Discovery Early Career Researcher Award - Grant ID: DE190100116
Funder
Australian Research Council
Funding Amount
$415,737.00
Summary
Cell types and cell states revealed by single-cell regulatory networks. This project aims to use single-cell gene regulation networks to predict cell types. Computational approaches are needed to recapitulate how the over 37 trillion cells program the shared genome sequence in a human body to create astoundingly diverse forms and functions. This project integrates millions of high-resolution single-cell gene expression profiles with large-scale population regulatory data to systematically recons ....Cell types and cell states revealed by single-cell regulatory networks. This project aims to use single-cell gene regulation networks to predict cell types. Computational approaches are needed to recapitulate how the over 37 trillion cells program the shared genome sequence in a human body to create astoundingly diverse forms and functions. This project integrates millions of high-resolution single-cell gene expression profiles with large-scale population regulatory data to systematically reconstruct gene regulatory networks. These networks are the molecular basis for understanding human cells. This projects outcomes intend to include the first reference single-cell regulatory database and novel methods and software to predict individual cells. This project will contribute to advancing Australia's capabilities in single-cell, precision medicine, and big biological data analysis leading to significant scientific, societal and commercial benefits.Read moreRead less
The Stemformatics gene expression compendium: development of multivariate statistical approaches for cross platform analyses. Scientific data is gathered in many different forms, but there are significant gaps in our ability to analyse multiple datasets when generated on different pieces of equipment. This project will study three typical research questions in stem cell biology to develop new analytical approaches to help solve this major data gap.
Genetic variation of single cell transcriptional heterogeneity in HiPSCs. This project aims to investigate whether induced pluripotent stem cells (iPSC) can be used to study the functions of genetic variants associated with human phenotypes and cell fate decisions. The project will utilise technology to produce single cell RNA sequence data for 100,000s of cells. By sequencing individual cells, the genetic control of cellular heterogeneity both within and between cells can be identified, and in ....Genetic variation of single cell transcriptional heterogeneity in HiPSCs. This project aims to investigate whether induced pluripotent stem cells (iPSC) can be used to study the functions of genetic variants associated with human phenotypes and cell fate decisions. The project will utilise technology to produce single cell RNA sequence data for 100,000s of cells. By sequencing individual cells, the genetic control of cellular heterogeneity both within and between cells can be identified, and in doing so, will provide significant benefit by revealing the potential for iPSC to be used for functional translation of human genomics.Read moreRead less
Decoding miRNA regulated genetic circuits. This project will aim to develop a much better understanding of how the process of making proteins from genes is regulated, and will develop scientific software capable of predicting how a cell will respond to changes in this regulation. The results will have widespread use, including assistance in deciding the best treatments for genetic diseases.
Developing bioinformatics methods for single cell transcriptomics. This project aims to develop novel bioinformatics methods for single cell transcriptomic data that seek to model variability in cell populations. The project expects to generate new approaches using Bayesian statistics that will act as high-end enablers of discovery in transcriptional regulatory processes. Through an interdisciplinary combination of experimental and computational research, insights into fundamental biological pro ....Developing bioinformatics methods for single cell transcriptomics. This project aims to develop novel bioinformatics methods for single cell transcriptomic data that seek to model variability in cell populations. The project expects to generate new approaches using Bayesian statistics that will act as high-end enablers of discovery in transcriptional regulatory processes. Through an interdisciplinary combination of experimental and computational research, insights into fundamental biological processes will be elucidated, specifically the robustness of cellular systems. Expected outcomes include a suite of novel tools that will push the boundaries of current bioinformatics solutions with potential to deliver significant benefits to every domain of biological science, particularly tissue engineering and synthetic biology.Read moreRead less