Team:Oxford/InterLab
Introduction: What is InterLab?
Standardisation is one of the main principles of synthetic biology. This includes a standard way of making measurements and expressing quantities across labs around the world. Standardisation is challenging for fluorescent measurements of genetic constructs because different labs use different protocols for making constructs, for measuring and for processing fluorescence. Without a standard way of expressing fluorescence it is extremely difficult to compare measurements for repeats of the same part from different labs or for different constructs. The iGEM InterLab study aims to standardise the way that fluorescent measurements are made and therefore make comparisons easier.
This year, iGEM has provided a standard protocol for making measurements in a plate reader, a spectrophotometer or a flow cytometer. In addition, to remove variations in the production of the constructs, all biobricks to be tested have been sent in plasmid form. A standard way of data processing has also been devised by iGEM HQ by sending a preconstructed Excel data sheet where each team will input its data and fluorescence would be calculated automatically in a standardised way.
Overview
Constructs
The Interlab study of 2016 aims to characterise the strength of 3 constitutive promoters from the Anderson Collection by measuring the fluorescence of the GFP encoded downstream of each of the promoters (promoter strength is proportional to GFP fluorescence). The three constructs, named Test Device 1,2 and 3 (TD1, TD2, TD3), include one of the promoters (J23101, J23106 and J23117 respectively) and share the RBS (B0034), GFP (E0040) and the transcriptional terminator (B0015).
We developed four variations of promoters that can be incorporated into our system. Modelling was used in order to simulate and later to characterise the behaviour of these promoters to demonstrate which of the promoters are suitable for the use of the project.
The four promoters are described below:
1. pCopA
2. pCopA with Feedback
3. pCusC
4. pCusC with Feedback
For each of the promoters, we developed a kinetic model to simulate / analyse its behaviour.
For more information about the parts and sequences, please visit our Parts page.
Model A. Reaction Kinetics
To predict copper chelation efficiency of our bacteria, we developed kinetic models to simulate reactions in the bacterial cell for each of our four promoters.
Method
In order to simulate the transition of different quantities, eventually reaching equilibrium, we used ordinary differential equations (ODEs) that can be solved by MATLAB.
Chemical reactions such as
can be modelled in a set differential equations
However, this is true only under an assumption that the chemical bindings are uncooperative - independent to each other.
Our chelators, Csp1 and MymT has 52 and 8 copper binding sites respectively. These bindings are cooperative, and a close approximation is formalised in the Hill function shown where Y denotes the fractional saturation of total copper binding sites.
The constant K is the half-saturating concentration of ligand, and so can be interpreted as an averaged dissociation constant. For Csp1, ¬ K¬_Cu = 1.3 * 10^-17 M, n = 2.4. [1]
From this, we can estimate the amount of copper that is bound to the chelator for different copper concentration.
[1] Nicolas Vita et al., 2015. A four-helix bundle stores copper for methane.
For modelling gene expression regulated by transcription-factor binding, we calculated the fraction of promoters in each possible state. To simplify the simulation when we don’t know the exact curve of copper concentration - transcription rate, we made the following assumption. (Later, we improved our model by developing model B)
For instance, for system 1 and 2, repressor CueR, copper and promoter binds according to the following:
Here, the promoter can either be in state P, P.CueR or P.CueR.Cu, where state P.CueR.Cu is the active state. Therefore, once we calculate the fraction of promoters that are in state P.CueR.Cu, we can figure out the rate of transcription.
Fraction in state P.CueR.Cu:
Therefore, we can model mRNA transcription as:
where the second term is for degradation and dilution.
