Team:Newcastle/Modelling/Introduction

Introduction to Modelling

Unlike other biological disciplines, which are usually descriptive or prescriptive, Synthetic Biology aims to design and create novel biological systems. However the genetic systems targeted for modifying are very complex. Before going into the lab one must first have a reasonable approximation of what they expect to happen. When building a car, you have every single piece mapped out, and know exactly what should happen. Indeed, Synthetic Biology is distinguished by its attempts to incorporate and apply engineering principles to biology.

To achieve this it is common practice to model the behaviour of the biological system of interest before building it. Whilst fairly rudimentary, a simplified model can inform the design process, providing results otherwise very difficult to predict without actually doing the experiments. The effects of slight variations can be measured and accounted for, and we can generate hypothesis about the behaviour of the system, and modify our protocols accordingly. However modelling is not perfect. Simplifications and assumptions must regularly be made. Furthermore it can be difficult to find the parameters necessary to model a system, with species concentrations or reaction rates unknown. Even when the data is there it can be very time consuming to find, or only be available for a few model organisms. Furthermore the interactions within cells are so complex that we cannot pretend to understand them, let alone model them. Therefore a model can only represent some of the molecular interactions that occur, especially as many species will be ignored for the sake of simplicity.

Despite this it is often possible to build a model that gives a rough approximation to the experimental results. As computers get more powerful and the body of research accumulates the outcomes become more reliable.

Modelling will have a big impact, both in and beyond Synthetic and Systems Biology.

Our modelling can be split into four sections:

• The prediction of how L-forms will grow in constrained shapes using Matlab.
• A java animation showing green/red fluorescent L-forms moving in a chamber and fusing, showing how we expected the colours to change.
• The expression of HBsu-xFP at differing IPTG concentrations
• How our L-form switch BioBrick causes the loss of the cell wall.