Developing methods for inferring regulatory mechanisms from intact systems: a neisseria case study

开发从完整系统推断调控机制的方法：奈瑟菌案例研究

• 批准号: BB/G001863/1
• 负责人: Michael Stumpf
• 金额: $ 41.03万
• 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%2FG001863%2F1
• 关键词: Developing; methods; inferring; regulatory; mechanisms

The behaviour of biological systems is controlled and coordinated through a network of 'regulators' and other intracellular interactions that control the expression of the genes within the cell. In bacteria the production of the messenger RNA and the production of proteins are closely linked, and much of the way in which a cell's behaviour is controlled is done at the level of transcription. Transcription can be measured for all of the genes in a cell simultaneously, using microarrays, and this gives a relatively direct read-out of the way in which many aspects of the cell's behaviour are being controlled. This method gives a 'snap shot' of the transcribed genes, and when the observations from many snap shots are combined, the way in which the cell controls its functions can be progressively pieced together, much as the meaning of a movie can be pieced together from the combination of multiple 'frames'. Finding ways to use this information to make testable models that can be used to dissect these central processes that control biological systems is a critical component of systems biology and understanding how biological systems work at a fundamental and 'whole cell' level. If causal relationship and key interactions controlling a cell's behaviour can be determined based upon this type of 'observational' information, then this means that these systems can be addressed without the (frequently impossible, impractical, or unaffordable) need to address each gene individually. Many genes are required for a cell to survive. Other genes are not required for life, but the resulting cell does not function 'normally' in several ways when a normal component has been removed / and it is very difficult to tell which effects are directly or indirectly due to the effects of a gene / gene product. To understand how cellular systems work, we propose that we need ways to analyze and use the information from 'intact / unbroken' biological systems. In this proposal we will make use of one of the largest collections of 'transcript' data, and augment this with information specifically designed to assist modeling the ways in which the cell is controlled. The effectiveness of this modeling will be tested, and the models will be augmented and refined by addressing the key genes by making mutants and testing to what extent they behave according to the model predictions. In this way, we will develop a generally applicable approach that can be applied generally, without the need for expensive, time consuming, and potentially misleading mutant generation in the future.

生物系统的行为是通过“调节器”网络和其他控制细胞内基因表达的细胞内相互作用来控制和协调的。在细菌中，信使RNA的产生和蛋白质的产生密切相关，细胞行为的大部分控制方式是在转录水平上完成的。使用微阵列可以同时测量细胞中所有基因的转录，这可以相对直接地读出细胞行为的许多方面的控制方式。这种方法给出了转录基因的“快照”，当将许多快照的观察结果组合起来时，细胞控制其功能的方式可以逐步拼凑在一起，就像电影的含义可以通过多个“帧”的组合拼凑在一起一样。找到使用这些信息来制作可测试模型的方法，这些模型可用于剖析控制生物系统的这些中央过程，是系统生物学的重要组成部分，并了解生物系统如何在基本和“全细胞”水平上工作。如果控制细胞行为的因果关系和关键相互作用可以根据这种类型的“观察”信息来确定，那么这意味着可以解决这些系统，而无需（通常不可能、不切实际或负担不起）单独解决每个基因。细胞的生存需要许多基因。其他基因不是生命所必需的，但当正常成分被去除时，所得细胞在多种方面无法“正常”发挥作用，并且很难判断哪些影响是直接或间接归因于基因/基因产物的影响。为了了解细胞系统如何工作，我们建议我们需要一些方法来分析和使用来自“完整/完整”生物系统的信息。在这个提案中，我们将利用最大的“转录”数据集合之一，并用专门设计的信息来增强它，以帮助对细胞的控制方式进行建模。该模型的有效性将得到测试，并且通过制造突变体并测试它们根据模型预测的行为程度来处理关键基因，从而增强和完善模型。通过这种方式，我们将开发出一种可以普遍应用的普遍适用的方法，而无需在未来进行昂贵、耗时且可能误导性的突变体生成。

项目成果

Maximizing the information content of experiments in systems biology.

• DOI: 10.1371/journal.pcbi.1002888
• 发表时间: 2013
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Liepe J;Filippi S;Komorowski M;Stumpf MP
• 通讯作者: Stumpf MP

Balancing the robustness and predictive performance of biomarkers.

• DOI: 10.1089/cmb.2013.0018
• 发表时间: 2013-12
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Kirk P;Witkover A;Bangham CR;Richardson S;Lewin AM;Stumpf MP
• 通讯作者: Stumpf MP