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 [1
Natural Lac Operon with Various Parts [2
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" .
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 . 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
OUR GENE SWITCH:
<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.
Building: Assembly Scheme
Testing: Modeling and GFP Imaging
Network Diagram Illustration of the Lac Model (Julia)
A LAC SWITCH MODEL
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 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.
Lac Switch Model: Important Variables and Parameters
|| IPTG concentration
|| GFP protein concentration
|| free LacI molecules
|| LacI molecules bound to IPTG
|| mRNA molecules of GFP
|| mRNA molecules of LacI
|| free Repressor/Reporter plasmids
|| Repressor/Reporter plasmids bound to LacI molecules
|| Repressor/Reporter plasmids bound to induced LacI molecules
|| number of Reporter plasmids per cell
|| number of Repressor plasmids per cell
|| protein degradation rate
|| mRNA degradation rate
|| GFP rate of synthesis
|| LacI rate of synthesis
|| GFP transcription rate
|| LacI transcription rate
|| equilibrium binding constant of the LacI-Ox complex
|| equilibrium binding constant for the binding of induced LacI molecule to the Ox operator sequence
|| equilibrium binding constant for binding IPTG-LacI
|| time constant of LacI binding to operator sequences
|| time constant of induced-LacI binding to operator sequences
|| time constant of LacI-IPTG binding
AN INTERACTIVE MODEL
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
COLLECTING IMPERICAL VALUES TO IMPROVE THE MODEL
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.
SUPPORTS & UNDERSTANDS
DOES NOT SUPPORT & UNDERSTANDS
SUPPORTS & DOES NOT UNDERSTAND
DOES NOT SUPPORT & DOES NOT UNDERSTAND
- My name is Emily Byrne, and I am a student majoring in biomedical engineering. I am taking BME 494 because I am interested in genetic engineering, synthetic biology, and their potential to impact the clinical setting. An interesting fact about me is that I enjoy painting and woodworking.
- 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.
- 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.
- 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 academic learning, I train reining horses.
- Benno Muller-Hill. The Lac Operon. Walter de Gruyter. isbn:3110148307.
- Ceroni F, Furini S, Giordano E, and Cavalcanti S. . pmid:21070658.