Centre for Systems Biology at Edinburgh

爱丁堡系统生物学中心

• 批准号: BB/D019621/1
• 负责人: Andrew Millar
• 金额: $ 1160.29万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FD019621%2F1
• 关键词: Centre; Systems; Biology; Edinburgh

Systems Biology is a fascinating development in modern biology. We have achieved a good general understanding of how cells work, including the 'central dogma': genes are transcribed to RNA; a splicing process produces mature messenger RNA; mRNA is translated to proteins; and protein pathways regulate gene expression and perform other functions such as detecting intercellular signals. With the Human Genome Project we know our DNA sequence and have a partial map of our genes. And, finally, high-throughput biology is giving us massive amounts of time series data, e.g., of protein or mRNA concentrations. Systems Biology seeks to understand how biological systems function by integrating all this knowledge. System theories are implemented as in silico models: computer simulations built using mathematical models. Biological systems are extraordinarily complex with many levels of interacting subsystems. We therefore expect to construct models by combining submodels, beginning with pathways, and eventually proceeding to organelles, cells, physiological systems and whole organisms. One hopes to be able to predict the effect of variations, e.g.: environmental, or resulting from disease or adding drugs. Such a Systems Biology would produce an enormous increase in understanding and lead to major progress in medicine, agriculture and industry. The Edinburgh Centre for Systems Biology will advance our ability to make and use such models by applying advanced computer science and mathematical techniques to a carefully chosen range of important biological systems which are different enough to test our model-making ability to the limit. Our largest such system is the interferon pathway, an important signaling pathway in macrophages, the main immune system cells. It is already hard here to conveniently describe the pathway intricacies, and we shall develop new international graphical standards. The middle-sized system is RNA metabolism, the process leading from raw to mature RNA. It should be possible to model this still complex system in detail, correlating the models with high-throughput data. Finally comes circadian rhythm, biological clocks, where small genetic circuits regulate large parts of gene expression. Here we may perform mathematical analyses, e.g., investigating how light and temperature synchronise clocks in a noisy environment. The traditional modelling technique of mathematical biology uses systems of differential equations: systems biology presents new challenges. We shall produce SBSI, a modelling facility of industrial quality freely available to all. Probabilistic models are sometimes more realistic than differential ones, e.g., for few protein molecules. We shall explore such variations to ensure realistic yet tractable modelling. High-throughput data are noisy and hard to obtain in sufficient quantity. We shall apply Bayesian techniques, familiar from Artificial Intelligence, to help discover pathways. We wish to construct big systems from small ones (modules) and to experiment efficiently with system variants. Programming languages let one do this for computational systems; we shall apply the lessons learnt to design and use languages for biological ones, with the additional prospect of being able to query and design systems using special logics. In summary, we intend to build a science of Systems Biology using computer science and mathematics to produce models refined by and informing biological experiment. The variety of the biology we do will ensure the wide usefulness of the techniques; the variety of the techniques we will explore will give the enterprise every prospect of success. However to achieve usefulness requires much more. We will therefore combine our scientific effort with training and outreach programmes: the one to contribute to the production of the next generation of systems biologists; and the other to make our work available to our colleagues in academia and our partners in industry.

