Team:Edinburgh/Modelling

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Modelling BRIDGEs

An integral part of the engineering approach to biology is the use of modelling techniques that attempt to predict and understand the behaviour of complex biological systems before they are actually fabricated. One of the greatest advantages of models is that they can present an abstract picture of the activity of a part or device long before the actual biology - limited by the time needed for protocols such as PCR and transformation - can. Furthermore, they can help to simplify the mind-boggling amount and complexity of data and interactions involved into a more concise form with some measure of predictive ability.

On the other hand, modelling remains very much a guessing game. Abstractions and assumptions are made at every stage of the process, and even then the finished model will fail to capture all the intricacies of working in biology: interference between various parts and devices, the host not always being a benevolent chassis, and so forth. The extraction of meaningful biological data upon which to base the model, for example kinetic rate parameters, is an extremely time-consuming process, fraught with difficulty and inaccuracy .

Hence, biological modelling can be considered to be a little bit of a black art. If you're good, it will give you an answer; if you're very good, it might even be close to the truth.

Still, in order to effect in the future the rational design and engineering of biological systems, advances in modelling techniques remain crucial. As happened in chemistry in the 1940s and 1950s, capturing the enormous complexity of such processes in a way useful for applications can lead to the establishment of new and useful disciplines. It is our fervent hope that this section of our project has made at least some small progress in this direction.

Our Project

Our primary goal was to ensure that all of our endeavours in the wet lab had a corresponding modelling element, including but not limited to a model for simulating the BRIDGE protocol, a model for the core repressilator system, a model for each of the light sensor pathways and how they responded to stimulation by light of the appropriate wavelength, and a model for attempting to simulate a colony communicating as one via light.

On the other hand, we didn't want to model just for the sake of modelling; in our opinion, modelling should always have a clear objective, aiding and adding to the biology in some way. For example, by highlighting possible problems or inefficiencies in the biological systems in question, or by testing two separate designs against one another to determine which of them would be more efficient in achieving the desired outcome, we would be able to solve theoretical conundrums and help our wet-lab team to eliminate unnecessary time and work. Although the time and feasibility constraints of the project and the setbacks we suffered along the way meant that this was not always possible, work continued throughout on refining and rebuilding the model to flexibly adapt to the continually evolving needs of our system.

Table of Contents

• An introduction to our primary technique, the Kappa biological modelling language.
• Modelling the BRIDGE protocol in Kappa.
• Modelling the individual light sensing pathways and their integration into a repressilator system in Kappa.
• Modelling the intercellular signalling processes.
• Some of the tools that we used during the modelling process, and an explanation for them.
• A summary of what we achieved through the construction and analysis of our models.
• Our vision of the future of the modelling process, and where we would like to go next.
• References used throughout the section.