Bayesian methods for modelling and integrating metabolic data

用于建模和整合代谢数据的贝叶斯方法

• 批准号: BB/E020372/1
• 负责人: Sylvia Richardson
• 金额: $ 66.38万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE020372%2F1
• 关键词: Bayesian; methods; modelling; integrating; metabolic

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.

生物技术的最新进展使得能够测量从细胞到整个生物体的复杂系统的多种测量。然而，这些技术会产生大量的数据，稳健有效地处理这些数据是一项重要任务。我们的多学科项目的目的是设计方法来联合收割机和分析不同的数据测量产生的实验在现代生物学，最终将有助于了解常见疾病的原因，并导致新的治疗方法的发展。现在可以通过非常详细地测量例如血液或尿液样品的化学成分来研究复杂生物体的功能，并且还可以测量该成分如何随时间变化，或者对不同治疗或实验条件的反应。也许最重要的是，还可以比较可能患有或未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（由缩写NMR和MS已知），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正在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，其对活细胞的功能至关重要。NMR和MS都是非常复杂的测量程序，每种都产生大量的数据（光谱），但是尽管这两种技术的测量包含相同代谢物的一些信息，但两种来源的大多数信息并不相同，重要的统计建模任务涉及以最合理的方式组合它们的数据。我们将这项任务分为两个部分：首先是NMR和MS代谢物光谱的数学建模，其次是两个测量系统的数据组合。这两个组成部分都需要参与我们研究方案的生物学家和统计学家的主要投入。对NMR和MS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。一些数据分析方法已经存在，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，我们将使用关于典型代谢物的物理化学信息来指导我们如何构建模型和进行分析。这种物理化学的“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，以原则性的方式结合数据和先验信息。然而，尽管在科学上很有吸引力，但这种建模方法需要先进的计算方法才能实现分析，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。了解和模拟NMR和MS代谢物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。我们的项目的新奇在于，通过使用贝叶斯分析框架，我们能够利用和纳入这样的专业信息。我们的多学科研究团队结合了建模，统计，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，并促进其结果向广大社区传播。

项目成果

A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures

用于复杂生物混合物中代谢物解卷积和定量的 NMR 谱贝叶斯模型

• DOI: 10.48550/arxiv.1105.2204
• 发表时间: 2011
• 作者: Astle W

MetAssimulo: simulation of realistic NMR metabolic profiles.

• DOI: 10.1186/1471-2105-11-496
• 发表时间: 2010-10-06
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Muncey HJ;Jones R;De Iorio M;Ebbels TM
• 通讯作者: Ebbels TM

A differential network approach to exploring differences between biological states: an application to prediabetes.

• DOI: 10.1371/journal.pone.0024702
• 发表时间: 2011
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Valcárcel B;Würtz P;Seich al Basatena NK;Tukiainen T;Kangas AJ;Soininen P;Järvelin MR;Ala-Korpela M;Ebbels TM;de Iorio M
• 通讯作者: de Iorio M

Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification.

• DOI: 10.1021/pr1003449
• 发表时间: 2010-09-03
• 期刊: JOURNAL OF PROTEOME RESEARCH
• 影响因子: 4.4
• 作者: Chadeau-Hyam, Marc;Ebbels, Timothy M. D.;Brown, Ian J.;Chan, Queenie;Stemler, Jeremiah;Huang, Chiang Ching;Daviglus, Martha L.;Ueshima, Hirotsugu;Zhao, Liancheng;Holmes, Elaine;Nicholson, Jeremy K.;Elliott, Paul;De Iorio, Maria
• 通讯作者: De Iorio, Maria

sdef: an R package to synthesize lists of significant features in related experiments.

• DOI: 10.1186/1471-2105-11-270
• 发表时间: 2010-05-20
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Blangiardo M;Cassese A;Richardson S
• 通讯作者: Richardson S