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A generic framework for computational modelling and analysis of regulatory gene networks applied to the response to wounding in arabidopsis

用于拟南芥受伤反应的调控基因网络计算建模和分析的通用框架

基本信息

批准号：
BB/F009437/1
负责人：
Jan Kim
金额：
$ 45.5万
依托单位：
University of East Anglia
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%2FF009437%2F1
关键词：
generic framework computational modelling analysis

项目摘要

Plants are exposed environmental factors, many of which are detrimental, such as wounding and pathogen attack. Specific defensive responses to such challenges are critical to a plant's fitness and survival. The molecular mechanisms underlying the response to wounding, which is spatially structred into a local and a systemic response, and to other challenges have extensively been studied, and key pathways, mediated by signalling molecules including jasmonic acid, salicylic acid and ethylene, have been identified. These pathways are interlinked by crosstalk, mediated by components that participate in more than one pathway. The system that mediates defensive responses can be characterised as a regulatory gene network (RGN). Regulatory gene networks are generally a central biological mechanism of decoding genetic information that confers adaptive capabilities into phenotypic responses and other traits. RGNs are complex systems that cannot be fully understood by based either on straightforward inspection, and that can only partially be analysed mathematically. Computational modelling and analysis are tools for investigating and understanding such complex systems. Computational models of regulatory networks can be used in 'forward' simulations to generate synthetic gene expression profiles. Comparing these synthetic profiles to empirically measured gene expression data gives some indication how well a computational RGN model corresponds to the real RGN. However, discrepancies between synthetic and empirical profiles may have (at least) two causes, they may be due to an incorrect network structure, or the structure may be correct but numerical parameters (e.g. kinetic constants) were chosen incorrectly. In this project we will develop and use a statistsical approach to discriminate alternative RGN models based on the consistence of their synthetic profiles with a data set of empirical gene expression measurements. Effects resulting from parameterisation will be factored out by applying computational optimisation to find the best parameters for each of the candidate models. If this fit to the data is consistently better for one model than for an alternative one, the models are thus discriminated and the RGN structure that is more consistent with the data is identified. In the computational part of this project, a software system, called the model discrimination software platform (MDP), implementing this approach will be developed. The MDP will use transsys, a computational framework for RGN modelling. The experimental part of the project will produce a data set of gene expression measurements from various Arabidopsis mutants with altered wounding responses. The interdisciplinary project will use the MDP to produce comprehensive models of the RGNs organising the wounding response. These models will then be studied by computational simulations and analyses in order to investigate the role of crosstalk and the mechanisms by which RGNs organise the spatiotemporal structure of the defensive responses. Predictions and new hypotheses derived from these studies will be tested experimentally. This project will release MDP as an open source sofware system that is useful for RGN modelling in general, and contribute to the system-level understanding of the RGNs organising the plant wounding response.

植物是暴露在环境因素中的，其中许多是有害的，如创伤和病原体的攻击。对这种挑战的特定防御反应对植物的适应性和生存至关重要。已经广泛研究了对创伤的响应（其在空间上构造成局部和全身响应）和对其他挑战的响应的分子机制，并且已经鉴定了由包括茉莉酸、水杨酸和乙烯的信号分子介导的关键途径。这些通路通过串扰互连，由参与多个通路的组件介导。介导防御反应的系统可以被表征为调节基因网络（RGN）。调节基因网络通常是解码遗传信息的中心生物学机制，其赋予适应能力到表型反应和其他性状。RGN是一个复杂的系统，无法通过简单的检查来完全理解，只能部分地进行数学分析。计算建模和分析是研究和理解这种复杂系统的工具。调控网络的计算模型可用于“正向”模拟以生成合成基因表达谱。将这些合成谱与经验测量的基因表达数据进行比较给出了计算RGN模型与真实的RGN对应程度的一些指示。然而，合成曲线和经验曲线之间的差异可能（至少）有两个原因，它们可能是由于不正确的网络结构，或者结构可能是正确的，但数值参数（例如动力学常数）选择不正确。在这个项目中，我们将开发和使用一个statistsical方法来区分替代RGN模型的基础上，他们的合成配置文件与经验基因表达测量的数据集的一致性。参数化产生的影响将通过应用计算优化来找出每个候选模型的最佳参数。如果一个模型对数据的拟合始终优于另一个模型，则可以区分模型，并识别出与数据更一致的RGN结构。在本项目的计算部分，将开发一个软件系统，称为模型判别软件平台（MDP），实现这种方法。MDP将使用transsys，这是RGN建模的计算框架。该项目的实验部分将产生一个数据集的基因表达测量从不同的拟南芥突变体与改变创伤反应。跨学科项目将使用MDP来产生组织创伤反应的RGN的综合模型。然后，这些模型将通过计算模拟和分析进行研究，以研究串扰的作用和RGNS组织防御反应的时空结构的机制。从这些研究中得出的预测和新的假设将通过实验进行检验。该项目将发布MDP作为一个开源软件系统，这是有用的RGN建模一般，并有助于系统级的RGN组织植物受伤的反应的理解。