{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/FT250100252"},"data":{"type":"grant-details","id":"FT250100252","attributes":{"code":"FT250100252","administering-organisation":"The University of Sydney","announcement-administering-organisation":"The University of Sydney","scheme-name":"ARC Future Fellowships","grant-status":"Active","funding-commencement-year":2025,"years-funded":4,"project-start-date":"2026-06-30","anticipated-end-date":"2030-06-29","grant-summary":"Interpretable deep learning for cell programming. This project aims to harness cutting-edge deep learning and single-cell omics technologies to improve the accuracy and efficiency of cell programming, where one cell type is converted into another. This project expects to generate new knowledge of molecular networks and interdisciplinary approaches that utilise such knowledge for addressing key challenges in cell programming. Expected outcomes of this project include the development of advanced computational models that make cell programming more accurate, efficient, and reproducible. This should provide significant benefits by accelerating advancements in synthetic biology, and enhancing efficacy and efficiency in bioproduction and biomanufacturing.","funding-current":1307612.00,"funding-at-announcement":1280036,"investigators-current":[{"title":"A/Prof","firstName":"Pengyi","familyName":"Yang","roleName":"Future Fellowship","roleCode":"FT","isFellowship":true,"orcidIdentifier":null}],"investigators-at-announcement":[{"title":"A/Prof","firstName":"Pengyi","familyName":"Yang","roleName":"Future Fellowship","roleCode":"FT","isFellowship":true,"orcidIdentifier":null}],"organisations-current":[{"organisationName":"The University of Sydney","roleName":"Administering Organisation","state":"NSW"}],"organisations-at-announcement":[{"organisationName":"The University of Sydney","roleName":"Administering Organisation","state":"NSW"}],"field-of-research":[{"isPrimary":true,"code":"3102","name":"Bioinformatics and Computational Biology","type":"FOR20"},{"isPrimary":false,"code":"310201","name":"Bioinformatic Methods Development","type":"FOR20"},{"isPrimary":false,"code":"310204","name":"Genomics and Transcriptomics","type":"FOR20"}],"socio-economic-objective":[{"code":"280102","name":"Expanding Knowledge In the Biological Sciences","type":"SEO20"},{"code":"280115","name":"Expanding Knowledge In the Information and Computing Sciences","type":"SEO20"},{"code":"280118","name":"Expanding Knowledge In the Mathematical Sciences","type":"SEO20"}],"international-collaboration":["France","Singapore","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"Cell programming, the ability to convert one type of cell into another, holds tremendous potential for applications in synthetic biology, bioproduction, and biomanufacturing. However, its application is currently hindered by challenges such as low accuracy, efficiency, and reproducibility. This project aims to overcome these challenges by using advanced artificial intelligence techniques to uncover molecular networks that govern cell identity and cell-fate decisions for making cell programming more accurate, efficient, and reproducible.\n\nThe outcomes of this project will significantly advance Australia’s leadership in machine learning and computational science, particularly in their application to bioinformatics and molecular biology. Furthermore, the outcomes will enable subsequent innovations in synthetic biology, bioproduction, and biomanufacturing, driving economic, commercial, and environmental benefits for Australia’s burgeoning biotechnology industry.\n\nThis project will also play a pivotal role in advancing research training and education in Australia, cultivating the next generation of computational and experimental scientists. These efforts will provide lasting benefits to both academia and industry, supporting the development of a skilled workforce and fostering research and collaboration across scientific disciplines.\n"}}}