Difference between revisions of "Team:CGU Taiwan/Modeling"
| Line 51: | Line 51: | ||
</div> | </div> | ||
<div id="accordion-container"> | <div id="accordion-container"> | ||
| − | + | This mathematical model helped us to understand the dynamics of our modified Saccharomyces cerevisiae (Yeast). Our model is based on the yeast MAPK pathway, and we hope it can help us to analyze the problems that may occur in the lab. For instance, how long it will take from IL-8/CXCR 1 binding to reaching the highest concentration of the output signal — GFP .<br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/7/7b/CGU-figure-M1.jpg"><br> | |
| − | + | Figure 1 : overview of our engineered yeast pathway<br> | |
| − | + | <br> | |
| − | + | We use a set of ordinary differential equations (ODEs) to describe changes in the concentration of the biochemical substances. In a system of m biochemical species with the concentration ci (i = 1, ..,m) and r biochemical reactions with the rates vj (j = 1, .., r), one may write:<br> | |
| − | + | <br> | |
| − | + | dc1/dt = f1(c1, c2, .., cm) = n11v1 + n12v2 +…+n1r vr<br> | |
| − | + | <br> | |
| − | + | dc2/dt= f2(c1, c2, .., cm) = n21v1 + n22v2 +….+n2r vr<br> | |
| − | + | <br> | |
| − | + | dcm / dt = fm(c1, c2, .., cm) = nm1v1 + nm2v2 +….+nmr vr<br> | |
| − | + | <br> | |
| − | + | Our model is based on a mathematical model of the pheromone pathway and it is as follows:<br> | |
| − | + | <br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/7/75/CGU-figure-M2.jpg"><br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/7/7f/CGU-figure-M3.jpg"><br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/9/98/CGU-figure-M4.jpg"><br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/5/5b/CGU-figure-M5.jpg"><br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/6/69/CGU-figure-M6.jpg"><br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/8/82/CGU-figure-M7.jpg"><br><br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/7/7d/CGU-figure-M8.jpg"><br> | |
| − | + | Figure 2 . the results show the duration for producing GFP and its estimated concentration | |
| − | + | <br> | |
| − | + | The following conclusions can be made from our analysis of the model:<br> | |
| − | + | <br> | |
| − | + | The simulation of the yeast pathway is not accurate. Modelling is simply simulating the kinetic equations mathematically, yet it still provides reference for us to do the wet lab in the future.<br> | |
| − | + | <br> | |
| − | + | <br> | |
| − | + | References:<br> | |
| − | + | ||
| − | + | ||
| − | + | Bennett Kofahl, Edda Klipp, “Modelling the dynamics of the yeast pheromone pathway”, Yeast, 831-850, 2004<br> | |
| − | + | R. William Hipkin, Gregory Deno, Jay Fine, Yongliang Sun, Brian Wilburn, Xuedong Fan, Waldemar Gonsiorek, and Maria T. Wiekowski, “Cloning and Pharmacological.<br> | |
| − | + | Characterization of CXCR1 andCXCR2 from Macaca fascicularis”,THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS,291-300,2004<br> | |
| − | + | </div> | |
| − | + | ||
| − | + | ||
| − | + | <div class="single-blog blog-details two-column" id="Protocols"> | |
| − | + | <br> | |
| − | + | <h2>Instrument</h2> | |
| − | + | </div> | |
| − | + | <div id="accordion-container"> | |
| − | + | ||
| − | + | Since the engineered yeast will produce GFP based on a vary concentration of IL-8 and our goal is screening with the concept of point-of-care, we decided to design a handheld instrument that can detect the concentration of GFP and then automatically define if the user needs any further diagnostic test.<br> | |
| − | + | The following graph shows the concept of our instrument:<br> | |
| − | + | <img src="https://static.igem.org/mediawiki/2015/c/ca/CGU-figure-M9.jpg"><br> | |
| − | + | Blue light emitting diode (LED) is the light source, and after going through the filter and dispersing, the light reaches the sample. The GFP excited in the sample then goes under the same process. Later, green fluorescence goes to the photomultiplier (PMT) for detection. Finally, the result will be displayed.<br> | |
| − | + | <br> | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| + | The design of our detector is as follows:<br> | ||
| + | <img src="https://static.igem.org/mediawiki/2015/5/59/CGU-figure-M10.jpg"><br> | ||
| + | Our instrument is low-priced and user-friendly, which makes it feasible to be popularized among the general public<br> | ||
| + | <br> | ||
| + | References:<br> | ||
| + | Yordan Kostov, Cornelia Renee Albano and Govind Rao, ”All solid-state GFP sensor”<br> | ||
| + | Biotechnology and Bioengineering | ||
| + | |||
| + | |||
| + | |||
| + | |||
| + | |||
Revision as of 03:13, 21 November 2015
Modeling
This mathematical model helped us to understand the dynamics of our modified Saccharomyces cerevisiae (Yeast). Our model is based on the yeast MAPK pathway, and we hope it can help us to analyze the problems that may occur in the lab. For instance, how long it will take from IL-8/CXCR 1 binding to reaching the highest concentration of the output signal — GFP .