系统生物学是现代生物学的一个引人入胜的发展。我们已经对细胞如何工作有了一个很好的总体理解，包括“中心法则”：基因被转录成RNA；剪接过程产生成熟的信使RNA；mRNA被翻译成蛋白质；蛋白质通路调节基因表达并执行其他功能，如检测细胞间信号。通过人类基因组计划，我们知道了我们的DNA序列，并拥有了我们基因的部分图谱。最后，高通量生物学为我们提供了大量的时间序列数据，例如蛋白质或mRNA浓度。系统生物学试图通过整合所有这些知识来理解生物系统的功能。系统理论是在计算机模型中实现的：使用数学模型建立的计算机模拟。生物系统是非常复杂的，有许多层次的相互作用的子系统。因此，我们期望通过结合子模型来构建模型，从途径开始，最终推进到细胞器、细胞、生理系统和整个生物体。人们希望能够预测变化的影响，例如：环境、疾病或添加药物引起的变化。这样的系统生物学将极大地增进人们的理解，并导致医学、农业和工业的重大进步。爱丁堡系统生物学中心将通过将先进的计算机科学和数学技术应用于精心挑选的一系列重要的生物系统来提高我们制作和使用这些模型的能力，这些生物系统的差异足以测试我们的模型制作能力到极限。我们最大的此类系统是干扰素通路，这是巨噬细胞（主要免疫系统细胞）中的重要信号通路。这里已经很难方便地描述路径的复杂性，我们将开发新的国际图形标准。中等规模的系统是RNA代谢，即从原始RNA到成熟RNA的过程。应该有可能对这个仍然复杂的系统进行详细建模，并将模型与高通量数据相关联。最后是昼夜节律，即生物钟，小的基因回路调节着大部分基因表达。在这里，我们可以进行数学分析，例如，研究光和温度如何在嘈杂的环境中同步时钟。传统的数学生物学建模技术采用微分方程系统，系统生物学提出了新的挑战。我们将生产SBSI，一个工业质量的模型设施，免费提供给所有人。概率模型有时比微分模型更现实，例如，对于少数蛋白质分子。我们将探索这些变化，以确保现实而易于处理的建模。高通量数据是有噪声的，难以获得足够数量的数据。我们将应用人工智能中熟悉的贝叶斯技术来帮助发现路径。我们希望从小系统（模块）构建大系统，并对系统变量进行有效的实验。编程语言让我们可以为计算系统做到这一点；我们将把所学到的经验应用于设计和使用生物语言，并期望能够使用特殊逻辑查询和设计系统。总之，我们打算建立一门系统生物学科学，利用计算机科学和数学来产生由生物实验提炼和提供信息的模型。我们所从事的生物学研究的多样性将确保这些技术的广泛用途；我们将探索的各种技术将给企业带来成功的希望。然而，要实现有用性需要更多。因此，我们将把我们的科学努力与培训和外联规划结合起来：一个是为培养下一代系统生物学家作出贡献；另一个是将我们的研究成果提供给学术界的同事和工业界的合作伙伴。

项目成果

A rule-based kinetic model of RNA polymerase II C-terminal domain phosphorylation.

• DOI: 10.1098/rsif.2013.0438
• 发表时间: 2013-09-06
• 期刊: Journal of the Royal Society, Interface
• 作者: Aitken S;Alexander RD;Beggs JD
• 通讯作者: Beggs JD

Processivity and coupling in messenger RNA transcription.

• DOI: 10.1371/journal.pone.0008845
• 发表时间: 2010-01-28
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Aitken S;Robert MC;Alexander RD;Goryanin I;Bertrand E;Beggs JD
• 通讯作者: Beggs JD

Modelling reveals kinetic advantages of co-transcriptional splicing.

• DOI: 10.1371/journal.pcbi.1002215
• 发表时间: 2011-10
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Aitken S;Alexander RD;Beggs JD
• 通讯作者: Beggs JD

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology.

• DOI: 10.1093/bioinformatics/btt023
• 发表时间: 2013-03-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Adams R;Clark A;Yamaguchi A;Hanlon N;Tsorman N;Ali S;Lebedeva G;Goltsov A;Sorokin A;Akman OE;Troein C;Millar AJ;Goryanin I;Gilmore S
• 通讯作者: Gilmore S

Complementary approaches to understanding the plant circadian clock

• DOI: 10.4204/eptcs.19.1
• 发表时间: 2010-01-01
• 期刊: ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE
• 作者: Akman, Ozgur E.;Guerriero, Maria Luisa;Troein, Carl
• 通讯作者: Troein, Carl