Predicting properties of biological networks from noisy and incomplete data

网络旨在将不同对象(或代理)之间的交互和依赖关系置于单个连贯的上下文中。它们的分析在不同的科学学科中引起了极大的关注，因为它们提供了复杂现象的图示，而且它们还经常允许对这些现象进行详细的数学分析。不幸的是，观察到的网络往往与真实网络非常不同，因为我们不能可靠地测量所有交互作用。此外，通常只考虑网络的一小部分。这两个因素都会影响我们可靠地解释网络数据的能力。对于许多生物网络数据集来说，情况尤其如此。申请者小组开发了一系列数学工具，使我们能够研究这些来源或错误对我们分析的影响，并在一定程度上克服它们施加的限制。在拟议的研究中，我们将调整这些数学方法，以便它们可以应用于生物网络，特别是蛋白质相互作用网络数据。这将涉及建立用于获得蛋白质相互作用数据的不同实验方法的详细模型。通过模拟实验，我们可以详细研究误差的影响(和原因)，并利用这一点来深入了解不同数据集的可靠性。通过更好地理解噪声和不完备性对实验数据集的影响，我们可以尝试预测真实(但部分未观察到)网络的属性。我们将利用这一点来预测不同物种中相互作用网络的规模：现在已知，基因的数量与我们对不同生物的相对复杂性的理解不太相关(例如，人类基因的数量不到果蝇基因数量的两倍)。在拟议的研究过程中开发的统计预测程序将使我们能够推断不同物种之间相互作用网络的大小，从而使我们能够了解网络的复杂性是否有助于解释不同物种之间生物复杂性的差异。最后，我们将研究新的、更现实的蛋白质相互作用网络模型。

Networks aim to put interactions and dependencies among different objects (or agents) into a single coherent context. Their analysis has attracted great attention in different scientific disciplines because they offer a pictorial representation of complex phenomena, and they frequently also allow a detailed mathematical analysis of these phenomena. Unfortunately, observed networks are often very different from the true network because we cannot measure all interactions reliably. Moreover frequently only some small part of the network is considered. Both factors affect our ability to interpret network data reliably. This is especially true for many biological network datasets. The applicants group has developed a range of mathematical tools that allow us to study the effects these sources or error have on our analysis, and to overcome the limitations imposed by them to some extent. In the proposed research we will adapt these mathematical methods so that they can be applied to biological networks, in particular protein-interaction network data. This will involve the formulation of detailed models of the different experimental methods used to obtain protein interaction data. By simulating the experiment we can study the effects (and causes) of error in detail and use this to gain insights into the reliability of different datasets. With this better understanding of the effects of noise and incompleteness on experimental datasets we can then try to predict properties of the true (but partially unobserved) network. We will use this to predict the size of interaction network in different species: it is now known that the number of genes does not correlate well with our understanding of the relative complexity of different organisms (for example the number of human genes is less than twice the number of genes in the fruitfly). The statistical prediction procedures to be developed in the course of the proposed research will allow us to infer the sizes of the interaction networks in different species and will therefore enable us to see if the complexity of the network could help to explain the differences in biological complexity between different species. Finally, we will study new and more realistic models for protein interaction networks.