Bayesian evidence analysis tools for systems biology

对生物系统的研究，从细胞，到生物体和种群，正在变得越来越量化。即使在单个细胞的水平上，分子生物学家和遗传学家也能够测量蛋白质和RNA等分子的数量，并开始解开构成维持细胞功能的路径和过程的分子之间的联系。我们关于分子和基因相互作用的知识来自许多来源。这些研究包括对蛋白质的三维结构的研究，从蛋白质的三维结构可以推断其功能，一直到体外和体内的研究，展示了基因以及打开和关闭它们的分子如何在试管中以及在关键的单细胞有机体(如酵母)或高等植物(如拟南芥)中相互作用。描述分子系统的方式正在从传统的可能相互作用的图表示意图转变为一套将一个分子的变化率与其他分子的量联系起来的数学方程式。当分子的数量很小时，一组随机反应比一组常微分方程式更能准确地表示。但在这两种情况下，在数学模型和实验室数据之间找到最佳匹配成为一个主要问题。第二个重要问题涉及对生物系统进行建模时所做决策的合理性。我们可能会说，只有一个模型描述了数据--但这对于任何复杂的系统都是不可能的。相反，我们可以希望证明一种模型比另一种模型更适合数据，这就是这里提出的研究的目的。我们将应用一种概率方法，以优化模型对数据的拟合，并定量比较它们与数据的拟合程度。这将为长凳生物学家和系统生物学家提供有用的信息，他们与他们合作，以进一步加深我们对细胞的了解。

The study of biological systems, from cells, to organisms and populations, is becoming increasingly quantitative. Even at the level of a single cell, molecular biologists and geneticists are able to measure amounts of molecules such as proteins and RNAs, and to begin to unravel the connections between molecules that make up the pathways and processes that keep the cell functioning. Our knowledge of the interaction of molecules and genes comes from many sources. These include studies of the three dimensional structure of proteins, from which their function can be inferred, through to in vitro and in vivo studies that show how genes, and the molecules that switch them on and off, interact in the test tube, and in a key single cell organism such as yeast, or higher plant such as Arabidopsis thaliana. The way that molecular systems are described is changing from the traditional diagrammatic sketch of likely interactions, to a set of mathematical equations linking the rates of change of one molecule with the amounts of others. When the number of molecules is small, a set of stochastic reactions becomes a more accurate representation than a set of ordinary differential equations. But in both cases, finding the best fit between a mathematical model and data from the laboratory becomes a major problem. A second important issue concerns the justification for decisions made in modelling a biological system. We might like to say that only one model describes the data - but this is not possible for any complex system. Instead, we can hope to show that one model fits the data better than another, and this is the aim of the research proposed here. We shall apply a probabilistic approach that can optimise the fit of models to data, and quantitatively compare the extent to which they fit the data. This will provide useful information to the bench biologists and the systems biologists with whom they collaborate to further our knowledge of the cell.