We achieved 3 main aims in our modelling work:
We introduced a novel ensemble modelling approach to iGEM and made this approach accessible to other iGEM teams by sharing our code.
We improved our understanding of our system and used real experimental data to improve our model, using network mechanism analysis and parameter relationship analysis.
We answered key questions that arose during our integrated human practices work, helping to improve the design of our system using cost analysis. All of our models are available on our Github page
|Network Mechanism Analysis||Parameter Relationship Analysis||Cost Analysis|
|Comparing model predictions with experimental data for different potential circuit topologies. read more||Assessing the interlinking nature of specific parameter pairings on the outcomes of the system. read more||Predicting the costs for a range of different system specifications by varying the amount of enzymes based on experimental data. read more|
We focused on modelling the Cell-free Mechanism. The short version is the AlcoPatch relies on alcohol, alcohol oxidase (AOx), horseradish peroxidase (HRP) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) to detect and quantify alcohol levels. The ABTSOxidised produced in the prescence of alcohol is dark green and can be detected spectophotometrically or visually. We focused on this small system because it was possible to obtain a large amount of experimental data for model validation, and because it allowed us to establish and illustrate the ensemble modelling process.
The majority of our experimental data came from the proof-of-concept study of the analogous system of glucose and glucose oxidase (GOx) rather than alcohol and AOx. The reaction network of the two sytems is the same and only some kinetic parameters differ.
A schematic diagram of the final circuit of our detection system is given below. For more information about the individual reactions click on the blue enzyme boxes.
Alternatively you can click on the enzyme name below:
Incomplete and uncertain knowledge of kinetic parameters is a common problem when building models for synthetic biology. Ensemble modelling is one strategy to deal with this problem. Instead of running our model with a single set of specific parameters (for example rate constants), we run our model multiple times using different sets of plausible parameter values and analyse the predictions as an ensemble. We collected all the available parameter values from published literature and took into account the uncertainties that are associated with them. The resulting confidence in our parameter values was then described by probability density functions. This has created probabilistic outputs allowing us to make rigorous conclusions about our reaction mechanism – and to assess which predictions are reliable, and where we are lacking information. To explore the theory of this process please click the boxes on the diagram below.
Alternatively you can click on the step name below: