Developmental imaging of bacterial biofilms with advanced microscopy, image processing and machine learning
Developmental imaging of bacterial biofilms with advanced microscopy, image processing and machine learning
批准号:
2306751
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --
中文摘要
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英文摘要
Biofilms are dense, spatially complex communities of bacteria enclosed in a polymer matrix. Over 65% of all human infections are estimated to be biofilm-related, include cystic-fibrosis related Pseudomonas aeruginosa lung infections and Escherichia coli urinary tract infections. Although the complex structure of biofilms is responsible for their remarkable robustness, our understanding of the developmental and physical principles which determine biofilm structure is currently very limited. One important reason for this is that it is currently difficult to observe the development of a biofilm at sufficient resolution. To solve this, we need to build tools to observe how a micron sized bacterium multiplies and differentiates into a highly structured millimetre scale community. During this PhD you will establish widely useful microscopy and image processing tools for biofilm imaging. You will perform single cell resolution microscopy of biofilms as they grow and develop. You will then develop open source image analysis tools and software for high resolution 3D bacterial cell segmentation and lineage tracing using deep learning to achieve high robustness and sensitivity. The outcome of this project will be widely useful open source software & open science protocols for tracing bacterial development in structured biofilm communities via microscopy. What will you be doing for your PhD research? You will perform highly interdisciplinary work, working on both quantitative bioimaging (biophysics & computer science) and microbiology. This will include using advanced microscopy and microfluidics to image the development of biofilms at single cell resolution, and developing new image processing methods for three dimensional single cell lineage tracing of growing bacteria in space and time.
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