An Integrative Systems Biology approach to define the divergent kinetic responses of S. cerevisiae and C. albicans to amino acid starvation

微生物如果要在这些变化中生存下来，就必须适应环境的快速变化。例如，微生物必须能够调整它们的新陈代谢以利用可用的营养物质，并且当这些营养物质耗尽时，它们必须适应营养限制。我们正在比较两种不同的酵母如何适应一种特定类型的营养限制（氨基酸饥饿）。我们正在研究面包酵母（酿酒酵母），因为它是现有的最好的模式生物之一，而且因为已经有了一个关于酵母氨基酸饥饿反应的强大知识平台，使我们能够建立这种反应的数学模型。我们将面包酵母与白色念珠菌进行比较，因为这是一种医学上重要的人类病原体，经常引起口腔和阴道感染（鹅口疮），并在重症监护患者中引起危及生命的血液感染。显然，这些酵母在非常不同的生态位中进化。然而，我们已经证明，致病性酵母白色念珠菌对氨基酸饥饿的反应与面包师的酵母大致相同。然而，它们的反应调节方式有显著差异。因此，这些酵母似乎对这个问题保留了类似的解决方案（它们都通过代谢产生更多的氨基酸来克服氨基酸的短缺），但在调节它们的适应性反应的控制系统上存在差异。这两种酵母都必须对最初的营养饥饿迅速作出反应，但随着氨基酸通过新陈代谢变得可用，这种饥饿反应会慢慢停止。因此，必须长期有效地管理这两种酵母的反应，即使它们的控制系统不同。我们的目标是描述这些有趣的差异，因为它们将告诉我们这些控制系统是如何在这些酵母中进化的。我们的方法包括建立一个数学模型，可以定量地描述氨基酸饥饿反应，并且可以准确地预测对新实验条件的反应。我们已经建立了一个初步模型。在这个项目中，我们将优化面包师酵母的模型，然后建立一个致病酵母的等效模型。这些数学模型将非常有用，因为它们将使我们能够（在计算机上）快速模拟大量在实验室中无法进行的实验。这将使我们能够在实验室里把精力集中在那些可能最有意思和最有信息量的实验上。通过这种方式，我们将描述这两种酵母控制系统之间的差异。这将产生有关这些控制系统如何演变的信息，这将提供有关微生物控制系统一般演变的宝贵信息。

Microbes must adapt to rapid changes in their environment if they are to survive these changes. For example, microbes must be able to adapt their metabolism to use the available nutrients, and they must adapt to nutrient limitation as these nutrients become exhausted. We are comparing how two different yeasts adapt to a particular type of nutrient limitation (amino acid starvation). We are studying bakers' yeast (Saccharomyces cerevisiae) because it is one of the best model organisms available, and because there is already a strong platform of knowledge about the amino acid starvation response in this yeast that has allowed us to build a mathematical model of this response. We are comparing bakers' yeast to Candida albicans because this is a medically important pathogen of humans that frequently causes infections in the mouth and vagina (thrush) and causes life-threatening bloodstream infections in intensive care patients. Clearly these yeasts have evolved in very different niches. Nevertheless we have shown that the pathogenic yeast C. albicans responds in roughly the same way as bakers' yeast to amino acid starvation. However, there are significant differences in the way their responses are regulated. Hence these yeasts appear to have retained a similar solution to the problem (they both make more amino acids via metabolism to overcome the shortage of amino acids), but there are differences in the control systems that regulate their adaptive responses. Both yeasts must respond rapidly to the initial nutrient starvation, but slowly turn off this starvation response as amino acids become available through metabolism. Therefore, the responses in these two yeasts must be effectively managed over time, even though their control systems differ. Our aim is to characterise these interesting differences because they will tell us about how such control systems have evolved in these yeasts. Our approach includes the building of a mathematical model that can describe the amino acid starvation response quantitatively, and that can accurately predict responses to novel experimental conditions. We have built a preliminary model. In this project we will optimise this model for bakers' yeast, and then build an equivalent model for the pathogenic yeast. These mathematical models will be very useful because they will allow us to rapidly simulate (on the computer) large numbers of experiments that are impractical to perform in the lab. This will allow us to focus our efforts in the laboratory on those experiments that are likely to be most interesting and informative. In this way we will characterise the differences between the control systems in these two yeasts. This will generate information about how these control systems have evolved, which will provide valuable messages about the evolution of microbial control systems in general.