Resurrecting Ancient Proteins to Unlock New Catalytic Activity. This project aims to study the proteins that nature uses to make penicillin and related antibiotics, and their prehistoric ancestors. By doing so, the project expects to deepen understanding of these important processes, open up ways to make new antibiotics, and generate new knowledge about protein evolution. Intended outcomes include new biocatalysts based on the ancient ones, new antibiotic compounds active against resistant bacte ....Resurrecting Ancient Proteins to Unlock New Catalytic Activity. This project aims to study the proteins that nature uses to make penicillin and related antibiotics, and their prehistoric ancestors. By doing so, the project expects to deepen understanding of these important processes, open up ways to make new antibiotics, and generate new knowledge about protein evolution. Intended outcomes include new biocatalysts based on the ancient ones, new antibiotic compounds active against resistant bacteria, and a richer understanding of how these proteins have evolved over the last 4 billion years. This promises significant benefits in the form of new ways to address the challenge posed by antimicrobial resistance to antibiotics, which is a serious threat to the continued effectiveness of current antibiotics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100944
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Statistical frameworks for high-parameter imaging cytometry data. The project aims to develop statistical and bioinformatics methodology for characterising the complex interactions between cells in their native environment. Recent advances in imaging cytometry technologies have made it possible to observe the behaviour of multiple cell-types in tissue concurrently. The intended outcome is a suite of statistical methodologies that are crucial for addressing a variety of biological problems with t ....Statistical frameworks for high-parameter imaging cytometry data. The project aims to develop statistical and bioinformatics methodology for characterising the complex interactions between cells in their native environment. Recent advances in imaging cytometry technologies have made it possible to observe the behaviour of multiple cell-types in tissue concurrently. The intended outcome is a suite of statistical methodologies that are crucial for addressing a variety of biological problems with these state-of-the-art technologies. This work will advance knowledge in bioinformatics, statistics and image analysis, providing benefits to scientists studying the fundamental behaviour of cells and underlying disease mechanisms.Read moreRead less