Multiscale mathematical modelling in biology and medicine
Multiscale mathematical modelling in biology and medicine
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DOI：
10.1093/imamat/hxr025
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发表时间：
2011-06-01
影响因子：
1.2
通讯作者：
Chaplain, Mark A. J.
中科院分区：
文献类型：
--
作者：
Chaplain, Mark A. J.
癌症是世界上（特别是发达国家）的主要死亡原因之一，每年约有1100万人被诊断出癌症，约700万人死亡。世界卫生组织预测，目前的趋势显示，2015年将有约900万人死亡，2030年将增至1150万人。癌症生长是一种复杂的现象，涉及广泛的空间和时间尺度上的许多相互关联的过程，尽管取得了许多进展，但正如以前的统计数据所显示的那样，仍然难以治疗和治愈。如果要在治疗这种疾病方面取得进一步进展，就必须采取新的方法。对人体内大多数生物过程的描述涉及许多不同但相互关联的现象，这些现象发生在不同的空间和时间尺度上。从建模的角度来看，有三个感兴趣的自然尺度：亚细胞，细胞和组织。本文描述的建模有一个共同的主题，即对癌症生长和治疗的关键方面进行定量预测数学建模、分析和计算模拟。长期目标是建立一个“由不同生物尺度（从基因到组织再到器官）的不同但相互关联的数学模型组成的虚拟癌症”。定量预测模型的开发（基于可靠的生物学证据，并由生物学数据支持和参数化）无疑将通过改善临床治疗对癌症等疾病的患者产生积极影响。
Cancer is one of the major causes of death in the world (particularly the developed world), with around 11 million people diagnosed and around 7 million people dying each year. The World Health Organization predicts that current trends show around 9 million people will die in 2015, with the number rising to 11.5 million in 2030. Cancer growth is a complicated complex phenomenon involving many interrelated processes across a wide range of spatial and temporal scales, and in spite of many advances, it is still difficult to treat and cure as the previous statistics show. New approaches are necessary if further progress in curing the disease is to be made. The description of most biological processes in the human body involves many different but interconnectedphenomena, which occur at different spatial and temporal scales. From the modelling viewpoint, there are three natural scales of interest: subcellular, cellular and tissue. The modelling described in this paper has a common theme of quantitativepredictive mathematical modelling, analysis and computational simulation of key aspects of cancer growth and treatment. The long-term goal is to build a 'virtual cancer made up of different but connected mathematical models at the different biological scales (from genes to tissue to organ)'. The development of quantitative predictive models (based on sound biological evidence and underpinned and parameterized by biological data) will no doubt have a positive impact on patients suffering from diseases such as cancer through improved clinical treatment.