Below is a listing of the academic projects I have been involved with. I have worked on projects involving the mathematical modelling of plants in studies of epidemiology and evolution, along with studies in bioinformatics and molecular biology.

Seed treatment differential equation based model (Rothamsted)

A very basic representation of the epidemiological model used. H is healthy plant area, L is latent infected area and I is infectious area.
A very basic representation of the epidemiological model used. H is healthy plant area, L is latent infected area and I is infectious area.

Read the paper here.

Fungicides can be applied as foliar sprays or seed treatments, but are susceptible to fungicide-resistance. To compare the selection pressures from both treatments I developed a differential equation based model in C++ that would calculate the fungicide effective life after the treatment types in isolation or in combination.

The model involved the simulation of the 11 leaf layers of

Growth of the simulated leaf layers over a season
Growth of the simulated leaf layers over a season

a winter wheat crop as infected by the pathogen Zymoseptoria tritici. Over time (measured as degree days) the volume of fungicide from the seed treatment pool would distribute amongst the leaf layers (imagine it like plumbing), and if specified at certain time points fungicide would be added as a spray.

sens_resist
The dynamics of the sensitive and resistant pathogen strains over time (growth seasons)

The model included a sensitive and resistant strain of pathogen, which would consume the plant healthy leaf area. Dose response curves would determine how much

the infected area of the sensitive strain would decline at certain fungicide concentrations, whereas the resistant strain would not decline at all, and so would be selected for over the simulated growing seasons. Once a certain proportion of healthy area was consumed, disease control was deemed lost, and so we would arrive at the effective life.

Plant Evolution Modelling: University of Warwick

Read the papers here and here

The evolutionary processes behind crop domestication is what allowed us to cultivate plants and develop agriculture, making the shift from hunter-gathering to the reliance upon agriculture of today possible. Selection under domestication led to traits such as larger seed sizes and tougher grain rachises that have made plants more amenable to farming, yet the process of domestication is still unclear.

Agrisims: proof of concept. The simulated crops are growing around the pool of water in the bottom right, and the availability of water decreases linearly with distance. The crops are adapting to the availability of water, with the red crops having an increased fitness on the top left corner of the environment where water is more scarce.
Agrisims: proof of concept. The simulated crops are growing around the pool of water in the bottom right, and the availability of water decreases linearly with distance. The crops are adapting to the availability of water, with the red crops having an increased fitness on the top left corner of the environment where water is more scarce.
The different layers of regulation in Agrisims: gene network, genome, individual, population and environment
The different layers of regulation in Agrisims: gene network, genome, individual, population and environment

At the University of Warwick I developed a spatial agent-based model called Agrisims that was used to simulate the process using the Java programming language. Each simulated individual contained a collection of genes that were able to function in a network, take in an input from the environment and generate an output contributing to an individual’s trait and thus its fitness (for example, the degree of reliance on moisture, right).  Mutation of the gene would thus impact on it’s fitness, leading to selection for certain alleles over others.

Elements of the work were published in peer review papers and at the 2012 conference for the Society of Molecular Evolution (SMBE). In the latter publication I used Approximate Bayesian Computation (ABC) to calculate liklihood densities of model parameters involving the tough grain rachis trait using observed archaeological data. Results showed that crops may have been harvested early in the year (green harvesting) lowering the selection pressure for this trait.

ag_gui
The graphical user interface to Agrisims included wizards for making new species and new genes.

Cheap PCR primer design at the University of Warwick

Read the paper here

To be continued!