Biography
Aaron Darling is a Professor in Computational Genomics and Bioinformatics in the UTS Faculty of Science's ithree institute. He has over a decade of experience developing computational methods for comparative genomics and evolutionary modeling and in 2013 moved from the University of California-Davis to start a computational genomics group at UTS.
Darling embarked on his research career at the University of Wisconsin-Madison. Following a bachelor's degree in Computer Science, he worked with members of the UW-Madison Genome Center to sequence and analyze the first genomes of pathogenic E. coli. During this time Darling led the development of some widely used computational methods for analysing genomic data, including the mpiBLAST open source parallel BLAST software and the Mauve software for comparing multiple genome sequences.
Following the award of a Ph.D. at UW-Madison, Darling received a fellowship from the US National Science Foundation to pursue postdoctoral studies at The University of Queensland. After two years at UQ he then returned to UC Davis to develop a research program in computational metagenomics -- the study of uncultivated microorganisms from the environment using computational methods.
Darling now brings his experience to understand the relationship between humans and microorganisms in collaboration with microbiologists at the ithree institute.
Professional
Journal Editor:
- PLOS Computational Biology
Conference chair:
- ISMB Microbiome COSI (2018, 2019)
- Workshop on Algorithms in Bioinformatics (2013)
- RECOMB Comparative Genomics (2011)
Professional society memberships:
- President, Australian Bioinformatics and Computational Biology Society (ABACBS)
Links
Research Interests
Comparative genomics
Designing and developing scalable computational algorithms to identify the complete set of genetic differences between two or more organisms and relating these differences to aspects of the organism's biology. Associating genomic changes to phenotypic changes.
Computational metagenomics
The vast majority of life on the planet is microbial, and most of it can not be studied by laboratory cultivation. Metagenomics involves DNA sequencing of microbes taken directly from the environment. Current metagenomic methods require advanced computational, statistical, and machine learning techniques to identify the organisms present in a sample and characterize their potential for encoding functional proteins.
Genome evolution
Life is thought to have existed on earth for at least four billion years. During this time, evolution has shaped the genomes of modern organisms. Using statistical methods such as continuous time Markov chain models we can infer the history of genome evolution that led to modern organisms. I am interested in applying methods from statistical mechanics and financial market modeling to develop scalable computational methods to reconstruct evolutionary histories.
Next-generation DNA sequencing
DNA is fundamentally a molecule that encodes digital information. New sequencing technology enables us to read this biological information en masse so that it can be analyzed computationally. I am interested in designing sequencing experiments and protocols in ways that maximize the useful information obtained about a biological system.
Teaching Areas
Professor Darling supervises research higher degree students.
External partners
Fred Hutchinson Cancer Research Center
Dr. Erick Matsen
University of California - Davis
Professor Jonathan A. Eisen
NSW Department of Primary Industries
Dr. Toni A. Chapman, Dr. Daniel Bogema
Helmholtz Centre for Infection Research
Prof. Alice McHardy