Bayesian evidence analysis tools for systems biology

生物系统的研究，从细胞到生物体和种群，正变得越来越量化。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，并开始解开分子之间的联系，这些分子构成了保持细胞功能的途径和过程。我们对分子和基因相互作用的认识来自许多方面。这些包括蛋白质的三维结构的研究，从中可以推断出它们的功能，通过体外和体内研究，显示基因和开关它们的分子如何在试管中相互作用，以及在关键的单细胞生物体中，如酵母，或高等植物如拟南芥。描述分子系统的方式正在从传统的可能相互作用的示意图转变为一组数学方程，将一个分子的变化率与其他分子的数量联系起来。当分子的数量很小时，一组随机反应比一组常微分方程更精确。但在这两种情况下，找到数学模型和实验室数据之间的最佳拟合成为一个主要问题。第二个重要的问题涉及在模拟生物系统时所作决定的合理性。我们可能会说，只有一个模型描述的数据-但这是不可能的任何复杂的系统。相反，我们可以希望证明一个模型比另一个模型更适合数据，这就是这里提出的研究的目的。我们将应用概率方法，可以优化模型对数据的拟合，并定量比较它们拟合数据的程度。这将为实验室生物学家和系统生物学家提供有用的信息，与他们合作，以进一步了解细胞。

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.