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Modelling - Overall Description
Personal note

I have never heard about the term ‘Synthetic Biology’ and the competition ‘iGEM’ until one day I received this email to recruit engineers. As I was hunting for something productive to do for the summer, and having had a strong interest in medicine and biology, I instantly applied for it. GREAT! Few days later, I joined the team, not knowing what the hell I was going to be working on.

Since then, I have learnt a lot more about biological systems, and ways to model them. Synthetic biology really is an AMAZING branch of research. The things you can design bacteria to do are limitless and will be never ending. iGEM has been a great way of self teaching, and motivation while working in a inter-disciplinary undergraduate team. I look forward to hearing about outstanding breakthroughs from ‘Synthetic Biology’ in the future.

Our system requires a lot of components to function properly, so we decided the aim of our modelling would be to model different setups on how it should be put together, and pick the best one suited to the task of our biosensor. This is a cheap way of doing things, and can be done many times. This arrangement would then be made in the wet lab if time allowed.

We thought we would do something different this year from the traditional ordinary differential equations type modelling. The disadvantage of a model based on systems of differential equations is the necessity to enumerate every potential molecular species that could arise in the modelled system. Rule-based modelling on the other hand is much simpler. It describes biological interactions in terms of rules without the need to make presumptive choices about which terms to exclude from the model for simplification.

The modelling software we used is Cellucidate (Kappa language), an advanced, web-based platform for biological modelling. In Cellucidate, the molecular components of the cell are ‘agents’. These have sites that can undergo changes of state and can bind to the sites of other agents, as defined by the set of ‘rules’ that describe all of the permissible interactions between them. The dynamic behaviour of the modelled system can then be explored by executing the model using Cellucidate's stochastic simulator. The state of every agent in the system and every rule based events are observables, making the model easily understood. More details of this can be found here.

In addition to this, we also did some modelling of how the system would operate in real life which can be found here.

Edinburgh University iGEM Team 2009