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Team:Warsaw/Stage1/Modeling
From 2010.igem.org
Modeling
The RBS strength depends on RNA secondary structure. We were not sure if it is correct to assume that RBS, that is strong with GFP would be strong with other genes. One way to check it would be to investigate it experimentally - a brilliant but time consuming idea. Modeling has been developed to save biologist time, not it is time to explore its power.
1. First we started looking for suitable software and discovered that it has been very recently created. We are lucky this year.
2. Then we compared the software's prediction against our measurements - to find out if it gives reliable results.
3. Later we in silico measured strength of different RBS parts in various genetic set up - with different reporter genes.
People used to design computational programs that would automaticall locate bacterialribosome binding sittes, various techniques were used[] including neural networks[]. However the software was capable only of finding the sequence and didn't give any information about the strength of RBS. In 2009 Nature issue the revolutionary software has been described[]. RBScalculator allows prediction of RBS localisation and strength,
moreover it designs RBS of desired strength for specific gene sequence. In 2010 another RBS strength predictor was created[].
A mathematical mofdel for transcription initiation has been clearly describrd by ___ in 2010. Upon similare models are based the current predictors. Model contains lots of mathematics but it is based on biological common sense:
We all know that for transcription initiation the ribosome has to be recruted to the RNA.
First there must be accesible RNA molecue
Then the ribosome must
The
There are some events that has not been included in this model e.g.
We have evaluated the accuracy of the RBScalculator by comparing its predictions to our measurements and data from registry. We prepared the in silico constructs comprising of biobrick scar.RBS.E0040.B0015 to reflect the mRNA that was used in wet lab experiments. Then we made RBS strength predictions (RBScalculator reverse engineering mode)using and expressed them as a percent of predicted B0034 strength. Below you can see that the results are more or less in agreement with wet lab measurements. However the program has its limitations. Each result returned by the program has a parameter that says how reliable the prediction is. For J61117 and J61127 we didn't obtain reliable predictions and results were extremely different from measured strength.
Strength of all RBS sequences except J61117 and J61127 was correctly predicted by RBScalculator
In experiment 2, we decided to predict the expression from only RBS sequences that were predicted correctly in experiment 1.
Fig 1. Comparison of our experimental measurements, registry data and predictions for community RBS collection.
This time we prepared in silico constructs with 8 different RBS sequences and all fluorescent proteins that we found in registry. Some proteins had identical sequence at the beginning of the gene e.g. YFP and CFP and differed only by few point mutations later in the gene. In case of identical sequences we used only one representative of the sequence e.g. we used only YFP and omitted CFP. We performed RBS strength predictions for those constructs.
In absolute units the risults for the same RBS differ a lot for various Fluorescent proteiins For instance B0034.GFP gives high result whlile B0043 YFP gives low. In general all predictions for RBS.GFP sequences were much higher that the predictions for RBS.YFP. This data would suggest that it doesn't make sense to measure RBS sequences at all.
However when the he relative strength of RBS sequences was compared it turned out that RBSes in different genetic contexts are comparable. For each fluorescent protein we divided all predicted values by value of B0034 and that protein. And as you can see below the strength of various RBS parts relative to B0034 doesn't depend on genetic context.
Fig 2. RBS strengths in different genetic context relative to B0034.
