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和新颖的研究领域。这种综合方法有助于解释大型数据集，并捕捉生命系统的内在复杂性，这有可能彻底改变我们对生物学的理解。拟议的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。这将使生物系统中的复杂过程的更基本的理解，特别强调组织细胞异质性，综合应激反应（ISR）和免疫系统之间的联系。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。 为此，研究有四个目标：O 1。使用成像流式细胞术结合高性能机器学习架构，开发一种新型的无生物标志物的细胞表型分类系统（包括上皮和间充质）。O2.建立一个基于实验研究的房室数学模型，以检查细胞表型之间的动态变化和环境因素（缺氧，代谢）对表型转换的影响，例如，在上皮-间充质转化中观察到的。氧气确定几种组织类型中不同细胞表型存活的分子网络，这将通过开发ML架构来完成，以分类来自基因编辑技术（如shRNA和CRISPR/Cas9）的遗传筛选数据。氧气发展一种系统生物学方法来研究O3中确定的这些分子网络与ISR之间的联系，以及它们对调节和免疫反应的影响。这项研究将进一步推动生物科学当前知识的前沿，更深入地了解ISR在介导细胞表型和免疫应答中的作用。了解细胞表型转换的机制对伤口愈合、转移和组织工程具有重要意义。考虑到生物学过程在正常组织发育和恶性进展中的重要性，我预计这项研究将为未来的研究提供合理的基础。学生将获得生物，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。为了跟上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学生向学术和工业环境（包括生物技术公司）的职业过渡。