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Team:KU Leuven/Project/modelling
From 2013.igem.org
Secret garden
Congratulations! You've found our secret garden! Follow the instructions below and win a great prize at the World jamboree!
- A video shows that two of our team members are having great fun at our favourite company. Do you know the name of the second member that appears in the video?
- For one of our models we had to do very extensive computations. To prevent our own computers from overheating and to keep the temperature in our iGEM room at a normal level, we used a supercomputer. Which centre maintains this supercomputer? (Dutch abbreviation)
- We organised a symposium with a debate, some seminars and 2 iGEM project presentations. An iGEM team came all the way from the Netherlands to present their project. What is the name of their city?
Now put all of these in this URL:https://2013.igem.org/Team:KU_Leuven/(firstname)(abbreviation)(city), (loose the brackets and put everything in lowercase) and follow the very last instruction to get your special jamboree prize!
Modelling the BanAphids
Our project aims to reduce crop loss due to aphid infestations. With an environmental project like ours, the computer is our best friend: through modelling and prediction algorithms we can reduce the real costs of field tests. The figure below shows how our different modeling aspects interact and allows for easy navigation to the different parts. The first step in the modelling was to predict the effect of our pheromones on the environment and the ecosystem through a series of modelling steps: our ecosystem level work. Secondly, the pheromones E-β-farnesene and methyl salicylate will not only affect the environment but also the bacterial cell itself. To figure out the impact of our system on the cellular level we performed a Flux Balance Analysis. Once we knew that our bacteria could handle the production of the pheromones we tried to predict the exact production amounts and find the rate limiting steps. Here our goal was also to feed wet-lab data into our algorithms. Finally, to optimise the impact of the released pheromones on the aphids and the ladybugs, we designed an oscillating transcription factor network to regulate their production. This oscillator also communicates between cells, enforcing the oscillating rhythm onto the whole colony. So this is modelling on a colony level. Summarised, these algorithms allow us to model our system from the cellular metabolism throughout to the environmental impact. Based on our models, we adapted the actual building of the system towards the most effective circuit. Finally, our mathematical predictions will provide significant benefits once we prepare our BanAphids for field tests and will give valuable data to the end user.
