Inference-based Modelling in Population and Systems Biology

群体和系统生物学中基于推理的建模

• 批准号: BB/G007934/1
• 负责人: Michael Stumpf
• 金额: $ 39.76万
• 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: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FG007934%2F1
• 关键词: Inference; based; Modelling; Population; Systems

Increasing amounts of biological data are being generated and collected which describe the change of biological systems over time. In systems biology, for instance, it is now normal practice to screen the interactions among a large number of molecules using automated techniques. To interpret such data we are more and more reliant on mathematical models. Such models summarise the way we think biological systems work. Often, we do not know with certainty how biological systems work and what mechanisms operate, and there are often many different models that could describe a given biological system. To find out which model is best, or which mechanism is most likely, one needs to collect data and compare the output of the models with the data. We propose to develop techniques to carry out such an analysis to select models and make conclusions about biological systems. Here we will use concepts from the theory of dynamical systems and statistical inference, combine them in novel ways and develop them for the analysis biological systems in ecology and systems biology, respectively. We will then apply these techniques to different biological questions. The mathematical models and the tools needed to do this are very similar in population biology and in systems biology, and we have therefore selected a mixture of applications form population biology and systems biology. The art to compare different mathematical models in describing data from biological systems and processes is thus of utmost importance for the future development of the modern life- and biomedical sciences. This problem has been studied and practiced before by many others, but the present study introduces a novel element to this field. A model normally consists of two parts: it has a mathematical structure, which specifies which parts of a system interact; and secondly, it has a set of variables, which specify how much the various parts interact (called the model parameters, e.g. kinetic rate constants). The model structure is often 'guessed' or hypothesized, and these hypotheses tested by performing experiments; the model parameters are often inferred from experimental data but some model parameters can be very hard to estimate. While it had previously been thought that not being able to estimate the parameters with certainty makes the analysis of biological processes difficult, if not impossible, recent research - including research done by the three groups that propose to do this research - has shown that substantial progress can be made even without knowing this. This is because (i) if a parameter is hard to estimate, it is often because it has little impact on how the system works, and (ii) by integrating over all the possible parameters of parameters that are not known with certainty one can get a very good understanding of how the system works. Even when such approaches do not yield definitive answers as to how biological systems work, they can help us to make design better experiments or point to data that ought to be collected in order to be most informative. The statistical tools that will be developed during the course of this project will be applied to datasets from a diverse range of biological systems. Together with experimental research collaborators we will explore how well these novel techniques work, and explore the new insights that we hope to get by using such techniques. The biological systems that we will study are: plankton in freshwater lakes, mechanisms by which bacteria cope with their environment, two different sets of interacting molecules, which transmit signals through cells, energy production during infection of barley by powdery mildew, and the ecosystem of algae, midges and fish in a lake in Iceland. These different biological systems will help us to fine tune the statistical techniques, suggest how to make the best use of biological data, and thus improve our understanding of how nature works.

越来越多的生物数据被生成和收集，这些数据描述了生物系统随时间的变化。例如，在系统生物学中，使用自动化技术来筛选大量分子之间的相互作用现在是一种正常的做法。为了解释这些数据，我们越来越依赖数学模型。这些模型总结了我们认为生物系统工作的方式。通常，我们并不确定生物系统如何工作以及什么机制运作，并且通常有许多不同的模型可以描述给定的生物系统。为了找出哪个模型最好，或者哪个机制最有可能，我们需要收集数据并将模型的输出与数据进行比较。我们建议开发技术来进行这样的分析，以选择模型，并作出有关生物系统的结论。在这里，我们将使用动力系统理论和统计推断的概念，以新颖的方式将它们联合收割机结合起来，并将它们分别用于生态学和系统生物学中的生物系统分析。然后，我们将把这些技术应用于不同的生物学问题。在种群生物学和系统生物学中，实现这一目标所需的数学模型和工具非常相似，因此我们选择了种群生物学和系统生物学的混合应用。因此，在描述来自生物系统和过程的数据时比较不同数学模型的艺术对于现代生命科学和生物医学科学的未来发展至关重要。这个问题已经研究和实践之前，许多其他人，但本研究介绍了一个新的元素，这一领域。一个模型通常由两部分组成：它有一个数学结构，它规定了系统的哪些部分相互作用;其次，它有一组变量，它规定了各个部分相互作用的程度（称为模型参数，例如动力学速率常数）。模型结构通常是“猜测”或假设的，这些假设通过执行实验进行测试;模型参数通常从实验数据推断，但某些模型参数可能很难估计。虽然以前人们认为，无法确定地估计参数会使生物过程的分析变得困难，如果不是不可能的话，但最近的研究-包括提议进行这项研究的三个小组所做的研究-表明，即使不知道这一点，也可以取得实质性进展。这是因为（i）如果一个参数很难估计，通常是因为它对系统的工作方式几乎没有影响，（ii）通过对所有可能的参数进行积分，可以很好地理解系统是如何工作的。即使这些方法不能给出生物系统如何工作的明确答案，它们也可以帮助我们设计更好的实验，或者指出应该收集的数据，以便提供最多的信息。将在本项目过程中开发的统计工具将应用于各种生物系统的数据集。我们将与实验研究合作者一起探索这些新技术的工作原理，并探索我们希望通过使用这些技术获得的新见解。我们将研究的生物系统是：淡水湖泊中的浮游生物，细菌科普环境的机制，两组不同的相互作用分子，通过细胞传递信号，大麦感染白粉病期间的能量生产，以及冰岛湖泊中藻类，摇蚊和鱼类的生态系统。这些不同的生物系统将帮助我们微调统计技术，建议如何最好地利用生物数据，从而提高我们对自然界如何运作的理解。

项目成果

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.

• DOI: 10.1016/j.celrep.2016.05.024
• 发表时间: 2016-06-14
• 期刊: Cell reports
• 影响因子: 8.8
• 作者: Filippi S;Barnes CP;Kirk PD;Kudo T;Kunida K;McMahon SS;Tsuchiya T;Wada T;Kuroda S;Stumpf MP
• 通讯作者: Stumpf MP

Bayesian design strategies for synthetic biology

• DOI: 10.1098/rsfs.2011.0056
• 发表时间: 2011-12-06
• 期刊: INTERFACE FOCUS
• 影响因子: 4.4
• 作者: Barnes, Chris P.;Silk, Daniel;Stumpf, Michael P. H.
• 通讯作者: Stumpf, Michael P. H.

Protection versus pathology in aviremic and high viral load HIV-2 infection-the pivotal role of immune activation and T-cell kinetics.

• DOI: 10.1093/infdis/jiu165
• 发表时间: 2014-09-01
• 期刊: The Journal of infectious diseases
• 作者: Hegedus A;Nyamweya S;Zhang Y;Govind S;Aspinall R;Mashanova A;Jansen VA;Whittle H;Jaye A;Flanagan KL;Macallan DC
• 通讯作者: Macallan DC

On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo.

• DOI: 10.1515/sagmb-2012-0069
• 发表时间: 2013-03-26
• 期刊: Statistical applications in genetics and molecular biology
• 影响因子: 0.9
• 作者: Filippi, Sarah;Barnes, Chris P;Stumpf, Michael P H
• 通讯作者: Stumpf, Michael P H

Considerate approaches to constructing summary statistics for ABC model selection

• DOI: 10.1007/s11222-012-9335-7
• 发表时间: 2012-11-01
• 期刊: STATISTICS AND COMPUTING
• 影响因子: 2.2
• 作者: Barnes, Chris P.;Filippi, Sarah;Thorne, Thomas
• 通讯作者: Thorne, Thomas