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，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）。术语脑积水代表以脑内脑室扩张为特征的疾病家族。遗传因素被认为有助于先天性脑积水的发展;相反，后天性脑积水发生在大脑发育之后，可能是由于许多原因，如创伤，出血，感染，肿瘤。越来越多的老年患者患有正常压力脑积水（NPH），进一步证实了对这种疾病更深入了解的必要性。开发一个精确的，定量的模型来模拟脑组织的力学行为，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在各种情况下大脑所遭受的损伤，并指导治疗决策。此外，识别影响脑肿瘤和其他癌性肿瘤的治疗反应的主要因素一直是一个长期感兴趣的问题，并与临床肿瘤学相关。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和最复杂的行为之一。我在这里的研究计划的主旨，是开发一个全面的定量模型，再加上精心设计的联锁序列的计算模拟的基础上实验数据，预测脑组织的反应在非常不同的负载情况下，经常面临的神经外科医生。数学建模，精心设计的实验和新技术的使用的精心耦合将导致对引起长期困扰的潜在机制的更深入的理解，如“正常压力”脑积水，肿瘤的生长和控制以及各种其他条件。

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.