Difference between revisions of "BME494s2013 Project Team1"

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(Testing: Modeling and GFP Imaging)
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|+ Lac Switch Model: Important Variables and Parameters
|+ '''Lac Switch Model: Important Variables and Parameters'''<ref>Ceroni, F. et al (2010). ''Rational Design of Modular Circuits for Gene Transcription: A Test of the Bottom-up Approach''. Journal of Biomedical Engineering, 4(14).</ref>
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! α<sub>G</sub>
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| GFP rate of synthesis
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! K<sup>I</sup><sub>x</sub>
! K<sup>I</sup><sub>x</sub>
| equilibrium binding constant for the binding of induced LacI molecule to the O<sub>x</sub> operator sequence
| equilibrium binding constant for the binding of induced LacI molecule to the O<sub>x</sub> operator sequence
| molecules/cell
| molecules/cell

Revision as of 13:05, 28 April 2013

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Overview & Purpose


Escherichia 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.

The Lac switch that we have created in the genetic coding of E. coli bacteria produces a glowing blue color that initially runs off of glucose and eventually runs off of lactose. With this technology, we can create a glow stick that can be used in emergency kits that will provide light in dire situations. By using a non-harmful strain of E. coli, we can create an environmental conscious and biodegradable glow stick that will not cause harm to the surroundings.

This technology will prove to be very helpful for hunters or those who are outdoors for they will not have to worry about disposing of their light source. Used like a regular glow stick, the different components of the device will remain separated and will be mixed together to produce light once a certain amount of force is applied.


Basic Components of a Lac Operon


Natural Lac Operon with Various Parts [1]

The lac operon itself is a set of genes found in certain bacterias' DNA that is required for the transport and metabolism of lactose. Most commonly found in Escherichia coli, the operon was the first example of a group of genes under the control of an operator region to which a lactose repressor (LacI) binds.

The Lac operon functions as a single transcription unit and in its basic form comprises of an operator, a promoter, and one or more structural genes such as a regulator or terminator that are transcribed into one polycistronic mRNA. Typically, the structural genes include LacZ, LacY, and LacA.

  • LacZ encodes β-galactosidase, an intracellular enzyme that cleaves the disaccharide lactose into glucose and galactose.
  • LacY encodes β-galactoside permease, a membrane-bound transport protein that pumps lactose into the cell.
  • LacA encodes β-galactoside transacetylase, an enzyme that transfers an acetyl group from acetyl-CoA to β-galactosides.

"Only LacZ and LacY appear to be necessary for lactose catabolism" [2].

When the bacteria are transferred to lactose-containing medium, allolactose (which forms when lactose is present in the cell) binds to the LacI repressor, inhibits the binding of the repressor to the operator, and allows transcription of mRNA for enzymes involved in lactose metabolism and transport across the membrane as seen in the image.

The main idea is that E. coli (the most common medium when investigating the Lac operon) conserves its resources by not making many Lac proteins when other more easily-accepted sugars, such as glucose, are available [3]. This was tested by Jacques Monod during World War II. He tested the combinations of different sugars for E. coli and discovered that when the bacteria are grown with glucose and lactose, glucose would get metabolized first during the bacteria's growth phase I and then lactose during growth phase II.

This means that if glucose and lactose are available for the cell, transcription will occur but at a slow rate. Obviously, if there is no lactose at all, nothing will be transcribed. As long as lactose is available, transcription will happen as the LacI repressor is never binded to the operator. Thus, when these Lac proteins are made with the presence of lactose, the lac gene and its derivatives can be used to trigger a color change within the cell. Once glucose is used up, lactose acts as the power source, and the lac operon can truly act as a reporter gene. As in the case for our group, the lac operon device contained the necessary promoters, ribosome-binding sites, terminators, a LacI repressor, a cyano fluorescent protein, and a vector backbone based on the Type IIS assebmly strategy and would turn a bright cyan color when exposed to lactose. This "switch" function can have a multitude of possibilities, and one of these uses is focused on in the page.

Design: Our genetic circuit




<tab>pSB1A3-1 is a high copy number plasmid. The replication origin is a pUC19-derived pMB1 (copy number of 100-300 per cell). The terminators bracketing pSB1A3 MCS are designed to prevent transcription from inside the MCS from reading out into the vector.

