Numerical models and methods for cell image analysis

细胞图像分析的数值模型和方法

• 批准号: 239162-2010
• 负责人: Wan, Justin
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547226
• 关键词: Numerical; models; methods; cell; image

Common diseases such as stroke, diabetes, asthma, and cancer result in substantial health care expenditure in Canada every year. These diseases are often caused by environmental factors interacting with genetically susceptible individuals. Understanding how the regulatory gene networks affect cell function will facilitate treatment of common disorders. However, linking genes, diseases, and treatments is time-consuming. This involves collection of massive amounts of data from native and genetically perturbed cells. The recent DNA microarray technology allows cell biologists to quickly record cell activities by high-throughput real-time imaging systems. With thousands of images generated in each experiment, the traditional manual or interactive computer-assisted tracking would no longer be feasible. Efficient and reliable software for tracking cellular behaviour becomes critical in analyzing the cell images.

中风、糖尿病、哮喘和癌症等常见疾病每年在加拿大造成大量的医疗支出。这些疾病通常是由环境因素与遗传易感个体相互作用引起的。了解调控基因网络如何影响细胞功能将有助于常见疾病的治疗。然而，将基因、疾病和治疗联系起来是很耗时的。这涉及到从天然细胞和受基因干扰的细胞中收集大量数据。最近的DNA微阵列技术使细胞生物学家能够通过高通量实时成像系统快速记录细胞活动。由于每次实验都会产生数以千计的图像，传统的人工或交互式计算机辅助跟踪将不再可行。用于跟踪细胞行为的高效和可靠的软件在分析细胞图像时变得至关重要。