BME494s2013 Project Team1: Difference between revisions
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To track the overall GFP production over time, we analyzed 24 images taken over a 24 hour period (one image was taken every hour). We used ImageJ software to track the GFP production of 12 individual cells over the 24 hour period. Since the GFP production was directly proportional to intensity in the images, we recorded the average intensity corresponding to a specific time for each of the cells that we tracked. The average cell intensities for each of the individual images was calculated based on the collected data for the 12 cells. <br> | To track the overall GFP production over time, we analyzed 24 images taken over a 24 hour period (one image was taken every hour). We used ImageJ software to track the GFP production of 12 individual cells over the 24 hour period. Since the GFP production was directly proportional to intensity in the images, we recorded the average intensity corresponding to a specific time for each of the cells that we tracked. The average cell intensities for each of the individual images was calculated based on the collected data for the 12 cells. <br> | ||
<b>Raw Data of Yeast Cells | <b>Raw Data of Yeast Cells GFP Production</b><br> | ||
[[Image:DJMyeast007.jpg|300px|Raw Data of Yeast at 7 hours]] | [[Image:DJMyeast007.jpg|300px|Raw Data of Yeast at 7 hours]] | ||
[[Image:DJMyeast010.jpg|300px|Raw Data of Yeast at 10 hours]] <br> | [[Image:DJMyeast010.jpg|300px|Raw Data of Yeast at 10 hours]] <br> | ||
[[Image:DJMyeast016.jpg|300px|Raw Data of Yeast at 16 hours]] | [[Image:DJMyeast016.jpg|300px|Raw Data of Yeast at 16 hours]] | ||
[[Image:DJMyeast022.jpg|300px|Raw Data of Yeast at 22 hours]] | [[Image:DJMyeast022.jpg|300px|Raw Data of Yeast at 22 hours]] <br> | ||
<!-- Describe summarize how you measured GFP intensities, plotted charts in MatLab, make a curve of best fit, and tried to determine the maximum rate of GFP production. You don't have to include your raw values, just the graphs and equations. You may include some small images of the GFP-expressing yeast. If you didn't get a nice peak, explain how your image analysis might be changed to improve your outcome. | |||
Images shown were taken at 7, 10, 16, and 20 hours respectively.<br><br> | |||
- show plot of data and discuss outcome. | <!-- Describe summarize how you measured GFP intensities, plotted charts in MatLab, make a curve of best fit, and tried to determine the maximum rate of GFP production. You don't have to include your raw values, just the graphs and equations. You may include some small images of the GFP-expressing yeast. If you didn't get a nice peak, explain how your image analysis might be changed to improve your outcome. - show plot of data and discuss outcome. | ||
- include some of the pictures of the raw data | - include some of the pictures of the raw data | ||
- wrap up section to explain how the curves could be improved | - wrap up section to explain how the curves could be improved--> | ||
<b>MATLAB Modeling</b><br> | |||
We plotted the average intensity versus time in MATLAB. A 4th order polynomial was then fitted to the plot using MATLAB's basic fitting tool. The resulting plot can be seen below. <br> | |||
[[Image:Data_with_4th_degree_poly_fit.png|1000px|center|Data with 4th Degree Polynomial "Best Fit"]] | |||
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DJMyeast010.jpg | DJMyeast010.jpg | ||
[[Image:Best_Fit_Derivative_Plot.png| | [[Image:Best_Fit_Derivative_Plot.png|700px|Plot of the Derivative of the "Best Fit" Polynomial]] | ||
Ideally, the GFP production rate measured by this method could be entered as a value for [which parameter] in the Ceroni et al. model. | Ideally, the GFP production rate measured by this method could be entered as a value for [which parameter] in the Ceroni et al. model. | ||
Revision as of 01:32, 29 April 2013
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Overview & PurposeEscherichia coli, commonly referred to as E. coli, has many different strains. The most commonly known serotypes of these bacteria can cause serious food poisoning or even fatality in humans. However, most strains are completely harmless. These strains are usually found in the gut of the host and help by producing K2 and helping with digestion. The presence of these bacteria is very beneficial for it helps to prevent pathogenic bacteria from being present in the intestine.[1]
Background
"Only LacZ and LacY appear to be necessary for lactose catabolism" [3].
