Team:Wash U/Biological Parts

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(Completed Characterization)
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The optical density of the cultures was recorded at 600nm, and each absorbance spectrum was normalized to OD600 by division by this value.  Background subtraction of spectrophotometer data was performed in Origin 6.1 Software.  A ten-point baseline was created by a "positive peak" algorithm then modified to approximate the scattering curve that falls as the inverse fourth power of wavelength.  <br>
The optical density of the cultures was recorded at 600nm, and each absorbance spectrum was normalized to OD600 by division by this value.  Background subtraction of spectrophotometer data was performed in Origin 6.1 Software.  A ten-point baseline was created by a "positive peak" algorithm then modified to approximate the scattering curve that falls as the inverse fourth power of wavelength.  <br>
'''Results'''<br>
'''Results'''<br>
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[[Image:02 puc pro characterization graph.png]]
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[[Image:02 puc pro characterization graph.png|700px|]]
== '''Future Characterization''' ==
== '''Future Characterization''' ==

Revision as of 22:46, 18 October 2009


The Registry of Standard Biological Parts is a library of DNA sequences combined with online characterization resources. The Registry has created a standard protocol for making segments of DNA compatible with all other segments of DNA, regardless of order or size. BioBrick is the term given to such segments of DNA, a term alluding to the fact that any number of bricks may be combined in any order to produce complex, unique systems. This is accomplished by standardizing the restriction enzymes used to surround BioBricks, as well as the plasmids used to transform them. For a graphical representation of of the process, please click [http://ginkgobioworks.com/support/BioBrick_Assembly_Manual.jpg here]. A powerful online database provides information and characterization of all of the BioBricks in the Registry and uses the wiki format (the same one used in wikipedia) which encourages others to edit content directly on the page. Below are list of parts that were used/created for this project.

Parts

Parts used to characterize and build our final project

Component Part/Accession # Base Pairs Plasmid Resistance Well
RBS-34 [http://partsregistry.org/Part:BBa_B0034 BBa_B0034] 12 pSB1A2 Ampicillin plate 1, 2M
Cph8 [http://partsregistry.org/Part:BBa_I15010 BBa_I15010] 2,238 pSB2K3 Kanamycin N/A
RFP [http://partsregistry.org/Part:BBa_J04051 BBa_J04051] 720 N/A N/A N/A
OmpR (E. coli) [http://partsregistry.org/wiki/index.php?title=Part:BBa_K098011 BBa_K098011] 720 pSB1T3 Tetracycline N/A
OmpR (R. sphaeroides) [http://partsregistry.org/Part:BBa_K227010 BBa_K227010] 720 N/A N/A N/A
Terminator [http://partsregistry.org/Part:BBa_B0015 BBa_B0015] 129 pSB1AK3 Ampicillin
and Kanamycin
plate 1, 23L
RBS +OmpR(sph) + Terminator

includes prefix and suffix

sequence 916 pany-amp Ampicillin synthesized
OmpC promoter [http://partsregistry.org/Part:BBa_R0082 BBa_R0082] 108 pSB1A2 Ampicillin plate 1, 16K
puc promoter [http://partsregistry.org/Part:BBa_K227007 BBa_K227007] 651 pSB1k3 Kanamycin N/A
puc BA [http://partsregistry.org/Part:BBa_K227006 BBa_K227006] 375 pSB1k3 Kanamycin N/A
puc B [http://partsregistry.org/Part:BBa_K227005 BBa_K227005] 156 pSB1k3 Kanamycin N/A
puc A [http://partsregistry.org/Part:BBa_K227004 BBa_K227004] 165 pSB1k3 Kanamycin N/A
OmpC promoter+BA sequence 539 pany-kana Kanamycin synthesized
TetR repressible [http://partsregistry.org/Part:BBa_J13002 BBa_J13002] 74 pSB1A2 Ampicillin plate 1, 13B
Green Fluorescent Protein [http://partsregistry.org/Part:BBa_E0240 BBa_E0240] 876 pSB1A2 Ampicillin plate 1, 12M


