Systems Understanding of Microbial Oxygen-Dependent and Independent Catabolism (SUMO2)

微生物氧依赖性和独立分解代谢的系统理解 (SUMO2)

• 批准号: BB/I004777/1
• 负责人: Guido Sanguinetti
• 金额: $ 34.07万
• 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: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI004777%2F1
• 关键词: Systems; Understanding; Microbial; Oxygen; Dependent

In this project, a multidisciplinary team of scientists, comprising molecular biologists, biochemists, mathematicians and computer scientists will work together and use Systems Biology to study the response of a bacterium to oxygen in its environment. The bacterium is E. coli, a major model organism, study of which has provided much of our current knowledge of basic molecular biology and continues to be the workhorse of the biotechnology sector. The Systems Biology approach to research is a relatively new area of investigation that tries to understand entire biological processes or systems (such as an organism and its behaviour), rather than merely the components of the system (such as atoms, molecules, and individual reactions). In effect, Systems Biology attempts to 'put Humpty Dumpty together again'. Systems Biology can be used to simulate biological processes and predict the outcome of perturbing the system, even before experiments are carried out. This ability to predict leads to the design of more meaningful experiments. We aim to obtain a complete, quantitative description of the metabolism of this bacterium, i.e. understand how carbon foodstuffs are broken down and used to generate energy in fermentation and respiration. The expected results are as follows. (1) Complex experimental results describing: (a) the dynamics of gene expression during the change from fermentative to anaerobic (i.e. without oxygen) respiratory growth; (b) the dynamics of responses to pulses of oxidants (oxygen and chemically related molecules); (c) the effects of mutation of substrate transport processes on physiology under defined aerobiosis conditions (i.e. in air); (d) effects of perturbation of central metabolism by mutation and by switching carbon source; (e) the role of a branched respiratory chain in adapting when conditions change. (2) Multi-scale, multi-level mathematical models describing: (a) the dynamics of gene regulatory networks during perturbations of anaerobic cultures; (b) the regulatory consequences of dynamics within complex networks of gene regulation processes; (c) the regulatory mechanisms of gene promoters; (d) catabolism (break-down) of foodstuffs from their entry into the cell right through to energy generation, including anaerobic pathways; (e) the regulation and action of alternative respiratory chains; (f) single-cell dynamics and cell behaviour in unstable conditions. (3) The formulation of a predictive, quantitative, experimentally validated model of the integrated metabolic sub-systems of E. coli and how these respond to changes in internal and external signals.

在这个项目中，一个由分子生物学家、生物化学家、数学家和计算机科学家组成的多学科科学家团队将共同努力，利用系统生物学研究细菌对环境中氧气的反应。该菌为E.大肠杆菌是一种主要的模式生物，对大肠杆菌的研究为我们提供了目前基本分子生物学的大部分知识，并继续成为生物技术部门的主力。系统生物学研究方法是一个相对较新的研究领域，试图了解整个生物过程或系统（如有机体及其行为），而不仅仅是系统的组成部分（如原子，分子和个体反应）。实际上，系统生物学试图“把Humpty Dumpty再次放在一起”。系统生物学可以用来模拟生物过程，并预测干扰系统的结果，甚至在实验进行之前。这种预测能力导致设计更有意义的实验。我们的目标是获得这种细菌代谢的完整定量描述，即了解碳食物如何被分解并用于在发酵和呼吸中产生能量。预期结果如下。(1)复杂的实验结果描述：（a）从发酵到厌氧的变化过程中基因表达的动态（即无氧）呼吸生长;（B）对氧化剂脉冲的响应动力学（氧和化学相关分子）;（c）在确定的有氧条件下，基质运输过程的突变对生理学的影响（d）突变和转换碳源干扰中枢代谢的影响;（e）呼吸链分支在适应条件变化方面的作用。(2)描述以下内容的多尺度、多层次数学模型：（a）厌氧培养物扰动期间基因调控网络的动态;（B）基因调控过程复杂网络内动态的调控结果;（c）基因启动子的调控机制;（d）催化剂（e）替代呼吸链的调节和作用;（f）单细胞动力学和不稳定条件下的细胞行为。(3)制定了一个预测，定量，实验验证模型的综合代谢子系统的E。以及它们如何对内部和外部信号的变化做出反应。

项目成果

Approximate Inference in Latent Diffusion Processes from Continuous Time Observations

根据连续时间观测对潜在扩散过程进行近似推断

• 作者: Botond Cseke (Author)

Sparse Approximate Inference for Spatio-Temporal Point Process Models

时空点过程模型的稀疏近似推理

• DOI: 10.1080/01621459.2015.1115357
• 发表时间: 2017
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Cseke B

MMDiff: quantitative testing for shape changes in ChIP-Seq data sets.

• DOI: 10.1186/1471-2164-14-826
• 发表时间: 2013-11-24
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Schweikert G;Cseke B;Clouaire T;Bird A;Sanguinetti G
• 通讯作者: Sanguinetti G