Maths models for growth

new shoots in field
Two studies led by researchers at the University of Nottingham have highlighted the usefulness of mathematical approaches in the life sciences. Details of both projects, undertaken at the Centre for Plant Integrative Biology, have recently appeared in the journal Proceedings of the National Academy of Sciences of the United States of America (PNAS).

The work may have repercussions in the ongoing effort to meet the ever-growing global demand for food. The studies examined the way that a particular hormone – gibberellin – affects the growth of plants. Gibberellin is a key factor in all aspects of plant development and in all parts of a plant, from root to stem to flower. Involved in many complex molecular processes, its general function is to convert environmental stimuli into responses which ensure the plant's survival.

The first team applied mathematical modelling techniques to demonstrate how changing hormone levels and genetic mutations in pathways involving the hormone, influenced feedback; work that could be applied to the improvement of future crop varieties.

"We know that plants with low levels of gibberellin show drastically reduced growth, whilst adding gibberellin can significantly increase growth rates," said Dr Markus Owen, Reader in Applied Mathematics, and research leader. "Mathematical modelling has proved to be a powerful tool to help us understand how gibberellin works. Ultimately, this should help plant scientists to develop crops with improved growth, and hence to address problems of global food security."

Led by John King, Professor of Theoretical Mechanics, the second group used multiscale mathematical modelling to look at the role of gibberellin more specifically in root growth. It was revealed in work by researcher Leah Band that growth may eventually cease due to the dilution of gibberellin which results from the expansion of root cells.

While the first study has indicated the significance of feedback loops in gibberellin's signalling network, the second has built on this work by predicting the distribution of DELLA proteins, which usually suppress growth.

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