Plasmids used to create and characterize our project

Plasmid Base Pairs Resistance Copy Number
[http://partsregistry.org/Part:pSB1A2 pSB1A2] 2,079 Ampicillin high
[http://partsregistry.org/Part:pSB1K3 pSB1K3] 2,206 Kanamycin high
[http://partsregistry.org/Part:pSB1A3 pSB1A3] 2,157 Ampicillin high
[http://partsregistry.org/Part:pSB2K3 pSB2K3] 4,425 Kanamycin variable
[http://partsregistry.org/Part:pSB1T3 pSB1T3] 2,463 Tetracycline high
[http://partsregistry.org/Part:pSB1AK3 pSB1AK3] 3,189 Ampicillin and
Kanamycin
high
pANY
pRK404
pRKPLHT7 Tetracycline
pRKCBC3 Tetracycline


Parts submitted to the Registry of Standard Biological Parts

Part/Accession #Component Component Type Base Pairs Plasmid Resistance
[http://partsregistry.org/Part:BBa_I15010 BBa_I15010] Cph8 (resubmission) Coding 2,238 plasmid Resistance
[http://partsregistry.org/Part:BBa_K227004 BBa_K227004] puc A Coding 165 pSB1K3 Kanamycin
[http://partsregistry.org/Part:BBa_K227005 BBa_K227005] puc B Coding 156 pSB1K3 Kanamycin
[http://partsregistry.org/Part:BBa_K227006 BBa_K227006] puc BA Coding 376 pSB1K3 Kanamycin
[http://partsregistry.org/Part:BBa_K227007 BBa_K227007] puc promoter Regulatory 651 pSB1K3 Kanamycin
[http://partsregistry.org/Part:BBa_K227008 BBa_K227008] ompC+PucBA (synthesized) Composite 539 pSB1AT3 Ampicilin,Tetracycline
[http://partsregistry.org/Part:BBa_K227009 BBa_K227009] PucPromotor+GFP Composite 1377 pSB1A2 Ampicilin
[http://partsregistry.org/Part:BBa_K227010 BBa_K227010] OmpR (sphaeroides) Coding- not available n/a n/a n/a
[http://partsregistry.org/Part:BBa_K227011 BBa_K227011] RBS34+OmpR+Term (synthesized) Composite 916? pSB1K3? Kanamycin?
[http://partsregistry.org/Part:BBa_K227012 BBa_K227012] RBS34+OmpR(sph)+Term+OmpC+PucB/A Composite ??? pSB1K3 Kanamycin
[http://partsregistry.org/Part:BBa_K227013 BBa_K227013] ompC + GFP Composite 992 plasmid Resistance
[http://partsregistry.org/Part:BBa_K227014 BBa_K227014] pucpro+pucBA Composite ??? pSB1K3 Kanamycin
[http://partsregistry.org/Part:BBa_K227015 BBa_K227015] RBS34+OmpR(sph)+Term+OmpC+GFP Composite ??? plasmid Ampicilin

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Completed Characterization

Slide2.jpg
The first part of our characterization begins with the puc promoter, which promotes transcription of the LH2 pucB/A genes naturally in Rhodobacter sphaeroides. It is important that we are able to compare the transcription rate of the puc promoter in the natural system vs. our mutant system so that we can determine exactly how much efficiency is gained by adding a red light sensor. The absorption spectra of a DBCOmega mutant (LH2 deficient) transformed with pRKCBC3 containing the puc promoter and pucB/A genes will allow us to characterize the puc promoter under high and low oxygen conditions.

More absorption of light at the LH2 spectra peaks normalized to culture OD corresponds with more transcription and vis versa.
Method
The optical density of the cultures was recorded at 600nm, and each absorbance spectrum was normalized to OD600 by division by this value. Background subtraction of spectrophotometer data was performed in Origin 6.1 Software. A ten-point baseline was created by a "positive peak" algorithm then modified to approximate the scattering curve that falls as the inverse fourth power of wavelength.
Results
02 puc pro characterization graph.png

Future Characterization












Slide3.jpg
The next step in our characterization of our synthetic red light response system is to analyze changes in phosphorylation of ompR in Rhodobacter sphaeroides. In our final system, we only want puc genes to be transcribed and expressed via halting the autophosphorylation of ompR, not simply the puc promoter as it naturally occurs. By placing the ompR coding region downstream of the red light sensor and upstream of a terminator our modified system controls expression of the puc genes by the red light sensor. It should be impossible for the puc promoter to directly cause the transcription of puc genes due to the terminator, but instead, transcription of the puc genes must be activated via decreasing the presence of phosphorylated ompR. The end target of ompR transcription is the ompC promoter, located directly upstream of the puc genes. Placing GFP on the ompC transcript will show how often the promoter is transcribed and how often ompR is phoshorylated.








