A plant leaf grows into its final shape because each cell carries an internal compass that tells it which way to grow, and this project aims to discover how those compasses stay aligned across thousands of cells. Without this coordination, tissues would bulge and buckle randomly rather than forming orderly structures like flat leaves or straight stems. The problem is that plant cells are locked inside rigid walls that block direct communication, so the compasses must be synchronised indirectly—likely through the hormone auxin, which diffuses from cell to cell. The researchers have developed a hypothesis for how this works, but it remains untested. This is fundamental science with no immediate practical application. Understanding how tissues coordinate their growth could eventually allow scientists to predictably shape plants—for example, engineering crops with more efficient leaf arrangements for sunlight capture, or trees that grow straighter for timber. Past discoveries in plant development have already enabled advances in agriculture and bioengineering, and a deeper grasp of self-organising growth could unlock similar unexpected applications.
View original technical description
Unlike most things that we manufacture, like cars and phones, the biological structures we see around us are generated through growth: a tree grows from a seed and a human child grows from a microscopic fertilised egg. This growth is not guided by an external hand but is self-organised, coordinated by internal rules. Complex structures can arise from this process because growth and deformation occur at varying rates over tissues and preferentially in particular directions. A major challenge is to understand how this process of self-construction through growth operates. Answering this question may allow us to modulate growing structures in a predictive way and thus enable us to exploit, manage and interact with living systems more effectively. A major block to achieving this goal is that we currently understand little about how orientations are specified and coordinated within growing tissues. Our best working hypothesis is that this process depends on each cell having a polarity, like an internal compass, but how this polarity is propagated and coordinated across a tissue remains unclear. This project aims to address this problem by studying polarity coordinated in plants and how it may lead to tissue growing out in particular orientations. Plant cells are surrounded by walls which impede direct communication from one cell to another. Polarity coordination is therefore thought to operate indirectly, mediated by small molecules, such as the plant hormone auxin, that can diffuse through the cell wall. We have recently developed a general hypothesis for how this process may lead to coordinated polarity across tissues and a major aim of this project is to test this and other hypotheses through a combination of experimental and computational methods. To understand the mechanism of polarity coordination, we aim to switch on genes that have been implicated in polarity control in patches of cells within growing leaf tissue. By activating these genes in small patches of tissue we hope to see how the polarity of the surrounding tissue is affected and how this affects the growth of the cells, which we can follow in detail with bioimaging. These experiments will be conducted in normal plants and also plants in which certain polarity components have been removed through mutation. We also intend to develop computational modelling systems that will allow us to determine whether particular hypotheses can account for the observed patterns of polarity and growth change. This will involve developing new software to deal with both the cellular nature of tissues and the way they grow and deform. Taken together these experimental and computational approaches should enable us to arrive at a new and deeper understanding of how polarity and growth are coordinated and thus advance our ability to manage and exploit biological systems.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know