Bayesian methods for modelling and integrating metabolic data

生物技术的最新进展使从细胞到整个生物体的复杂系统的多重测量成为可能。然而，这些技术产生了大量的数据，如何稳健高效地处理这些数据是一个重要的任务。我们的多学科项目的目的是设计方法来结合和分析现代生物学实验中产生的不同数据测量结果，最终有助于理解常见疾病的原因，并导致新治疗方法的发展。现在有可能通过非常详细地测量例如血液或尿液样本的化学成分来研究复杂生物体的功能，也可以测量这些成分随时间的变化，或对不同处理或实验条件的反应。也许最重要的是，还可以比较可能患有或不患有特定疾病的不同群体的组成，并利用这种比较来了解如何开发治疗方法。然而，只有尽可能准确地收集和分析实验数据，才能实现这一令人兴奋的前景。这是我们研究的主要目标。我们将专注于所谓的“代谢”分析，使用两种特定类型的技术（缩写为NMR和MS），使我们能够测量大量不同的化学物质（或代谢物）的量，这些化学物质存在于被分析的血液或其他体液样本中。代谢物是存在于所有生物体中的小分子，对其活细胞的功能至关重要。核磁共振和质谱都是非常复杂的测量程序，每个都产生大量的数据（光谱），但是，尽管两种技术的测量包含相同代谢物的一些信息，但来自两种来源的大多数信息并不相同，一个重要的统计建模任务涉及以最合理的方式组合来自它们的数据。我们将把这项任务分成两个部分；首先，对核磁共振和质谱代谢物谱进行数学建模，其次，将两种测量系统的数据结合起来。这两个组成部分都需要参与我们研究计划的生物学家和统计学家的大量投入。对核磁共振和质谱技术产生的大量数据进行统计分析是一项极具挑战性的任务。一些数据分析的方法确实已经存在，但它们并没有使用手头的所有信息。我们的方法的一个重要优势是，我们将使用已经获得的关于典型代谢物的物理化学信息来指导我们如何建立我们的模型并进行我们的分析。这种物理化学“先验”信息很少用于代谢物数据的分析，但我们认为它为如何进行分析提供了重要的指导。因此，我们将采用贝叶斯统计方法，以一种原则的方式将数据和先验信息结合起来。然而，尽管在科学上很有吸引力，这种建模方法需要先进的计算方法，以便分析可以实施，我们将开展的研究的一个主要组成部分将是实施最有效的计算策略。理解和模拟NMR和MS代谢物光谱的内容是一项复杂的任务，需要高度专业化的化学知识和最先进的统计技术。我们项目的新颖之处在于，通过使用贝叶斯分析框架，我们能够利用和合并这些专业信息。我们的多学科研究团队结合了建模、统计学、化学生物学和生物信息学方面的专业知识，将确保我们的研究计划取得成功，并促进其成果向更广泛的社区传播。

Recent advances in biological technology enable the measurement of multiple measures of complex systems from the cell to the whole organism. However, these technologies generate massive amount of data and it is a major task to process these robustly and efficiently. The aim of our multidisciplinary project is to devise methods to combine and analyze different data measurements arising from experiments in modern biology that will ultimately aid in the understanding of the causes of common diseases, and lead to the development of new treatments. It is now possible to investigate how complex organisms function by measuring in great detail the chemical composition of, for example, a sample of blood or urine, and also to measure how that composition changes over time, or in reaction to different treatments or experimental conditions. Perhaps most importantly, it is also possible to compare the composition across different groups that may have or not have a particular disease, and to use this comparison to understand how treatments might be developed. This exciting prospect can only be achieved, however, if the experimental data are collected and analyzed as accurately possible. This is the principal goal of our research. We will focus on so-called 'metabolic' analysis using two specific types of technology (known by the initials NMR and MS) that allow us to measure the amount of a large number of different chemicals (or metabolites) that are present in the samples of blood or other body fluids being analyzed. Metabolites are small molecules present in all organisms which are essential to the functioning of their living cells. NMR and MS are both extremely sophisticated measurement procedures that each produce a large amount of data (spectra), but although the measurements from the two technologies contain some information on the same metabolites, most of the information from the two sources is not identical, and an important statistical modelling task involves combining data from them in the most sensible fashion. We will separate this task into two components; first, the mathematical modelling of the NMR and MS metabolite spectra, and secondly the combination of the data across the two measurement systems. Both components require major input from both biologists and statisticians involved in our research programme. The statistical analysis of the large amounts of data generated by NMR and MS technologies is an extremely challenging task. Some methods for data analysis do already exist, but they do not use all the information at hand. An important advantage of our approach is that we will use physico-chemical information already available about typical metabolites to direct how we build our models and carry out our analysis. Such physico-chemical 'prior' information has been only rarely used in the analysis of metabolite data, but we feel that it provides an important guide as to how analysis should proceed. Thus we will adopt a Bayesian statistical approach that combines data and prior information in a principled fashion. However, despite being scientifically attractive, this modelling approach needs advanced computing methods so that the analysis can be implemented, and a major component of the research we will carry out will be to implement the most efficient computational strategies. Understanding and modelling the content of NMR and MS metabolite spectra is a complicated task that requires both highly specialized chemical knowledge and state of the art statistical techniques. The novelty of our project is that by using a Bayesian analysis framework we are able to harness and incorporate such specialist information. Our multidisciplinary research team that combines expertise in modelling, statistics, chemical biology and bioinformatics will ensure the success of our research programme and facilitate the dissemination of its results to a wide community.