Advancing Bayesian network algorithms for inferring gene regulation using an integrative computational-biological approach in a yeast model system

最近，收集生物学中的大量数据已经成为可能，例如，测量酵母中每个基因的表达水平。生物学中的大量数据刺激了分析这些数据的计算工具的发展。这样的数据和计算工具使我们能够比以前更广泛地看待生物学：我们可以检查大量相互作用的元素，而不是只对少数几个进行定向实验，从而能够调查整个系统的行为。这类工作的一个领域是使用计算算法来揭示基因调控网络。基因调控是指一种蛋白质--被称为调节器--与基因附近的DNA结合，并影响该基因的表达方式，增加或减少RNA的生成量。然后，这种RNA被用来制造基因的蛋白质产物。因此，基因附近的调节子的结合最终会影响基因制造的蛋白质的数量。该调节因子也是一种蛋白质，因此也是由一种制造RNA的基因产生的。事实上，监管机构可以有一个自己的监管机构。基因调控网络是由蛋白质组成的网络，这些蛋白质是其他蛋白质的调节者，这些蛋白质要么在细胞中执行某些功能，要么是更多蛋白质的调节者。尽管调控网络包括从基因到RNA和从RNA到蛋白质的步骤，但目前的算法只使用来自RNA的数据，而不使用蛋白质。这主要是因为RNA测量更容易，因此数据是可用的。然而，蛋白质测量正在改进，考虑RNA到蛋白质的转变可能很重要，因为调控也可能在这一步发生。在这里，我们建议通过包括蛋白质数据来改进揭示基因调控网络的算法。此外，还有许多其他信息可能有助于我们弄清楚基因调控网络：发现调节器与DNA结合的位置，我们知道调节器结合的DNA序列附近有哪些基因，哪些蛋白质相互结合，以及当另一个基因被操纵时，哪些基因改变了表达。我们还将把所有这些信息添加到算法中，努力最大限度地利用可用的信息来准确预测基因调控网络。但是，制造一个应该做事情的算法并不是故事的全部--我们还必须测试它。我们将通过两种方式测试我们开发的算法。首先，我们将使用一个模拟，在那里我们组成一个基因调控网络，从它那里采集数据，就像我们在做生物实验一样--但在计算机中，然后看看算法是否能计算出我们建立的基因调控网络。这一步帮助我们找出我们在哪里做对了，算法什么时候找到了正确的网络，我们哪里做错了，算法什么时候出错。然后我们可以致力于修复算法，以减少错误。其次，我们将采用我们在模拟器中测试的算法，并尽可能地改进，并将其应用于生物实验室中从酵母中提取的数据。该算法将输出一个网络，显示它根据数据预测的基因调控网络。然后，我们将挑选这个网络的片段，如调节器和基因对，在我们自己的酵母实验中进行测试。这些测试将告诉我们算法是否做出了准确的预测。这种类型的验证虽然很重要，但很少执行，因为做算法的人通常不同于做生物的人。因此，拟议的研究满足了这一经常被忽视的需求。这项研究的最终目标是产生一种能够很好地预测基因调控网络的算法。一旦我们有了这个算法，未来的研究就可以用它来测量基因调控网络并研究它们的特征。特别是，我们计划在未来的项目中使用这里产生的算法来研究基因调控网络的进化。

Recently it has become possible to collect large amounts of data in biology, for example, measuring the expression level of every gene in yeast. This large amount of data in biology has spurred development of computational tools to analyse it. Such data and computational tools enables us to look at biology at a broader level than previously possible: we can examine a large number of interacting elements, instead of doing directed experiments on only a few, enabling investigation into how the entire system behaves. One area of such work is to use computational algorithms to reveal gene regulatory networks. Gene regulation is when a protein--known as a regulator--binds to the DNA near a gene and affects how that gene expressed, either increasing or decreasing the amount of RNA produced. This RNA is then used to make the protein product of the gene. So the binding of the regulator near the gene ultimately affects the amount of protein the gene makes. The regulator is also a protein, and thus was also produced by a gene making RNA making protein. In fact, the regulator could have a regulator of its own. A gene regulatory network is a network formed by proteins that are regulators for other proteins, which either perform some function in the cell or are regulators for yet more proteins. Even though a regulatory network consists of steps going from genes to RNA and RNA to protein, current algorithms use data from only RNA, not proteins. This is mostly because RNA measurement is easier, and thus data is available. However, protein measurement is improving, and it may be important to consider the RNA to protein transition, as regulation could occur at this step too. Here, we propose to improve algorithms that reveal gene regulatory networks by including protein data. Additionally, there is a lot of other information available that might help us figure out the gene regulatory network: locations where regulators have been found to bind to DNA, what genes are near DNA sequences to which we know regulators bind, what proteins bind to each other, and what genes changed expression when another gene was manipulated. We will also add all of these pieces of information into the algorithm, in an effort to take maximal advantage of the available information to accurately predict gene regulatory networks. But making an algorithm that ought to do things is not the whole story--we also have to test it. We will test the algorithms we develop in two ways. First, we will use a simulation, where we make up a gene regulatory network, sample data from it like we are doing a biological experiment--but in the computer, and then see if the algorithm can figure out the gene regulatory network we made. This step helps us figure out where we got things right, when the algorithm finds the correct network, and where we got things wrong, when the algorithm makes mistakes. We can then work on fixing the algorithm to make fewer mistakes. Second, we will take the algorithm we have tested in the simulator, and made as good as we can, and apply it to data taken from yeast in biological laboratory. The algorithm will output a network showing what it predicts to be the gene regulatory network based on the data. We will then pick pieces of this network, such as a regulator and gene pair, to test in our own yeast experiment. These tests will tell us if the algorithm is making accurate predictions or not. This type of validation, while important, is rarely performed because different people usually make the algorithms than do the biology. Thus, the proposed research meets this often-missed need. The ultimate goal of this research is to produce an algorithm that does a good job of predicting gene regulatory networks. Once we have this algorithm, future research can use it to measure gene regulatory networks and study their features. In particular, we plan to use the algorithm produced here to study the evolution of gene regulatory networks in future projects.