Application of non-linear mathematics and stochastic modelling to complex biological systems

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

This project aims to develop novel mathematical approaches, based on non-linear mathematics and stochastic modelling, to analyse and predict the behaviour of complex agricultural and biological systems underpinning predictive systems biology. Key objectives of the project are:

1. Modelling impact of climate change
The probability and the magnitude of extreme events and impacts on crops are likely to increase under climate change. We will develop methodology and computational tools to analyse extreme impacts on crops and plant communities under climate change. Specifically:
a) to develop local-scale climate scenarios, based on the LARS-WG Weather Generator, a multi-model ensemble of global and regional climate models
b) to develop a dataset of LARS-WG baseline parameters for Europe with a 25 km grid.

2. Crop modelling
Crop models provide a consistent framework for integrating our understanding of plant processes as influenced by environments. Specifically:
a) to use crop simulation models to deconvolute complex traits, such as nitrogen use efficiency (NUE) or water use efficiency (WUE);
b) to develop computational tools for evaluating performance of new genotypes in diverse environments.

3. Individual-based modelling
The development of resistance in pest insects to insecticides is a significant barrier to sustainable farming. The evolution of resistance is affected by many factors limiting applications of classical modelling approaches. We will develop an individual-based model (IbM) that includes genetic status, individual behaviours, multitrophic interactions and environmental heterogeneity. Specifically:
a) to develop a predictive high-performance IbM;
b) to develop approaches for analysis of stochastic high-dimensional IbM output;
c) to predict of the evolution of resistance in model systems.

unavailable