BioCmp: Reconstructing Metabolic and Transcriptional Networks using Bayesian State Space Models

BioCmp：使用贝叶斯状态空间模型重建代谢和转录网络

• 批准号: 0524331
• 负责人: David Wild
• 金额: --
• 依托单位: Keck Graduate Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0524331&HistoricalAwards=false
• 关键词: BioCmp; Reconstructing; Metabolic; Transcriptional; Networks

Better understanding of the processes involved in the physiology of bacteria can potentially have tremendous impact on both therapeutic approaches to infectious diseases and metabolic engineering applications in biotechnology.In this project, Drs. David L. Wild and Matthew J. Beal, of the Keck Graduate Institute in Claremont, California and the State University of New York in Buffalo, New York, respectively, are proposing to build statistical models of time series data, with a view to leveraging sophisticated Bayesian methods to "reverse-engineer" an organism's complex genetic regulatory networks from the raw measurements of gene expression and metabolite concentration.Drs. Wild and Beal will apply their techniques to an ideal experimental system: the response of the bacterium E coli to acid stress, which enables pathogenic E. coli to survive passage through the acidic environment of the stomach and gastro-intestinal tract. They will collaborate with experimentalists Drs. Francesco Falciani and Mark Viant at the University of Birmingham, UK, who will provide data from both pathogenic and non-pathogenic strains of this bacterium. Predictions made by Wild and Beal's models can then be tested and explored back in the laboratory.Recent advances in functional genomics technologies have given biologists unprecedented access to measurements of the inner workings of complex biological organisms. Using microarray expression profiling, it is now possible to measure the expression levels of tens of thousands of genes in just a single biological experiment, conducted over several days in the form of a time series. Contrast this to the situation only ten years ago when it was rather unusual for a biologist to measure the expression of more than just one or two carefully chosen genes. As well as high-throughput gene expression methods, the new technology of "metabolomics" has opened the door to measuring even more information in the form of the concentration of hundreds of metabolites that are also crucial players in the complex cellular processes under study.This overwhelming amount of data challenges traditional methods of analysis, especially when one considers the element of time, because now one must consider how certain genes regulate the expression of other genes from one time point in the experiment to the next. A key ingredient in Drs. Wild and Beal's models is the inclusion of "hidden factors" that help to explain the correlation structure of the observed measurements. These factors may correspond to unmeasured quantities that were not captured during the experiment and often reduce the number of direct gene-to-gene dependencies, leaving the resulting networks much more interpretable for the biologist. A natural question arises: how many hidden factors should be used to account for the dependencies in the observed data? This is answered by employing Bayesian model selection, a well-founded principle used in machine learning and statistics to choose between models of differing complexities. Their models also use a technique called Automatic Relevance Determination to further simplify the models so that only those genes and metabolites that are participating players in the process are retained in the final model.Another advantage of the Bayesian framework is that existing information about known network connections and interactions, derived from the literature or commercial databases, can be included in the model. The output of the modeling procedure is a probabilistic reckoning of which genetic regulatory networks are plausible or not. These probabilities can be used to design future biological experiments targeted at specific genes, with a view to corroborating the model's in silico predictions or to simply probe a relatively uncharted network.

更好地了解细菌生理过程可能对传染病的治疗方法和生物技术中的代谢工程应用产生巨大影响。Wild和Matthew J.比尔分别来自加州州克莱蒙的Keck研究所和纽约州布法罗的纽约州立大学，他们提议建立时间序列数据的统计模型，以期利用复杂的贝叶斯方法进行“逆向工程”通过基因表达和代谢物浓度的原始测量来了解生物体复杂的基因调节网络。怀尔德博士和比尔博士将适用他们的技术是一个理想的实验系统：大肠杆菌对酸胁迫的反应，这使得致病性大肠杆菌。大肠杆菌在胃和胃肠道的酸性环境中存活。他们将与英国伯明翰大学的实验学家Francesco Falciani和Mark Viant博士合作，他们将提供这种细菌的致病性和非致病性菌株的数据。然后，怀尔德和比尔模型做出的预测可以在实验室中进行测试和探索。功能基因组学技术的最新进展为生物学家提供了前所未有的机会来测量复杂生物有机体的内部运作。使用微阵列表达谱，现在可以在一个单一的生物实验中测量数万个基因的表达水平，以时间序列的形式进行了几天。这与十年前的情况形成了鲜明的对比，当时生物学家测量不止一两个精心挑选的基因的表达是相当罕见的。 除了高通量基因表达方法外，“代谢组学”新技术还为测量数百种代谢物浓度形式的更多信息打开了大门，这些代谢物在所研究的复杂细胞过程中也是至关重要的参与者。大量的数据挑战了传统的分析方法，特别是当考虑到时间因素时，因为现在我们必须考虑从实验的一个时间点到下一个时间点，某些基因如何调节其他基因的表达。怀尔德博士和比尔博士的模型中的一个关键因素是包含了“隐藏因素”，这些因素有助于解释观察到的测量结果的相关结构。 这些因素可能对应于在实验过程中没有捕获的未测量的数量，并且通常减少了直接基因对基因依赖性的数量，从而使生物学家更容易解释所产生的网络。 一个自然的问题出现了：应该使用多少隐藏的因素来解释观察到的数据中的依赖关系？这是通过使用贝叶斯模型选择来回答的，贝叶斯模型选择是机器学习和统计学中用于在不同复杂性的模型之间进行选择的一个有充分依据的原则。 他们的模型还使用了一种称为自动相关性确定的技术来进一步简化模型，以便在最终模型中只保留参与该过程的基因和代谢物。贝叶斯框架的另一个优点是，来自文献或商业数据库的有关已知网络连接和相互作用的现有信息可以包含在模型中。 建模过程的输出是对哪些基因调控网络是合理的或不合理的概率推算。这些概率可用于设计未来针对特定基因的生物实验，以证实模型的计算机预测或简单地探测相对未知的网络。