BME494s2013 Project Team2: Difference between revisions
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When analyzing this model, we were concerned with three main things: how mRNA concentration changed over time, how IPTG concentration changed over time, and how the concentration of β-galactosidase changed over time. We ran a simulation setting the concentration of IPTG at an arbitrary value of 0.32 and produced the following three graphs: | When analyzing this model, we were concerned with three main things: how mRNA concentration changed over time, how IPTG concentration changed over time, and how the concentration of β-galactosidase changed over time. We ran a simulation setting the concentration of IPTG at an arbitrary value of 0.32 and produced the following three graphs: | ||
[[Image:MRNAconc.jpg |thumb|noframe|375px|right|mRNA Concentration vs. Time]] | [[Image:MRNAconc.jpg |thumb|noframe|375px|right|Figure 1: mRNA Concentration vs. Time]] | ||
[[Image:IPTG.jpg |thumb|noframe|375px|right|IPTG Concentration vs. Time]] | [[Image:IPTG.jpg |thumb|noframe|375px|right|Figure 2: IPTG Concentration vs. Time]] | ||
[[Image:Bgalv1.jpg |thumb|noframe|375px|right|β-galactosidase Concentration vs. Time]] | [[Image:Bgalv1.jpg |thumb|noframe|375px|right|Figure 3: β-galactosidase Concentration vs. Time]] | ||
As we can see from the graphs on the right, plotting mRNA concentration over time results in a curve that oscillates around the value of 6.06x10^-4. Plotting IPTG concentration over time results in a straight line, which is unsurprising considering that the model assumes that IPTG stays constant over time for simplification purposes, which is of course not true in real life. Plotting the concentration of β-galactosidase over time results in a curve that seems to max out at a value of | As we can see from the graphs on the right, plotting mRNA concentration over time (Figure 1) results in a curve that oscillates around the value of 6.06x10^-4. Plotting IPTG concentration over time (Figure 2) results in a straight line, which is unsurprising considering that the model assumes that IPTG stays constant over time for simplification purposes, which is of course not true in real life. Plotting the concentration of β-galactosidase over time (Figure 3) results in a curve that seems to max out at a value of 4.5 | ||
Things we need to include: | Things we need to include: | ||
Revision as of 22:12, 25 April 2013
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Overview & Purpose
Background: The Lac OperonThe Lac Operon is a gene specific to E. Coli that controls the cell's digestion of lactose. It consists of a promoter, an operator, three structural genes, and a terminator. It is both positively and negatively regulated, allowing expression to be contingent on the concentrations of glucose and lactose in the cell.
STRUCTURE
In addition to the structural genes, the Lac Operon includes a promoter and an operator region. The promoter region is the area to which the Lac I repressor and the CAP-cAMP complex bind, the mechanics of which will be discussed later (see Positive Regulation and Negative Regulation).
Why does this phenomenon occur? Well, like stated before, lactose is the cell's last resort energy source because it requires more energy from the cell to digest than does glucose. The enzyme that digests lactose is β-galactosidase, which can only be produced by initiating transcription of the Lac Operon. Thus, to be able to digest lactose, the cell needs to initiate transcription of the Lac Operon.
The genes encoding the LacI repressor are actually located upstream of the Lac Operon. The LacI gene is not regulated; therefore, it is produced continuously. It binds to the Lac Operon in the promoter region; however, it does not bind if there is lactose in the cell. Why is this? Well, the cell produces very low levels of β-galactosidase even when not in the presence of lactose. In these very low lactose conditions, β-galactosidase has a different function: it cleaves lactose and recombines it to form allolactose, which acts as an inducer for LacI. It binds to LacI and causes a conformational change, which in turn makes LacI unable to bind to the promoter region of the Lac Operon.
POSITIVE REGULATION: CAP-cAMP Complex
SUMMARY
If we analyze it from a digital logic context, we can describe glucose and lactose as inputs, and the transcription of β-galactosidase as an output. Furthermore, we can build a logic circuit symbolizing the operon's functionality (illustrated in diagram on left). When glucose acts as an input, it produces a NOT gate functionality (See Table 2). When lactose and the NOT gate output of glucose are incorporated as inputs to the system, they produce an AND gate functionality (see Table 3). Furthermore, there are a couple of other other proteins that "mimic" the function of lactose as an input for the natural lac operon. Among these are IPTG (used for our switch), and the previously mentioned allolactose which is an isomer of lactose.
Design: Our genetic circuitOUR GENE SWITCH:
As described above, the structural protein regions of the natural Lac Operon can be replaced by various other protein coding regions to alter the output of the Lac Operon. In the case of our gene switch, we chose to replace β-galactosidase with a gene coding for GFP, or green fluorescent protein. We used the lactose mimic IPTG as our system's input. Therefore, our switch turns "on" in the presence of IPTG, and produces a green fluorescent color as its output.
DEVICE STRUCTURE
Brick 2: GFP Production Brick
How it Works: The Role of IPTG and Lac-I
On the other hand, when an IPTG input is added to the system, results in the following:
Building: Assembly Scheme
Testing: Modeling and GFP Imaging
We used a previously published synthetic switch, developed by Ceroni et al., to understand how our system could potentially be modeled and simulated using the mathematical simulation program MatLab. A mathematical model is a mathematical representation of system behaviors defined by the relationships between various system parameters. Parameters are simply different values that affect the behavior of the system. One could even use a simple algebraic equation to represent a mathematical model. In the following equation,
the equation "3x - 7" would be a mathematical model of the system y. Because the value of x affects the ultimate value of y, x would be a parameter of this system.
In the case of this particular mathematical model, the system that was modeled was the natural Lac Operon. Some of the parameters that were used to describe its behavior are as follows:
When analyzing this model, we were concerned with three main things: how mRNA concentration changed over time, how IPTG concentration changed over time, and how the concentration of β-galactosidase changed over time. We ran a simulation setting the concentration of IPTG at an arbitrary value of 0.32 and produced the following three graphs: As we can see from the graphs on the right, plotting mRNA concentration over time (Figure 1) results in a curve that oscillates around the value of 6.06x10^-4. Plotting IPTG concentration over time (Figure 2) results in a straight line, which is unsurprising considering that the model assumes that IPTG stays constant over time for simplification purposes, which is of course not true in real life. Plotting the concentration of β-galactosidase over time (Figure 3) results in a curve that seems to max out at a value of 4.5 Things we need to include:
AN INTERACTIVE MODEL
Human Practices
Our Team
Works Cited[1] Heller, H. Craig., David M. Hillis, Gordon H. Orians, William K. Purves, and David Sadava. Life: The Science of Biology. Sunderland, MA,: Sinauer Ass., W.H. Freeman and, 2008. N. pag. Print. [2] Escalante, Ananias. "Regulation I." Class Notes. University of Arizona. 20 February 2013. [3] Registry of Standard Biological Parts. Web. 25 Apr 2013. <partsregistry.org>. [4] Slonczewski, Joan, and John Watkins. Foster. Microbiology: An Evolving Science. New York: W.W. Norton &, 2009. Print. [5] Schmidt, Markus. Synthetic Biology: Industrial and Environmental Applications. Weinheim, Germany: Wiley-Blackwell, 2012. Print. [6] Filho, Ernesto R., Fransisco L. Nunes, Jr., and Sidney O. Nunes. "Synchronus Machine Field Current Calculation Taking Into Account the Magnetic Saturation." SciELO - Scientific Electronic Library Online. N.p., May 2002. Web. 26 Apr. 2013. |