Plasmid Map of "Sweet Cyan"

Building: Assembly Scheme


Testing: Modeling and GFP Imaging

graphical model (Julia)

We used a previously published synthetic switch, developed by Ceroni et al., to understand how our system could potentially be modeled and simulated. The graphic to the left depicts the relationships between the parameters of the system using a network diagram illustration.

In order to approximate the behavior of this set-up, a mathematical model can be developed based upon the relationships between different parameters. 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 Ceroni et al. model and the network diagram illustration use the following table of terms in their representation of the Lac switch.

Lac Switch Model: Important Variables and Parameters<ref>Ceroni, F. et al (2010). Rational Design of Modular Circuits for Gene Transcription: A Test of the Bottom-up Approach. Journal of Biomedical Engineering, 4(14).</ref>
Variable Description Units
I IPTG concentration mM
G GFP protein concentration molecules/cell
LF free LacI molecules molecules/cell
LI LacI molecules bound to IPTG molecules/cell
MG mRNA molecules of GFP molecules/cell
ML mRNA molecules of LacI molecules/cell
DFG/L free Repressor/Reporter plasmids plasmids/cell
DLG/L Repressor/Reporter plasmids bound to LacI molecules plasmids/cell
DIG/L Repressor/Reporter plasmids bound to induced LacI molecules plasmids/cell
D0G number of Reporter plasmids per cell plasmids/cell
D0L number of Repressor plasmids per cell plasmids/cell
λG/L protein degradation rate minutes-1
λMG/L mRNA degradation rate minutes-1
αG GFP rate of synthesis minutes-1
AL LacI rate of synthesis minutes-1
αMG GFP transcription rate minutes-1
αML LacI transcription rate minutes-1
KLx equilibrium binding constant of the LacI-Ox complex molecules/cell
KIx equilibrium binding constant for the binding of induced LacI molecule to the Ox operator sequence molecules/cell
KLI equilibrium binding constant for binding IPTG-LacI mM
τLI time constant of LacI binding to operator sequences minutes
τDI time constant of induced-LacI binding to operator sequences minutes
τDL time constant of LacI-IPTG binding minutes


We 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 (fig. 1).

Figure 1: Original Bgal Concentration vs. Time with I = 0.32

This value was changed again to 0.25 in determining the threshold that produces this "on" state (fig. 2).

Figure 2: Bgal Concentration vs. Time with I = 0.25

After proceeding to go up and down with these a values, a threshold was indeed found where the concentration of IPTG is about 0.064 (fig. 3).

Figure 3: Bgal Concentration vs. Time with I = 0.064

We explored how one technique, imaging via microscopy could be used to determine the production rate of an output protein, in this case GFP in yeast, could be used to determine a "real" value for maximum GFP production rate under our own laboratory conditions.

- show plot of data and discuss outcome. - include some of the pictures of the raw data - wrap up section to explain how the curves could be improved

Ideally, the GFP production rate measured by this method could be entered as a value for [which parameter] in the Ceroni et al. model.

Stakeholder Assessment

Stakeholder Matrix





Our Team

Your Name

  • My name is Emily Byrne, and I am a student majoring in biomedical engineering. I am taking BME 494 because ###. An interesting fact about me is that ###.

Sarah K. Halls

  • My name is Sarah K. Halls, and I am a student majoring in Biomedical Engineering. I am taking BME 494 because I enjoy cell and tissue Engineering work and hope to start my career in this field of study. An interesting fact about me is that I did an internship at Harvard University working on cell patterning.

Sean Hector

  • My name is Edgil Hector (Sean), and I am a student majoring in biomedical engineering. I am taking BME 494 because the subject is relevant to my interests, and the class counts as a required technical elective. An interesting fact about me is that I am the most indecisive human being on the planet.

Julia Smith

  • My name is Julia Smith, and I am a senior majoring in Biomedical Engineering. I am taking BME 494 because I am extremely interested in synthetic biology. An interesting fact about me is that in addition to my nerdy side and love of accademic learning, I train reining horses.

Works Cited

[1] Potts, Michelle. "Microbiology Exam 2." Microbiology Exam 2. N.p., 12 Feb. 2012. Web. 24 Apr. 2013.

[2] "Lac Operon." Wikipedia. Wikimedia Foundation, 22 Apr. 2013. Web. 28 Apr. 2013.

[3] Muller-Hill, Benno (1996). The lac Operon, a Short History of a Genetic Paradigm. Berlin: Walter de Gruyter. pp. 7–10. ISBN 3-11-014830-7.