BME494s2013 Project Team1

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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.[1] 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 model would act as a proof-of-concept in the development of the device; the BioBricks available in the Parts Registry facilitated easy construction and testing of the model but do not contain the luminescent components the group would use in the distributed device. The cyan fluorescent protein (CFP), which is used in the proof-of-concept model, would necessitate the use of ultra-violet light to observe the fluorescent output. This would not be implementable in a model geared toward commercialization. Instead, the final model would use luciferase (such as BBa_I712019 or BBa_J52008), which would not require the use of external stimulants to operate. 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. Background 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" [3]. 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 [4]. 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, transcription will be repressed. 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 and the corresponding genes act as a switch, this mechanism can be seen as an engineering application, and in this case, used to trigger a color change within the cell. As in the case for our group, the lac operon device contained the necessary promoters, ribosome-binding sites, terminators, a LacI repressor, a cyan 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. Basic Components of a Lac Operon [3] Design: Our genetic circuit Our gene switch To 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 Parts The 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). Device Design for "Sweet Cyan" IPTG-Inducible Lac Promoter Part BBa_K418003 This BioBrick contains the genetic coding region for the LacI repressor protein along with a promoter adapted from the natural Lac Operon. When LacI binds to the pLac regulator and PLlac01 hybrid regulator, it inhibits transcription. When IPTG binds to LacI, it inhibits the LacI operation and therefore promotes transcription. The components of this BioBrick are: A constitutive promoter: causes transcription A ribosome binding site: place for ribosomes to attach during transcription. Gene coding for LacI repressor protein: gene that is transcribed to create the LacI repressor. Terminators: end the transcription process. LacI regulated promoter: initiates the next part of the transcription process. Note: This promoter is negatively regulated by the LacI protein and therefore, transcription will only take place when the LacI protein is NOT present. Cyan Fluorescent Protein Part BBa_K411225 This BioBrick contains the genes for the expression of the enhanced cyan fluorescent protein (derived from A. victoria GFP) and is regulated by the IPTG-Inducible Lac Promoter (BBa_K418003). The components of the BioBrick are: A ribosome binding site: place for ribosomes to attach during gene transcription. Gene coding for Cyan Fluorescent Protein: gene that is transcribed to create cyan fluorescent protein. Terminators: end the transcription process. Vector Backbone Part pSB1A3-1 This part was chosen as the vector backbone of our device. 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. How the Switch Works Our Sweet Cyan switch works based on the relationship between the LacI protein, IPTG, and CFP. For the transcription of CFP to take place, the LacI regulated promoter (see PCFP in circuit diagram below) must be active. Because the LacI protein inhibits the CFP promoter, CFP will not be produced or is "turned off" in the presence of the LacI protein. Circuit Diagram for Sweet Cyan Switch PLac represents the LacI promoter and PCFP represents the CFP promoter The switch is designed so that under normal conditions LacI is produced, the LacI regulated promoter (PCFP) is consequently inhibited, and CFP production is inhibited. However, when IPTG is added to the system, it binds to the LacI protein that is being created and subsequently prevents the LacI from inhibiting PCFP, once PCFP is no longer inhibited, the system produces CFP. When the CFP promoter is active, the system will produce a fluorescent cyan color. A Plasmid map of the system is included for reference below. Plasmid Map of "Sweet Cyan" Building: Assembly scheme Mutagenesis process[5] The 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. Mutagenesis One 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] PCR Reaction Table Reagent [Brick 1] [Brick 2] [Vector] Template DNA 0.2 μL 0.2 μL 0.2 μL 10 μM Forward Primer 1.0 μL 1.0 μL 1.0 μL 10 μM Reverse Primer 1.0 μL 1.0 μL 1.0 μL 2x GoTaq Green Mix 25 μL 25 μL 25 μL H2O 22.8 μL 22.8 μL 22.8 μL Total Volume 50 μL 50 μL 50 μL PCR PCR, 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 90°C, 3 minutes [95°C, 15 seconds; 55°C, 15 seconds; 72°C, 30 seconds] × 30 72°C, 3 minutes 4°C, ∞ Digestion/Ligation