Team:Wash U/Chinese/Biological Parts

From 2009.igem.org


Parts

Component Description Part/Accession # Base Pairs Plasmid Resistance Well
RBS Ribosomal Binding Site BBa_B0034 12 pSB1A2 Ampicillin plate 1, 2M
Red Light Sensor description BBa_I15010 2,238 pSB2K3 Kanamycin N/A
OmpR (E. coli) description NP_417864.1 720 pSB1T3 Tetracycline N/A
Terminator Stops Transcription BBa_B0015 129 pSB1AK3 Ampicillin
and Kanamycin
plate 1, 23L
OmpR + Terminator description sequence 916 pany-amp Ampicillin synthesized
OmpC promoter description BBa_R0082 108 pSB1A2 Ampicillin plate 1, 16K
puc B/A description sequence 375 pSB1A3 Ampicillin N/A
puc B description YP_353390 156 ? ? N/A
puc A description YP_353391 165 ? ? N/A
OmpC promoter+BA description sequence 539 pany-kana Kanamycin synthesized
Light Response System description BBa_M30109 4,333 ? Ampicillin N/A
TetR repressible description BBa_J13002 74 pSB1A2 Ampicillin plate 1, 13B
Green Flourescent Protein Marker for successful transformation BBa_E0240 976 pSB1A2 Ampicillin plate 1, 12M

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Characterization

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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. 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 phosphorylation of OmpR as a consequence of the light signal reaching Cph8 bound to OmpR is capture 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. Thus, the model assumes that no phosphorylation occurs by this mechanism in the absence of light. The OmpC promoter binding equation was based on the Hill Equation for a Repressor (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 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.


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