Difference between revisions of "Part:BBa K201001:Experience"
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To experimentally and mathematically characterize the device and its sensitive to the inducer, we performed both a static and a dynamic GFP analysis after IPTG induction. | To experimentally and mathematically characterize the device and its sensitive to the inducer, we performed both a static and a dynamic GFP analysis after IPTG induction. | ||
| − | Static analysis. | + | '''Static analysis'''. |
Cells were inoculated in 5 ml of M9 medium with 0, 10, 20, 40, 60, 80, 100 uM IPTG, respectively. After O/N growth at 37° (about 12 h) samples were collected and slides prepared for microscope analysis. Images were then analyzed with the [http://2009.igem.org/Team:Bologna/Software VIFluoR software]. To obtain a significant representation of bacterium fluorescence, it was necessary to acquire several images, each one reporting a sufficient number of bacterial cells. VIFluoR operates the image segmentation and then recognises the bacterial cells yielding the mean fluorescence per bacterium as the output. The experimental data(Fig. 1) were used to identify by the [http://2009.igem.org/Team:Bologna/Modeling mathematical model] the operator binding affinity for the repressor LacI '''(K= 1.7 nM)'''. | Cells were inoculated in 5 ml of M9 medium with 0, 10, 20, 40, 60, 80, 100 uM IPTG, respectively. After O/N growth at 37° (about 12 h) samples were collected and slides prepared for microscope analysis. Images were then analyzed with the [http://2009.igem.org/Team:Bologna/Software VIFluoR software]. To obtain a significant representation of bacterium fluorescence, it was necessary to acquire several images, each one reporting a sufficient number of bacterial cells. VIFluoR operates the image segmentation and then recognises the bacterial cells yielding the mean fluorescence per bacterium as the output. The experimental data(Fig. 1) were used to identify by the [http://2009.igem.org/Team:Bologna/Modeling mathematical model] the operator binding affinity for the repressor LacI '''(K= 1.7 nM)'''. | ||
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{|align="center" | {|align="center" | ||
| − | |[[Image:static_induction_figure.jpg|center|450px|thumb|Fig.1. Experimental data (blue lines) of the static induction after 0, 10, 20, 40, 60, 80, 100 uM IPTG induction of the system. Tha data were fitted by the model (green line) to identify the operator-repressor binding affinity ('''K= 1.7 nM''']] | + | |[[Image:static_induction_figure.jpg|center|450px|thumb|Fig.1. Experimental data (blue lines) of the static induction after 0, 10, 20, 40, 60, 80, 100 uM IPTG induction of the system. Tha data were fitted by the model (green line) to identify the operator-repressor binding affinity ('''K= 1.7 nM''')]] |
After model identification, we computed the LacI repressed GFP generator (LacI inverter) curve (Fig.2). | After model identification, we computed the LacI repressed GFP generator (LacI inverter) curve (Fig.2). | ||
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| − | Dynamic analysis. | + | '''Dynamic analysis.''' |
Dh5alpha cells were inoculated in the morning (9 a.m.) in 5 ml of M9 medium with no IPTG. After daily growth (about 8 h) the culture was diluited to an OD=. 200 ul of the culture were used to fill 6 plate wells (with 0 and 100 uM IPTG, respectively). Samples were grown into a fluorimeter (Tecan M200) O/N (about 12h) at 37°. In the morning fluorescence data were analyzed and used to fit the model (Fig. 3 and 4). | Dh5alpha cells were inoculated in the morning (9 a.m.) in 5 ml of M9 medium with no IPTG. After daily growth (about 8 h) the culture was diluited to an OD=. 200 ul of the culture were used to fill 6 plate wells (with 0 and 100 uM IPTG, respectively). Samples were grown into a fluorimeter (Tecan M200) O/N (about 12h) at 37°. In the morning fluorescence data were analyzed and used to fit the model (Fig. 3 and 4). | ||
Revision as of 21:57, 21 October 2009
Dh5alpha cells were co-transformed with the BBa_K201001 on a high copy number plasmid (pSB1A2) and BBa_K201002 on a low copy number plasmid (pSB3K3). To experimentally and mathematically characterize the device and its sensitive to the inducer, we performed both a static and a dynamic GFP analysis after IPTG induction.
Static analysis.
Cells were inoculated in 5 ml of M9 medium with 0, 10, 20, 40, 60, 80, 100 uM IPTG, respectively. After O/N growth at 37° (about 12 h) samples were collected and slides prepared for microscope analysis. Images were then analyzed with the [http://2009.igem.org/Team:Bologna/Software VIFluoR software]. To obtain a significant representation of bacterium fluorescence, it was necessary to acquire several images, each one reporting a sufficient number of bacterial cells. VIFluoR operates the image segmentation and then recognises the bacterial cells yielding the mean fluorescence per bacterium as the output. The experimental data(Fig. 1) were used to identify by the [http://2009.igem.org/Team:Bologna/Modeling mathematical model] the operator binding affinity for the repressor LacI (K= 1.7 nM).
After model identification, we computed the LacI repressed GFP generator (LacI inverter) curve (Fig.2).
Dynamic analysis. Dh5alpha cells were inoculated in the morning (9 a.m.) in 5 ml of M9 medium with no IPTG. After daily growth (about 8 h) the culture was diluited to an OD=. 200 ul of the culture were used to fill 6 plate wells (with 0 and 100 uM IPTG, respectively). Samples were grown into a fluorimeter (Tecan M200) O/N (about 12h) at 37°. In the morning fluorescence data were analyzed and used to fit the model (Fig. 3 and 4).
UNIQ50f41cb65ca758a8-partinfo-00000000-QINU UNIQ50f41cb65ca758a8-partinfo-00000001-QINU |