Reaction Table Reagent Assembly Negative Control PCR fragment 1 (vector) 1.0 μL 1.0 μL PCR fragment 2 1.0 μL -- PCR fragment 3 1.0 μL -- 10x T4 ligase buffer 1.0 μL 1.0 μL 10x T4 ligase 0.25 μL 0.25 μL BsmBI 0.5 μL 0.5 μL H2O 5.25 μL 7.25 μL Total Volume 10 μL 10 μL Digestion and ligation reaction The 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 [45°C, 2 minutes; 16°C, 5 minutes] × 25 60°C, 10 minutes 80°C, 20 minutes 4°C, ∞ Parts and primers The 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. BioBrick parts used in the device assembly Name ID Size IPTG inducible lac promoter cassette [Brick 1] BBa_K418003 1416 bp RBS + CFPlva + term. X2 [Brick 2] BBa_K411225 917 bp BioBrick plasmid backbone [Vector] pSB1A3 2155 bp The following table lists the primers used in the mutagenesis and PCR procedures of device assembly. Primers used in the device assembly Part/Purpose Forward (5' to 3') Reverse (5' to 3') [Brick 1] CACACCACGTCTCATAGATTGACAGCTAGCTCA CACACCACGTCTCATGTGCTCAGTATCTT [Brick 2] CACACCACGTCTCAAAAGAGGAGAAATAC CACACCACGTCTCATAGTTATAAACGCAGAAAG [Vector] CACACCACGTCTCAACTAGTAGCGGCCGC CACACCACGTCTCATCTAGATGCGGCCGC [Brick 1] Mutagenesis ATCAGCTGTTGCCCGCCTCACTGGTGAAAAG TCTTTTCACCAGTGAGGCGGGCAACAGCTGA Luciferase: An Alternative to CFP Since CFP will only fluoresce when exposed to ultraviolet light, a possible alternative to CFP that is bioluminescent is Luciferase. Luciferase would likely be a more feasable solution to a biodegradable light source than CFP because it does not require exposure to an additional light source before it will emit light. There are BioBrick parts, such as BBa_I712019 or BBa_J52008 from Parts Registry which are possible candidates for creating a bio-luminescent device. Testing: Modeling and GFP imaging A lac switch model We used a previously published synthetic switch, developed by Ceroni et al.[6], to understand how our system could potentially be modeled and simulated. Network Diagram of the Relationships between Parameters in the Ceroni System The graphic to the right 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[6] 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 Figure 1: Original Bgal Concentration vs. Time with I = 0.32 Figure 2: Bgal Concentration vs. Time with I = 0.045 Figure 3: Bgal Concentration vs. Time with I = 0.064 Figure 4: Bgal Concentration vs. Time with all three 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 (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). The initial concentration of IPTG has a direct influence on the rate of βgal production and thus has a point where this rate is in equilibrium to the rate of βgal degradation which increases with the production of more βgal. Because the rate at which β-galactosidase is being produced must interact with the degradation rate of β-galactosidase, the production starts to cap off at around time = 100 with an approaching βgal concentration of about 4.52 x 10-4 where slope is 0, reaching saturation as seen in the original model. As the initial concentration of IPTG goes down (say 0.045), β-galactoside production rate isn't in equilibrium with the degradation rate till quite some time after an initial spike due to the always available mRNA seen as the IPTG input decreases, and so β-galactoside production is never 0. Even at this point however, the output of IPTG is still not quite sustained although a pattern is evident. As soon as the concentration gets to around 0.064, visible equilibrium is seen between production and degradation at a time of around 10 and a concentration of about 2.56 x 10-5 and thus a threshold is found with this sustainable βgal output where the equilibrium concentration is equal to the concentration caused by the initial mRNA. All four initial concentrations can be compared in figure 4. This model assumes that IPTG stays constant over time although realistically IPTG would of course fluctuate or run out as time would go on. Some more terms/variables are taken into account as well: Terms of Interest with Matlab Model Variable Description μ Dilution of input IPTG or [IPTG]/cell volume, with IPTG diffusion γβ Degradation of βgal protein (the β-galactosidase) concentration that is being produced αM Production rate that M (the mRNA) is being transcribed αβ Production rate that βgal (the β-galactosidase) is being translated cell (= 1) Bacterial cell volume of 1 Collecting imperical values to improve the model We used a microscopy imaging technique to determine the production rate of GFP yeast. Based on our collected data, we were able to determine a "real" value for the maximum GFP production rate for the given conditions. 