Award details

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

Reference	BBS/E/C/00004938
Principal Investigator / Supervisor	Dr Mikhail Semenov
Co-Investigators / Co-Supervisors
Institution	Rothamsted Research
Department	Rothamsted Research Department
Funding type	Research
Value (£)	315,653
Status	Completed
Type	Institute Project
Start date	01/04/2008
End date	31/03/2012
Duration	48 months

Abstract

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.

Summary

unavailable

Committee	Closed Committee - Agri-food (AF)
Research Topics	Crop Science, Plant Science, Systems Biology, Technology and Methods Development
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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