Analysing how auxin dynamics control root phenotype

生长素影响植物生长的许多方面。在植物根系中，生长素影响根的生长速度，引导根尖向下生长，并决定何时从主根长出新的根枝。因此，生长素控制着整个根系的形态，从而影响根系从土壤中吸收水分和养分的难易程度，以及根系将植物锚定在地下的安全程度。为了控制根的生长、弯曲和分支，每个细胞内生长素的数量在不同细胞之间和随着时间的推移而变化。植物通过在细胞膜上放置不同的蛋白质和通道来控制生长素的分布，从而影响生长素进出每个细胞的速度。很难预测每个细胞膜上每个蛋白质/通道的数量如何影响根尖内生长素的总体分布。在这个项目中，我们将建立和测试数学模型来研究细胞膜上的蛋白质/通道如何影响生长素的分布。然后，我们将使用这些模型来了解生长素如何控制根的生长、弯曲和分支。为了建立生长素运输的精确模型，我们将首先对细胞几何形状和细胞膜上蛋白质/通道的分布进行成像。利用这些信息，我们将写下生长素如何进出每个细胞的数学描述，以形成一个数学模型。然后，我们将模拟和分析数学模型来预测植物根系中的生长素分布。为了最大限度地利用所获得的知识，我们将使用一系列数学技术来产生不同类型的模型，每种模型都有不同的优点，并适用于不同类型的分析。然后我们将仔细地将模型结果与实验数据进行比较。由于生长素非常小，我们无法测量每个细胞内生长素的数量，也很难测量生长素跨细胞膜运输的速率。因此，我们将利用被生长素降解的荧光蛋白来收集用于测试模型的数据。我们将进行一系列实验来彻底测试模型，例如，考虑某些蛋白质不起作用的根，或者当生长素被提供给根时。如果模型预测和数据不一致，我们将使用模型来发展新的假设，并确定哪个新实验最能检验这些假设。因此，建模将激发新的实验，其结果将导致改进的模型，我们将在所谓的“模型-实验”循环中移动。该项目将提高我们对生长素如何通过控制根的生长、弯曲和分支来控制植物根系的理解。确定控制植物根系生长素动态的因素将为我们提供如何操纵植物根系的知识。从长远来看，这一知识将导致开发出更适合其环境条件的根系作物，这将显著提高作物产量。此外，该项目将产生严格的数学模型，这些模型将使用各种技术进行分析和测试。这些模型和技术可以应用于理解其他生物学问题，因此也将有利于未来的研究。

The hormone auxin affects many aspects of plant growth. In the plant root, auxin affects how quickly the root grows, orients the root tip to grow downwards and determines when a new root branch will grow from the main root. Therefore, auxin controls the form of the whole root system, which affects how easily roots can take-up water and nutrients from the soil and how securely roots anchor the plant in the ground. To control the growth, bending and branching of the root, the amount of auxin within each cell varies both between different cells and over time. The plant controls the auxin distribution by positioning different proteins and channels on the cell membranes, which affect how quickly auxin can get into and out of each cell. It is hard to predict how the amount of each protein/channel on each cell membrane affects the overall auxin distribution within the root tip. In this project, we will make and test mathematical models to investigate how the proteins/channels on the cell membranes affect the auxin distribution. We will then use these models to understand how auxin controls root growth, bending and branching.To create an accurate model of auxin transport, we will first image cell geometries and the distributions of the proteins/channels on the cell membranes. Using this information, we will write down a mathematical description of how auxin moves into and out of each cell to form a mathematical model. We will then simulate and analyse the mathematical model to predict the auxin distribution within the plant root. In order to maximise the knowledge gained, we will use a range of mathematical techniques to produce different types of model, each having different advantages and being amenable to different types of analysis. We will then carefully compare the model results with experimental data. Because auxin is very small, we are unable to measure the amount of auxin within each cell and it is hard to measure the rate of auxin transport across cell membranes. We will therefore make use of fluorescent proteins that are degraded by auxin to collect data with which to test the models. We will carry out a range of experiments to thoroughly test the models, for example, considering roots in which certain proteins are not functional, or when auxin has been supplied to the root. In the event that the model predictions and data do not agree, we will use the models to develop new hypotheses and identify which new experiment would best test these. The modelling will therefore motivate new experiments, the results of which will lead to improved models, and we will move around what is known as the 'model-experimental' loop.The project will improve our understanding of how auxin controls the plant root system by controlling the growth, bending and branching of the root. Determining what controls auxin dynamics in the plant root will provide us with knowledge of how to manipulate plant roots. In the longer term, this knowledge will lead to the development of crops with roots that are better suited to their environmental conditions, which will significantly improve crop yields. In addition, the project will produce rigorous mathematical models which will be analysed and tested using a wide range of techniques. These models and techniques could be applied to understand other biological questions and so will also be beneficial to future research.