Mikael Boden

Mikael Bodén has a PhD in Computer Science and statistical machine learning from the University of Exeter (UK) but has spent the last decade and a half in biological research environments, including the Institute for Molecular Bioscience/ARC Centre of Excellence in Bioinformatics and the School of Chemistry and Molecular Biosciences, where he is currently located. He is the director of UQ’s postgraduate program in bioinformatics. Mikael Bodén has supervised 7 postdocs from funding he received from both ARC and NHMRC; he has been the primary advisor for 11 PhD and 3 MPhil graduates; he is currently supervising another 6 PhD students in bioinformatics and computational biology. Mikael Bodén collaborates with researchers in neuroscience, developmental biology, protein engineering and bioeconomy to mention but a few, and contributes expertise in the processing, analysis and integration of biological data; this is exemplified by recent publications in Science, Nature Catalysis, Nature Communications, Cell Systems, Nucleic Acids Research and Bioinformatics. Bioinformatics: Thanks to major advances in biotechnology and instrumentation, biology is becoming an information centred science. The field of bioinformatics draws on computer science, math and statistics to enable discoveries in biological data sets. Our research aims to develop, investigate and apply bioinformatics methodologies to understand and resolve a range of open problems in genomics, molecular and systems biology. Recent applications involve protein sorting, nuclear protein organisation, mechanisms of transcriptional regulation, sequence and structure determinants of protein function and modification, and protein engineering. Biological data are now available at scales that challenges our ability to process and analyse them. On the flip side, greater scale gives statistical power to distinguish biologically meaningful signals from mere noise or artefacts, i.e. to identify "drivers" and "determinants" of function and structure. Sometimes the number of features (that describe each observation) is so great that we must use (biological) expertise to constrain the search for signals. Broadly put, our research aims to 1. effectively manage the complexity of operations involved in analysing millions of sequence reads, thousands of genomes, and proteomes of thousands of dynamically regulated molecules, etc 2. enable the seamless aggregation (or integration) of uncertain and incomplete data, typical of the next wave of biotechnology, across genomics, proteomics, structural biology, etc, and of using biological expertise 3. empower the interpretation of "whole system" data, aimed at understanding of basis of disease and other scientifically relevant phenotypes, using statistics and machine learning

[2026] Investigation of Substrate Specificity in 5‐Keto‐4‐Deoxy‐Glucarate Dehydratase and 2‐Keto‐3‐Deoxy‐L‐Arabinonate Dehydratase [2026] Cofactor specificity and extreme robustness in the catalytic landscape of ketol-acid reductoisomerase: an evolutionary reconstruction [2026] TRIAGE Toolkit: Streamlined Discovery of Regulatory Genes and Elements [2026] Ancestral protein reconstruction uncovers a thermotolerant rieske oxygenase with enhanced o -demethylation activity toward 3-O-methylgallate [2026] A methionine-lined active site governs carbocation stabilization and product specificity in a bacterial terpene synthase [2026] Thermostable ancestors enabled evolutionary diversification of promiscuous chemical defence enzymes [2026] Learning the language of phylogeny with MSA Transformer [202