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

植物暴露于环境因素中，其中许多是有害的，如伤害和病原体攻击。对这类挑战的特定防御反应对植物的健康和生存至关重要。对伤害的反应在空间结构上分为局部和系统反应以及对其他挑战的反应，其分子机制已被广泛研究，并已确定了由茉莉酸、水杨酸和乙烯等信号分子介导的关键途径。这些通路通过串扰相互连接，由参与不止一条通路的组件介导。调节防御反应的系统可以被描述为调控基因网络(RGN)。调控基因网络通常是解码遗传信息的核心生物学机制，这些机制赋予表型反应和其他特征以适应能力。RGNs是复杂的系统，不能通过简单的检查就能完全理解，而且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类复杂系统的工具。调控网络的计算模型可以用于“正向”模拟，以生成合成的基因表达谱。将这些合成的图谱与经验测量的基因表达数据进行比较，可以得出一些迹象，表明计算RGN模型与真实RGN的对应程度。然而，合成轮廓和经验轮廓之间的差异可能有(至少)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种统计方法来区分不同的RGN模型，该方法基于它们的合成轮廓与经验基因表达测量数据集的一致性。通过应用计算优化来为每个候选模型找到最佳参数，将会考虑到参数化所产生的影响。如果对数据的这种拟合度对于一个模型始终比对于可选模型更好，则区分模型并且识别与数据更一致的RGN结构。在这个项目的计算部分，将开发一个软件系统，称为模型识别软件平台(MDP)，用于实现这种方法。MDP将使用Transsys，这是一个用于RGN建模的计算框架。该项目的实验部分将产生一组基因表达测量数据，这些数据来自不同的拟南芥突变体，它们的伤害反应发生了变化。跨学科项目将使用MDP来制作组织伤人反应的RGNs的综合模型。然后将通过计算模拟和分析来研究这些模型，以调查串扰的作用以及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得到实验检验。这个项目将发布MDP作为一个开源软件系统，它对RGN建模总体上很有用，并有助于在系统级别上理解RGN组织植物伤害反应的过程。

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.