Advanced Stochastic Computation for Inference from Tree, Graph and Network Models

用于树、图和网络模型推理的高级随机计算

• 批准号: EP/K01501X/1
• 负责人: Maria De Iorio
• 金额: $ 51.14万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK01501X%2F1
• 关键词: Advanced; Stochastic; Computation; Inference; Tree

As a result of recent experimental advances, large amounts of biological data are becoming available for humans and other organisms. Such data pose inference problems well beyond the capabilities of standard statistical tools. Experimental advances must be accompanied by the development of suitable biostatistical and bioinformatic tools that will make efficient use of the complex data to improve our understanding of the genetic forces shaping the evolution of genome organization. Molecular evolution and comparative genomics are no longer fields where collecting data is the main obstacle to progress. Many probabilistic models proposed in biology try to capture the evolutionary mechanisms and reflect the data generating mechanism. Whilst these models still have limitations, there have been substantial improvements in recent years. We contend that progress is mainly limited by a lack of adequate computational tools for extracting information from existing data and performing inference for complex models. A major bottleneck in the application of probabilistic models to biology is that their calibration is computationally expensive and in many instances not possible using modern techniques. Thus, researchers often prefer to use simple summary statistics to characterize the underlying biological process; this approach is obviously unsatisfactory. E.g., topological summary statistics capture basic characteristics of binary interaction networks but are affected by different types of bias so great, that caution must be taken when drawing conclusions. A big challenge for systems biology nowadays consists in developing statistical and bioinformatics tools within the rigorous framework of probabilistic modelling that will allow for a better and more comprehensive understanding of cellular functions. In the last few decades a wealth of research has been performed on model-based inference for molecular data accompanied by an explosion of research in developing computationally efficient methods to facilitate it. Broadly speaking, there are three main approaches to statistical inference in molecular biology: (i) importance sampling (IS) for likelihood evaluation, (ii) Markov chain Monte Carlo (MCMC) methods (iii) Approximate Bayesian Computation (ABC) . In this proposal we concentrate on a combination of advanced IS (or more generally Sequential Monte Carlo (SMC)) and MCMC methods focussed upon Markov models in genetics and bioinformatics. In particular, building upon these techniques, we aim to develop a general framework for approximate inference which is theoretically sound, computationally feasable and still be able to accurately reflect the complexity of the underlying stochastic model. The main life science application on which we will concentrate are: genealogical trees, protein networks, phylogenetic trees.

由于最近的实验进展，人类和其他生物体可以获得大量的生物学数据。这些数据带来的推理问题远远超出了标准统计工具的能力。实验的进步必须伴随着合适的生物统计学和生物信息学工具的发展，这些工具将有效地利用复杂的数据来提高我们对塑造基因组组织进化的遗传力量的理解。在分子进化和比较基因组学领域，收集数据不再是阻碍进步的主要障碍。生物学中提出的许多概率模型试图捕捉进化机制并反映数据生成机制。虽然这些模型仍然有局限性，但近年来已经有了实质性的改进。我们认为，进展主要受到缺乏足够的计算工具来从现有数据中提取信息和对复杂模型进行推理的限制。概率模型应用于生物学的一个主要瓶颈是，它们的校准在计算上是昂贵的，而且在许多情况下，使用现代技术是不可能的。因此，研究人员往往倾向于使用简单的汇总统计来描述潜在的生物过程；这种方法显然不能令人满意。例如，拓扑摘要统计捕获二元相互作用网络的基本特征，但受不同类型偏差的影响非常大，因此在得出结论时必须谨慎。系统生物学目前面临的一个巨大挑战是在概率模型的严格框架内开发统计和生物信息学工具，这将允许对细胞功能有更好、更全面的理解。在过去的几十年里，人们对基于模型的分子数据推断进行了大量的研究，同时在开发计算效率高的方法来促进这一研究方面也出现了爆炸式的增长。从广义上讲，分子生物学中有三种主要的统计推断方法：(i)重要性抽样（IS）进行似然评估；（ii）马尔可夫链蒙特卡罗（MCMC）方法；（iii）近似贝叶斯计算（ABC）。在本提案中，我们专注于先进的IS（或更一般的顺序蒙特卡罗（SMC））和MCMC方法的组合，重点是遗传学和生物信息学中的马尔可夫模型。特别是，在这些技术的基础上，我们的目标是开发一个近似推理的一般框架，它在理论上是合理的，计算上是可行的，并且仍然能够准确地反映底层随机模型的复杂性。我们将集中讨论的主要生命科学应用有：谱系树、蛋白质网络、系统发育树。

项目成果

A simulation approach for change-points on phylogenetic trees.

系统发育树上变化点的模拟方法。

• DOI: 10.1089/cmb.2014.0218
• 发表时间: 2015
• 期刊: a journal of computational molecular cell biology
• 作者: Persing A

Bayesian Inference for Duplication-Mutation with Complementarity Network Models

使用互补网络模型进行重复突变的贝叶斯推理

• DOI: 10.48550/arxiv.1504.01794
• 发表时间: 2015
• 作者: Jasra A

A simulation approach for change-points on phylogenetic trees

系统发育树变化点的模拟方法

• DOI: 10.48550/arxiv.1408.6317
• 发表时间: 2014
• 作者: Persing A

Bayesian inference for duplication-mutation with complementarity network models.

• DOI: 10.1089/cmb.2015.0072
• 发表时间: 2015-11
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Jasra A;Persing A;Beskos A;Heine K;De Iorio M
• 通讯作者: De Iorio M

Computational Methods for a Class of Network Models

一类网络模型的计算方法

• DOI: 10.1089/cmb.2013.0082
• 发表时间: 2014
• 期刊: Journal of Computational Biology
• 影响因子: 1.7
• 作者: Wang J