Team:Valencia UPV/Modeling
Modeling
Do you think it is possible to mathematically describe a cell? Would you like to know the possibilities that Printeria modeling offers you?
One of the fundamental bases of the Printeria project has undoubtedly been mathematical modeling. Thanks to the development and application of new mathematical models, it is possible to quantify the expression of proteins in cells, and therefore characterize through different experiments the parts designed by Printeria. From the Printeria modeling team, we intend to reach different goals:
Design simple mathematical models based on differential equations that describe the biochemical processes of a cell. With them, we can simulate the different genetic circuits that Printeria allows us to build.
Develop a Simulation Tool that allows the user to visualize a prediction of the results of their experiment before running it in Printeria.
Optimize model parameters to match simulation results to experimental data obtained from Printeria constructions.
Characterize the parts of our Part Collection from the optimization results and provide the user with all the information about the Printeria kit.
Although in the development of the project we have dealt with all these aspects, all of them have a single purpose: to understand and describe in a mathematical way the biological processes that take place inside the cell.
Models & Experiments
Characterisation procedures
Simulation Tool
Models & Experiments
Characterisation procedures
Valencia UPV iGEM team has designed an extensive Part Collection in purpose of allowing the user to design multiple genetic constructions and experiments. One of our main objectives has been to show the user clear and structured information about the pieces that make up the Printeria kit. For this reason, we have considered the characterization of the parts as a priority when developing the project. In this way, we have elaborated some procedures which have allowed us to systematically obtain and structure information from the parts.
Procedure for obtaining protein spectra
Obtaining excitation and emission spectra is a fundamental aspect in the process of characterization of a fluorescent protein. Each protein has a characteristic spectrum, which indicates the energy in which the molecule is excited or emits at a certain wavelength.
The characterization of the reporter proteins by excitation and emission spectra are of great importance in the experimentation. When contrasting experimental information with the theoretical results of mathematical models, we experiment with numerous reporter proteins, such as fluorescence proteins or chromoproteins. However, the >fluorescence data obtained must be corrected applying diferent operations in order to obtain representative fluorescence data:
The subtraction of the blank fluorescence:
F_{subs} = F - F_{blank}The quotient of the fluorescence with the gain:
F_{gain} = \frac{F_{subs}}{G}The quotient of the fluorescence with excitation and emission efficiency:
F_{corrected} = \frac{F_{gain}}{\frac{exc(\%)em(\%)}{100}-
Measuring equipment: Biotek Cytation3
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96 well plate
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MATLAB 2018a software
We look for the theoretical spectraof the protein to be measured (or similar molecules) to determine the wavelength at which the protein is excited or emitted at maximum energy, i.e. where the theoretical spectral peaks occur.
We define the protocol of our equipment to get the absorbance and fluorescence dataset. In our protocol, the most important parameters to be established are summarized in the following Table.
Parameters
Description
Number of samples
From 3 to 6 samples of reporter protein and 3 samples of blank (BLK)
Temperature
37 ºC
Shake
Double Orbital. 01:00 (MM:SS)
Absorbance. Optical Density (OD)
Wavelenght at 600 nm emission
Excitacion and emission scans
The scans occur between two wavelength limit values. The established range will depend on the theoretical spectrum of the protein.
Excitacion and emission wavelengths
These values will depend on the range, and therefore on the spectrum. Values far from the theoretical peak lead to more attenuated fluorescence curves, and values very close to the peak can lead to overlap and error in reading the data. Therefore, a compromise must be reached between curve resolution and reading overlap.
Gain (G)
Normally the gain value G = 60, although for proteins with lower fluorescence, it is recommended that G takes higher values.
The experiment is introduced, and the experimental absorbance data and the fluorescence curves of the samples are obtained with the blank correction applied.
The dataset is exported to an Excel file.
The MATLAB script spectrum.m for fluorescent proteins is executed:
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Dataset is extracted from the Excel file.
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The fluorescence curves of all samples are averaged, and the result is normalised (from 0 to 100%).
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Graphs of the normalized absorption and emission spectra are plotted. The X-axis represents the wavelength (nm), and the Y-axis represents the normalized fluorescence intensity (%).
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Until now, only the first two transformations of the data were applied, but with the obtaining of the spectra, it is possible to normalize the fluorescence data in any protein.
Owing to this reason, a protocol has been established in the lab by Lab and Modeling team to obtain the spectrum of any reporter protein.
Materials:
Method:
sfGFP spectra
>Comparison between sfGFP and GFP relative fluorescence intensity
Simulation Tool