School of Mathematical Sciences
   
   
  

Food projects

A list of currently available food projects within the MASS doctoral training centre. For queries in relation to a particular project, please contact the supervisors associated with that project. Click on a project name to view more details below.

Bacterial infections in food animals as a problem for food security

Supervisors: Professor Michael Tretyakov (Mathematical Sciences), Dr Michael Jones (Veterinary Medicine and Science)

Campylobacter jejuni is a major cause of food-borne infections, and consequently a problem for food security. This project will develop stochastic models for C. jejuni mutation and selection and the evolution of antibiotic resistance. A significant mathematical challenge will be to estimate model parameters using a relatively small amount of in vitro and in vivo data. The student will be trained in foundations of microbiology and genetics as well as becoming an expert in stochastic modelling of biological processes and associated modern statistical techniques.

 

Mathematical modelling of the effect of temperature stress on crop fertility

Supervisors: Professor John King (Mathematical Sciences), Professor Zoe Wilson (Biosciences)

Increased temperatures during flowering have extreme affects on pollen development and thus reproductive success and yield in plants. It has been predicted that this may be the key factor in determining future productivity for many crops, particularly in the temperate cereals such as wheat and barley. This project will model the effects of temperature changes on reproductive success, focusing in particular on the impact this may have on yields for wheat.

It will also explore the influence of this temperature stress on the molecular pathways regulating pollen development by modelling of how the dynamic changes in hormone levels and gene expression are influenced by elevated temperature.

 

Towards sustainable antimicrobial use in agriculture: quantifying the risks of emergence of antimicrobial pathogens

Supervisors: Professor Michael Tretyakov (Mathematical Sciences), Dr Dov Stekel & Dr Jon Hobman (Biosciences)

Antimicrobial resistance is a major threat both to human and animal health. The majority of antibiotic use is in agriculture, thus the threat of antimicrobial resistance, and the appropriate use of antibiotics, are essential to sustainable agriculture and food production. The aim of this project is to develop mathematical models that can improve our capacity to predict the risk of emergence of antimicrobial resistant pathogens within a sustainable agriculture context. Specifically, the student will develop and analyze both spatially homogeneous and heterogeneous stochastic models for the spread of antimicrobial resistance between populations of bacteria in dairy slurry. The model will be based upon the real dairy slurry system in the University of Nottingham farm in Sutton Bonington.

In addition to being based in the sustainability programme, the project will be supported by on-going research in antimicrobial resistance in agriculture and will benefit from experimental measurements carried out by colleagues in Biosciences, Pharmacy and Engineering.

 

Optimisation of the aqueous recovery of oil from rapeseed

Supervisors: Dr Jonathan Wattis (Mathematical Sciences), Dr Etienne Farcot (Mathematical Sciences), Dr David Gray (Biosciences), Dr Vincenzo di Bari (Biosciences)

Oilseed rape is the main oilseed crop grown in the UK and Europe. In seeds the oil is stored within micron-sized organelles called oleosomes or oil bodies (OB). The approach currently used to produce edible oil relies on energy intensive processes in which organic solvents are employed.  The aqueous recovery of OBs from oilseed rape relies on the use of water as solvent and consists of five main steps (Soaking, Grinding, Filtration, Centrifugation & Washing). The final product is a natural, novel, label friendly oil-in-water emulsion.

Although this innovative approach to seed processing (pioneered in Food Sciences, Nottingham) has gained increasing attention from industry in recent years, its upgrade to an industrial level has been constrained by the relatively low oil yield (approx. 20g / 100g of seeds) and by the limited understanding of the role key processing steps play on OB recovery. These steps are the soaking, grinding, and centrifugation.

Experimental work at Food Sciences has allowed partial optimisation of those steps. The minimum soaking time required to achieve optimum seed softening was shown to be 16 hours and optimal grinding time 90 seconds, however, a mechanistic understanding of these process is lacking.

To better understand these processes and provide rational ways to optimize them, we will develop mathematical models of the three key steps mentioned above: (1) the soaking pre-treatment, using models of water diffusion at the scale of single seeds. Here, as well as the differing diffusivities and absorbancies of the hard shell and internal seed tissue complicate the process of water uptake. In the grinding process (2), we propose to model the evolving size distribution of the fragments; and with centrifugation (4), we propose to model the effectiveness of adding solute to the water to increase the density difference between the oil and the solvent/solute. The PhD student will have the possibility to carry out some specific experimental work which will help in the design of a model to optimise water and energy usage and reduce processing time.

 

Mechanical modelling of the stability of Earth's peatland carbon reservoirs

Supervisors: Dr David Large (Chemical & Environmental Engineering), Dr Matthew Hubbard (Mathematical Sciences), Dr Donald Brown (Mathematical Sciences), Dr Bagus Muljadi (Chemical & Environmental Engineering), Professor Neil Crout (Biosiences).

The project involves the development of mechanical models of peatland growth and restoration. Peat is a soft multiphase (solid, liquid, gas) material that stores 1/3 of earth’s terrestrial carbon. Current models combine mass balance and hydrology but none consider the mechanical stability of the peat.  This is a huge oversight as the extremely weak multiphase peat body should deform with ease and this deformation must influence gas emissions and long term stability. The project will develop novel numerical models of peat growth and the mechanical response of peat to the changes in loading, mass balance and hydrology. The student will have the opportunity to visit peatlands in the UK and Malaysia and to link their work to geospatial observations.

 

Mathematical modelling of hormone-regulated rice root growth

Supervisors: Dr Leah Band (Biosciences/Mathematical Sciences), Professor Markus Owen (Mathematical Sciences) and Professor Malcolm Bennett (Biosciences)

We need a significant increase in plant yields to sustain the growing population, which is a major challenge given the complexities of climate change and the need to reduce fertilizer use to maintain healthy ecosystems. Understanding the plant root system is an essential target to increase yields by for maximising water and nutrient uptake under different environmental conditions. The plant root system emerges from the growth and branching rates of the individual root tips, through processes that are controlled by the plant hormone auxin. This project will develop mathematical models to analyse how auxin controls the rice roots. We have new experimental data that provides details of the auxin dynamics in rice roots; we will develop models to gain insights into these data and predict how we can perturb the auxin dynamics to manipulate the architecture of the rice root system. The knowledge gained will contribute to the creation of rice varieties that make best use of the available resources to generate improved yields.

This PhD project will benefit from collaboration with an ongoing BBSRC-funded project that is investigating how rice root architecture affects water-uptake under drought, with experimental groups in Thailand and the Philippines.

 

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