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. Raw Data of Yeast Cells GFP Production Images shown were taken at 7, 10, 16, and 20 hours respectively. MATLAB Modeling 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. GFP Production Over Time with 4th Degree Polynomial "Best Fit" By creating a "best fit" model of the data, it is possible to solve for the parameter values of the system and therefore determine the maximum rate of GFP production. The 4th order "best fit" polynomial equation was found to be: y=0.00041x4-0.029x3+0.67x2-3.1x+2 To find the maximum rate of GFP production, the derivative of the 4th order polynomial (above) was plotted in MATLAB and the maximum y value was found. The plot of the 3rd order derivative polynomial can be seen below. Plot of the Derivative of the "Best Fit" Polynomial The maximum rate of GFP production corresponds to the maximum y value of the derivative plot which was found to be 3.299. Ideally, the GFP production rate measured by this method could be entered as a value for aMG (GFP transcription rate) in the Ceroni et al. model. To better improve the accuracy of the curves (and therefor the GFP production rate), a significantly larger number of cells should be tracked, their data averaged, and all of the plots recreated using the new data. Human Practices Stakeholder Assessment Stakeholder Matrix SUPPORTS & UNDERSTANDS When consumers/investors actually understand how a product works and the logic or science that is behind it, they fall into this supports & and understands category. Perhaps this group understands that negative implications may follow the use of E. coli, but these people also know that the risk is minimal enough for commercial use. As E. coli is naturally in one's intestines, the risks of this bacterium may seem negligible. There are also those that may find the use of this product outweighs the necessary risks that come with it. Usually scientists, engineers, doctors, or maybe those just knowledgeable of common bacteria in this case are going to fall into this group. Those that do might see the benefits of using such a green device (or in this case blue) for such common practices such as camping, scouting, or spelunking. This group would certainly be the most perceptive of the different groups. DOES NOT SUPPORT & UNDERSTANDS A group that understands the technology behind the device but would not support its construction and use could be competition, stakeholders in current technology that meet the customer needs that this new device would try and cater to. Also, scientists, engineers, and healthcare personnel who know the implications of using E. coli in a biodegradable device may oppose its commercialization, as mistakes made in production or consumer handling of the product could prove harmful to human and environmental health. 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. SUPPORTS & DOES NOT UNDERSTAND This group does not understand the technology behind a device, however they still support the construction and use of the technology. Stakeholders that fall into this group are likely to benefit from the success of the proposed technology therefore they support it even though they are not well versed in how the technology works. Because of their lack of knowledge, this group may not fully understand why the device is safe to use despite the potential risks of using a biodegradable device that is powered by E. coli bacteria or they are too focused on the potential success of the device to care about the risks. Additionally, this group sees the benefits of the device such as an emergency, saftey, or warning light without needing to understand the technology that is driving the device. DOES NOT SUPPORT & DOES NOT UNDERSTAND This is a group of potential consumers that choose not to support the product and does not understand the technology behind it. In general, when the common person hears about E. coli, they assume that it is the strain that causes great harm to the human species. Because of this, those who do not fully understand that many strains of E. coli that are harmless to humans exist. By using a biodegradable device that is powered with E. coli, it runs a risk that the average consumer would shy away from this type of light source because of the generalized fear of the bacteria. Therefore, those who do not fully understand that there are completely harmless types of E. coli and that they can be quite useful in our everyday life would not support such a product. This product would lack their support simply because of lack of knowledge and a general public opinion towards E. coli. Our Team Emily Byrne 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. 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 academic learning, I train reining horses. Works Cited http://en.wikipedia.org/wiki/Escherichia_coli http://www.studyblue.com/notes/note/n/microbiology-exam-2/deck/2183983 http://en.wikipedia.org/wiki/Lac_operon ISBN:3110148307 http://tools.invitrogen.com/content/sfs/manuals/geneart_site_directed_mutagenesis_man.pdf Ceroni F, Furini S, Giordano E, and Cavalcanti S. Rational design of modular circuits for gene transcription: A test of the bottom-up approach. J Biol Eng. 2010 Nov 11;4:14. DOI:10.1186/1754-1611-4-14 |