Smith Nicolas P, Crampin Edmund J, Niederer Steven A, Bassingthwaighte James B, Beard Daniel A
University Computing Laboratory, University of Oxford, Oxford, OX1 3QD, UK.
J Exp Biol. 2007 May;210(Pt 9):1576-83. doi: 10.1242/jeb.000133.
Predicting information about human physiology and pathophysiology from genomic data is a compelling, but unfulfilled goal of post-genomic biology. This is the aim of the so-called Physiome Project and is, undeniably, an ambitious goal. Yet if we can exploit even a small proportion of the rich and varied experimental data currently available, significant insights into clinically important aspects of human physiology will follow. To achieve this requires the integration of data from disparate sources into a common framework. Extrapolation of available data across species, laboratory techniques and conditions requires a quantitative approach. Mathematical models allow us to integrate molecular information into cellular, tissue and organ-level, and ultimately clinically relevant scales. In this paper we argue that biophysically detailed computational modelling provides the essential tool for this process and, furthermore, that an appropriate framework for annotating, databasing and critiquing these models will be essential for the development of integrative computational biology.
从基因组数据预测人类生理学和病理生理学信息是后基因组生物学一个引人关注但尚未实现的目标。这是所谓生理组计划的目标，不可否认，这是一个雄心勃勃的目标。然而，如果我们能够利用哪怕一小部分目前可用的丰富多样的实验数据，就将对人类生理学的临床重要方面有重大见解。要实现这一点，需要将来自不同来源的数据整合到一个通用框架中。跨物种、实验室技术和条件对现有数据进行外推需要一种定量方法。数学模型使我们能够将分子信息整合到细胞、组织和器官层面，并最终整合到临床相关层面。在本文中，我们认为具有生物物理细节的计算建模为这一过程提供了必不可少的工具，此外，为这些模型进行注释、建立数据库和进行评判的适当框架对于整合计算生物学的发展至关重要。