Figure 1 : overview of our engineered yeast pathway
We use a set of ordinary differential equations (ODEs) to describe changes in the concentration of the biochemical substances. In a system of m biochemical species with the concentration ci (i = 1, ..,m) and r biochemical reactions with the rates vj (j = 1, .., r), one may write:
dc1/dt = f1(c1, c2, .., cm) = n11v1 + n12v2 +…+n1r vr
dc2/dt= f2(c1, c2, .., cm) = n21v1 + n22v2 +….+n2r vr
dcm / dt = fm(c1, c2, .., cm) = nm1v1 + nm2v2 +….+nmr vr
Our model is based on a mathematical model of the pheromone pathway and it is as follows:
Figure 2 . the results show the duration for producing GFP and its estimated concentration
The following conclusions can be made from our analysis of the model:
The simulation of the yeast pathway is not accurate. Modelling is simply simulating the kinetic equations mathematically, yet it still provides reference for us to do the wet lab in the future.
References:
Bennett Kofahl, Edda Klipp, “Modelling the dynamics of the yeast pheromone pathway”, Yeast, 831-850, 2004
R. William Hipkin, Gregory Deno, Jay Fine, Yongliang Sun, Brian Wilburn, Xuedong Fan, Waldemar Gonsiorek, and Maria T. Wiekowski, “Cloning and Pharmacological.
Characterization of CXCR1 andCXCR2 from Macaca fascicularis”,THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS,291-300,2004
Figure 1 : overview of our engineered yeast pathway
We use a set of ordinary differential equations (ODEs) to describe changes in the concentration of the biochemical substances. In a system of m biochemical species with the concentration ci (i = 1, ..,m) and r biochemical reactions with the rates vj (j = 1, .., r), one may write:
dc1/dt = f1(c1, c2, .., cm) = n11v1 + n12v2 +…+n1r vr
dc2/dt= f2(c1, c2, .., cm) = n21v1 + n22v2 +….+n2r vr
dcm / dt = fm(c1, c2, .., cm) = nm1v1 + nm2v2 +….+nmr vr
Our model is based on a mathematical model of the pheromone pathway and it is as follows:
Figure 2 . the results show the duration for producing GFP and its estimated concentration
The following conclusions can be made from our analysis of the model:
The simulation of the yeast pathway is not accurate. Modelling is simply simulating the kinetic equations mathematically, yet it still provides reference for us to do the wet lab in the future.
References:
Bennett Kofahl, Edda Klipp, “Modelling the dynamics of the yeast pheromone pathway”, Yeast, 831-850, 2004
R. William Hipkin, Gregory Deno, Jay Fine, Yongliang Sun, Brian Wilburn, Xuedong Fan, Waldemar Gonsiorek, and Maria T. Wiekowski, “Cloning and Pharmacological.
Characterization of CXCR1 andCXCR2 from Macaca fascicularis”,THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS,291-300,2004
Instrument
Since the engineered yeast will produce GFP based on a vary concentration of IL-8 and our goal is screening with the concept of point-of-care, we decided to design a handheld instrument that can detect the concentration of GFP and then automatically define if the user needs any further diagnostic test.
The following graph shows the concept of our instrument:
Blue light emitting diode (LED) is the light source, and after going through the filter and dispersing, the light reaches the sample. The GFP excited in the sample then goes under the same process. Later, green fluorescence goes to the photomultiplier (PMT) for detection. Finally, the result will be displayed.
The design of our detector is as follows:
Our instrument is low-priced and user-friendly, which makes it feasible to be popularized among the general public
References:
Yordan Kostov, Cornelia Renee Albano and Govind Rao, ”All solid-state GFP sensor”
Biotechnology and Bioengineering
The following graph shows the concept of our instrument:
Blue light emitting diode (LED) is the light source, and after going through the filter and dispersing, the light reaches the sample. The GFP excited in the sample then goes under the same process. Later, green fluorescence goes to the photomultiplier (PMT) for detection. Finally, the result will be displayed.
The design of our detector is as follows:
Our instrument is low-priced and user-friendly, which makes it feasible to be popularized among the general public
References:
Yordan Kostov, Cornelia Renee Albano and Govind Rao, ”All solid-state GFP sensor”
Biotechnology and Bioengineering