Design: Our genetic circuitOur gene switchTo create our gene switch, we chose to replace β-galactosidase with a gene coding for a variant of GFP (green fluorescent protein). The switch is active or “on” in the presence of IPTG (Isopropylthiogalactoside) and causes the production of a cyan fluorescent color. Additionally, we did not want our switch to be influenced by the presence of glucose so we chose a promoter that is not sensitive to the CAP-cAMP complex and as a result IPTG is the sole input that causes an effect to the switch. Device Structure and PartsThe BioBricks for our design came from the Registry of Standard Biological Parts. Our system utilized two main BioBricks along with an appropriate vector backbone (see figure below). IPTG-Inducible Lac Promoter
Circuit_Diagram_of_Sweet_Cyan_Switch.PNG
Building: Assembly schemeThe assembly strategy employed in the design of this device is a Type IIS assembly strategy. The steps and procedures involved in this single-pot assembly are described below. MutagenesisOne of the parts chosen by our group contains a BsmBI cut site, which would disrupt the digestion and ligation process further along the assembly strategy. In order to counteract this, the part is subjected to mutagenesis to alter a selected base pair within the DNA to eliminate the BsmBI cut site while keeping the integrity of the coding sequence intact. This site-directed base substitution is performed using two primers with centrally located substitution sites to alter the selected base pair on a methylated plasmid. After the part has completed the mutagenesis, the DNA sequence (which is linear at this point in time) undergoes in vitro recombination reaction. The host cell then "circularizes" the mutated part DNA and digests the original methylated plasmid. If the methylation of the original plasmid is skipped, an additional purification step would be necessary to extract the mutated plasmid.[5]
PCRPCR, or polymerase chain reaction, is then used to amplify the DNA sequences and create modular fragments for ease of assembly. A DNA fragment is combined with its forward and reverse primers, nucleotides, and DNA polymerase. This mixture is then inserted into a thermal cycler, which cycles the internal temperature with predetermined values. These changes in temperature cause the DNA to separate, the primers to pair with their respective DNA strands, the DNA polymerase to activate, and a new DNA strand to be formed. The thermal cycling procedure, listed below, is continued until the specified fragment is amplified sufficiently for use. Thermal Cycling
Digestion and ligation reactionThe Type IIS assembly strategy employs a single-pot assembly, wherein digestion and ligation occur in tandem. To prevent the futile digestion/ligation loop that could result from this assembly strategy, the BsmBI restriction enzyme is used. BsmBI is useful in this scenario because its binding site is remote from its cutting site. In the digestion and ligation reaction, BsmBI binds to its binding site, CGTCTC, and cuts at a location further along the DNA strand. This creates a 4 base pair "sticky overhang." Complementary "sticky overhangs" then pair up and are connected by the ligase. The thermal cycling procedure used in the digestion and ligation reaction is shown below. Thermal Cycling
Parts and primersThe following table lists the BioBricks used in the construction of the Sweet Cyan device. The individual parts can be found with a simple search of the part IDs at the Parts Registry.
Testing: Modeling and GFP imaging
A lac switch modelWe used a previously published synthetic switch, developed by Ceroni et al.[6], to understand how our system could potentially be modeled and simulated. The graphic to the left depicts the relationships between the parameters of the lac operon switch described by Ceroni using a network diagram illustration. The parameters shown in the illustration relate to cell processes and could be used in forming a cohesive mathematical model of the cell's operation. In order to approximate the behavior of this set-up, a mathematical model can be developed based upon the relationships between the processes found in the cell. These relationships can be expressed in mathematical terms using numbers that relate to the system, including creation or decay rates, concentrations, or various constants. The actual values for these parameters can be sourced from experimentation, literature, or a predefined steady-state. If a model is well-defined and the necessary parameters known, a person may use the model to ascertain the state of a cell at a given point in time. For example, if an experimenter wanted to know the decay of the GFP protein molecules at a given point in time in a single cell, the following equation could be written using the notation found in the table below. Decay = G × λG/L The formula takes the concentration of the GFP protein in molecules per cell ("G") and multiplies it by the protein degradation rate in minutes-1 ("λG/L"). This results in a decay value for GPF in molecules per minute per cell. The Ceroni et al. model and the network diagram illustration use the table of variables and parameters seen below in their representation of the lac switch. The variables related to a particular cell process are located near to that process in the network diagram illustration.
An interactive modelWe used a model of the natural Lac operon to understand how changing the parameter values changes the behavior of the system. By changing the initial concentration of input (IPTG in this case), we were able to estimate the threshold that produces an "on" state in the system. Initially, the code had the concentration at 0.32 which is seen in the β-galactoside (Bgal concentration) vs. time plot (Figure 1). This value was changed again to 0.045 in determining the threshold that produces this "on" state (Figure 2). After proceeding to go up and down with these a values, a threshold was indeed found where the output concentration of IPTG was sustained and is about 0.064 (Figure 3).
Collecting imperical values to improve the model
Images shown were taken at 7, 10, 16, and 20 hours respectively.
Stakeholder Assessment
SUPPORTS & UNDERSTANDS Public and laymen knowledge of E. coli may consist of only what they hear in the news, which often relates to food contamination scares; for this reason, stakeholders of the device itself may not support its production if they believe that widespread stigma may prevent successful marketing and sales campaigns. DOES NOT SUPPORT & DOES NOT UNDERSTAND
Our Team
Works Cited |