The Mathematical and Computational Modelling of Various Problems in the Life Sciences

生命科学中各种问题的数学和计算建模

• 批准号: RGPIN-2020-05115
• 负责人: Sivaloganathan, Sivabal
• 金额: $ 1.75万
• 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=740400
• 关键词: Mathematical; Computational; Modelling; Various; Problems

Many processes in the Life Sciences involve a complex interplay between a multi-scale cascade of physiological processes and pathways. In humans, these result in function/dysfunction of organs and other crucial processes, and lead in particular to the manifestation of particular medical conditions or diseases. Whilst enormous amounts of experimental and clinical data are being continuously generated, one of the crucial challenges facing researchers currently, is how to organize and extract useful knowledge from the vast array of information gathered from experiments and clinical trials. My proposed research is directed at using mathematical/computational modeling to study problems arising in the life sciences in order to develop models that reveal underlying mechanisms driving processes that lead to dysfunction, and hence suggest possible disruptive strategies. This research attempts to understand and model clinical conditions and diseases (including hydrocephalus and tumor growth) through a synergistic combination of experimental data and theoretical and computational methods. The term hydrocephalus represents a family of disorders characterized by expansion of the ventricles within the brain. Genetic factors are believed to contribute to the development of congenital hydrocephalus; in contrast, acquired hydrocephalus occurs after development of the brain and can be due to many causes such as trauma, hemorrhage, infection, tumors. The increasing numbers of aging patients with Normal Pressure Hydrocephalus (NPH), further substantiates the need for a deeper understanding of this disorder. The development of an accurate, quantitative model for simulating the mechanical behavior of brain tissue, will be an important milestone in the clinical neurosciences since it will help in predicting the damage sustained by the brain in a variety of circumstances and guide treatment decisions. In addition, the identification of major factors that influence treatment response in brain tumors and other cancerous tumors has been a problem of longstanding interest and relevance to clinical oncology. Fresh approaches are needed to understand the causes and therapeutic potential of this deadly disease, of hydrocephalus and other conditions. The biomechanical behavior of biological tissues is one of the most demanding and complicated to model. The main thrust of my research program here, is to develop a comprehensive quantitative model, coupled with carefully designed interlocking sequence of computational simulations based on experimental data, to predict the response of brain tissue in vastly different loading situations as regularly facing the neurosurgeon. The careful coupling of mathematical modeling, well designed experiments and the use of novel technologies will lead to a deeper understanding of the underlying mechanisms that give rise to long standing puzzles such as "normal pressure" hydrocephalus, growth and control of tumors and various other conditions.

生命科学中的许多过程涉及生理过程和途径的多尺度级联之间复杂的相互作用。在人类中，这些会导致器官和其他关键过程的功能/功能障碍，特别是导致特定医疗状况或疾病的表现。虽然大量的实验和临床数据不断产生，但研究人员目前面临的关键挑战之一是如何从实验和临床试验收集的大量信息中组织和提取有用的知识。我提出的研究旨在使用数学/计算模型来研究生命科学中出现的问题，以便开发模型来揭示驱动导致功能障碍的过程的潜在机制，从而提出可能的破坏性策略。该研究试图通过实验数据与理论和计算方法的协同组合来理解和模拟临床状况和疾病（包括脑积水和肿瘤生长）。脑积水一词代表一类以脑内脑室扩张为特征的疾病。人们认为遗传因素会导致先天性脑积水的发生；相反，获得性脑积水发生在大脑发育之后，可能由多种原因引起，例如外伤、出血、感染、肿瘤。患有正常压力脑积水 (NPH) 的老年患者数量不断增加，进一步证明需要更深入地了解这种疾病。开发用于模拟脑组织机械行为的准确定量模型将成为临床神经科学的一个重要里程碑，因为它将有助于预测大脑在各种情况下所遭受的损伤并指导治疗决策。此外，影响脑肿瘤和其他癌性肿瘤治疗反应的主要因素的识别一直是临床肿瘤学长期关注和相关的问题。需要新的方法来了解这种致命疾病、脑积水和其他疾病的原因和治疗潜力。生物组织的生物力学行为是建模要求最高且最复杂的行为之一。我的研究计划的主要目标是开发一个全面的定量模型，加上基于实验数据精心设计的计算模拟的连锁序列，以预测脑组织在神经外科医生经常面临的截然不同的负载情况下的反应。数学模型、精心设计的实验和新技术的使用的仔细结合将导致对引起长期谜题的潜在机制有更深入的了解，例如“正常压力”脑积水、肿瘤的生长和控制以及各种其他病症。