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

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的复杂相互作用。在人类中，这些导致器官和其他关键过程的功能/功能障碍，并特别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正在不断产生，但研究人员目前面临的关键挑战之一是如何从从实验和临床试验中收集的大量信息中组织和提取有用的知识。我建议的研究是针对使用数学/计算建模来研究生命科学中出现的问题，以开发模型，揭示导致功能障碍的驱动过程的潜在机制，从而提出可能的破坏性策略。本研究试图通过实验数据与理论和计算方法的协同结合来理解和模拟临床状况和疾病（包括脑积水和肿瘤生长）。术语脑积水代表了以脑室扩张为特征的一系列疾病。遗传因素被认为有助于先天性脑积水的发展；相比之下，获得性脑积水发生在大脑发育之后，可能是由于创伤、出血、感染、肿瘤等多种原因造成的。老年常压脑积水（NPH）患者数量的增加，进一步证实了对这种疾病有更深入了解的必要性。开发一种精确的、定量的模型来模拟脑组织的机械行为，将是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，确定影响脑肿瘤和其他癌性肿瘤治疗反应的主要因素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病因和治疗潜力。生物组织的生物力学行为是建模要求最高和最复杂的问题之一。我在这里的主要研究项目是开发一个全面的定量模型，结合精心设计的基于实验数据的连锁计算模拟序列，来预测神经外科医生经常面对的脑组织在不同负荷情况下的反应。将数学建模、精心设计的实验和新技术的使用仔细结合起来，将使人们更深入地了解产生“常压”脑积水、肿瘤生长和控制以及各种其他疾病等长期难题的潜在机制。

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.