 gene editing, as well as in systems biology methods such as RNA-seq or glycomics.
We are especially interested in developing and applying methods for understanding the overarching role of glycans in biology and integrating glycobiology into current high-throughput systems biology efforts. Particularly, we are at the forefront for constructing glycan-focused machine learning algorithms. The integration of a computational "dry" lab and an experimental "wet" lab enables us to test our predictions and rapidly investigate new mechanisms that broaden our understanding of glycans and have considerable biomedical implications.
- Bioinformatics and Functional Genomics (BIO210)
- Experimental Systems Biology (BIO448)
Daniel Bojar studied biochemistry at the University of Tuebingen (Germany, B.Sc.) and biophysics at ETH Zurich (Switzerland, M.Sc.). Then, Daniel completed his Ph.D. in mammalian synthetic biology with Dr. Martin Fussenegger at the Department for Biosystems Science and Engineering (D-BSSE) of ETH Zurich, in which he worked on genetic engineering for biomedical applications and metabolic engineering for biotechnological applications. After his Ph.D., Daniel transitioned to a postdoctoral position with Dr. James J. Collins at the Wyss Institute for Biologically Inspired Engineering at Harvard University and the Institute of Medical Engineering & Science (IMES) at the Massachusetts Institute of Technology (MIT). There, Daniel pioneered the development and application of machine learning methods to the analysis of glycan sequences, to predict their biological properties and functions.