Slide4.jpg
Part three of our characterization measures the effectiveness of the red light sensor in downregulating the phosphorylation of ompR. This setup is identical to that of part two except we have introduced the red light sensor. Now, the rate of ompR autophosphorylation will be halted by binding to a domain on the light-activated EnvZ kinase analogue. GFP is still attached to the end product, the ompC promoter. By comparing the fluorescence of GFP in this scenario compared with the second scenario the decrease in rate of phosphorylation should be apparent due to the activity of the red light sensor.












Slide5.jpg
This is the final construct that will be our actual functioning model in Rhodobacter sphaeroides. This finished product will be compared to the wild type over various intensities of light and cell culture densities can be compared to see which strain, the wild type or the modified strain with the red light sensor was more efficient in harvesting light with varying intensities.















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Modeling


pucBA Expression Model Diagram
pucBA Model Equations
pucBA Model Reactions
Test Simulation Output

Modeling the Gene Regulatory Network

Our group seeks to assess the optimality of the synthetic system that modulates pucB/A gene expression and LH2 complex assembly in Rhodobacter sphaeroides. Here we employ a mathematical model of this system to generate predictions about the behavior of the active system in response to light input. Features of the system that the model may help investigate include the time scale of response to light signals, the robustness of the system in response to fluctuations in light intensity, and the translation between changes in gene expression and the absorbance spectrum of the engineered cells.




Though the context of the model can extend back to the transcription of PrrA/B genes involved in integration oxygen and light signals, a preliminary testing model was developed using assumptions of certain initial conditions to isolate the light signal's effect. Since Cph8 and OmpR are located on the same transcript downstream of the puc promoter region, it was assumed that their associated protein and mRNA had already reached steady state concentrations, and the phosphorylation reaction had already reached steady state. Moreover, the concentrations of the factors were assumed to be equal at this state. The model whose diagram was constructed in the Simbiology Toolbox distributed by MathWorks details key reactions leading to the translation of the pucB/A genes. The reaction rate equation used for the lack of phosphorylation of OmpR when the light signal reaches Cph8 bound to OmpR is captured in a modified form of Michaelis-Menten kinetics. A logic function that corresponds to light ON/OFF (1/0) multiplies the maximum reaction rate in the numerator of the phosphorylation equation. Thus, the model assumes that no phosphorylation occurs by this mechanism in the presence of light. The OmpC promoter binding equation was based on the Hill Equation for an Activator(1).




Component characterization steps and literature searches are underway in order to obtain quantitative parameters for the reaction rates. In order to simulate behavior of the system, putative values were included that exaggerate true concentrations and time scales. OmpR was given an initial concentration normalized to one, and all other components were assumed insignificant initially to this value. An ideal light pulse was introduced at an instant and removed thirty simulation seconds later. From this rudimentary simulation it can be drawn that the nonlinearities of the phosphorylation and transcription factor binding kinetics effectively smooth the sharp light input. By design, the light switch ON yields no phosphorylation of OmpR and repression of the pucB/A genes which would give rise to LH2. Conversely, when left OFF, the concentration of pucB/A recovers and increases until the steady state determined by its translation and degradation rates.




References

1. Alon, Uri. Introduction to systems biology and the design principles of biological networks. Boca Raton, FL: Chapman & Hall, 2006.
2. Bower, James M. Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology). New York: M.I.T. PRESS, 2001.
3. System modeling in cellular biology from concepts to nuts and bolts. Cambridge, MA: MIT P, 2006.


Simulating a Bioreactor



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