Integrative mathematical, computational, and experimental approach to study biological systems

研究生物系统的综合数学、计算和实验方法

• 批准号: RGPIN-2021-03472
• 负责人: Kohandel, Mohammad
• 金额: $ 2.33万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742569
• 关键词: Integrative; mathematical; computational; experimental; approach

The application of mathematical and computational models to biology is an exciting and novel area of research in Applied Mathematics. This integrative approach facilitates the interpretation of large datasets and captures the inherent complexity of living systems, which has the potential to revolutionize our understanding of biology. The long-term goal of the proposed research program is to advance the development and application of quantitative approaches in biological research by integrating experimental studies with mathematical and computational techniques. This will enable a more fundamental understanding of the complex processes in biological systems, with a special emphasis on the link between tissue cellular heterogeneity, the integrated stress response (ISR), and the immune system. This research will also provide valuable insights into the complex interactions between micro-environment and cell phenotype, and how these interactions modulate cell lineage. To this end, the research has four objectives: O1. Develop a novel, biomarker-free classification system for cell phenotypes (including epithelial and mesenchymal) using imaging flow cytometry combined with a high-performance machine learning architecture. O2. Build a compartmental mathematical model, grounded on experimental studies, to examine the dynamics between cell phenotypes and the effects of environmental factors (oxygen deficiency, metabolism) on phenotype switching, for instance, as observed in epithelial-mesenchymal transition. O3. Determine the molecular networks underlying the survival of different cell phenotypes in several tissue types, which will be accomplished by developing a ML architecture to classify genetic screening data from gene-editing techniques such as shRNA and CRISPR/Cas9. O4. Develop a systems biology approach to investigate the connection between these molecular networks determined in O3, and the ISR, as well as their effects on modulating and the immune response. This research will push the frontiers of current knowledge in the biological sciences further, providing a deeper understanding of the role of the ISR in mediating cell phenotype and immune response. Understanding the mechanisms underlying cell phenotype switching has implications for wound healing, metastasis, and tissue engineering. Given the importance of the biological processes being investigated in normal tissue development and malignant progression, I anticipate that this research will provide a rational basis for future studies. Students will gain invaluable experience in the biological, mathematical, and computational sciences and will forge connections with experimentalists from other research institutes. In keeping with the fast-paced growth of the machine learning and artificial intelligence sectors in Canada, this multidisciplinary training will help facilitate the transition of students to careers in both academic and industrial settings, including biotechnology companies.

数学和计算模型在生物学中的应用是应用数学中一个令人兴奋且新颖的研究领域，这种综合方法有助于解释大型数据集并捕捉生命系统固有的复杂性，这有可能彻底改变我们对生命系统的理解。拟议研究计划的长期目标是通过将实验研究与数学和计算技术相结合，促进定量方法在生物研究中的发展和应用，这将使人们对生物系统中的复杂过程有更基本的了解。特别强调组织之间的联系这项研究还将为微环境和细胞表型之间的复杂相互作用以及这些相互作用如何调节细胞谱系提供有价值的见解。四个目标： O1. 使用成像流式细胞术与高性能机器学习架构相结合，开发一种新颖的、无生物标记的细胞表型分类系统（包括上皮细胞和间质细胞）。进行实验研究，检查细胞表型之间的动态以及环境因素（缺氧、代谢）对表型转换的影响，例如在上皮-间质转化中观察到的 O3。多种组织类型的表型，这将通过开发机器学习架构来对来自基因编辑技术（例如 shRNA 和 CRISPR/Cas9）的遗传筛选数据进行分类来完成。开发系统生物学方法来研究这些分子之间的联系。这项研究将进一步推动当前生物科学知识的前沿，提供对 ISR 在介导细胞表型中的作用的更深入的了解。鉴于正在研究的生物过程在正常组织发育和恶性进展中的重要性，我预计这项研究将提供合理的基础。以供将来的学习之用。获得生物、数学和计算科学方面的宝贵经验，并将与其他研究机构的实验人员建立联系，以适应加拿大机器学习和人工智能领域的快速发展，这种多学科培训将有助于促进过渡。学生在学术和工业环境中就业，包括生物技术公司。