Difference between revisions of "Team:Nanjing-China/Model"

 
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  <ul><li><a href="https://2017.igem.org/Team:Nanjing-China/Model">Model</a></ul></li></div>
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  <ul><li><a href="https://2017.igem.org/Team:Nanjing-China/HP/Silver">HP-Silver</a></li></ul></div>
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  <ul><li><a href="https://2017.igem.org/Team:Nanjing-China/HP/Gold_Integrated">HP-Gold</a></li></ul></div>
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  <ul><li><a href="https://2017.igem.org/Team:Nanjing-China/Model">Model</a></li></ul></div>
 
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       <ul>
 
     <li><a href="#ch2o">For CH<sub>2</sub>O</a></li>
 
     <li><a href="#ch2o">For CH<sub>2</sub>O</a></li>
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      <p>Our cell model examines the characteristics  of the FrmR and promotor&nbsp;PfrmAB regulatory pathways. By this  model, we predicted the behaviour of our biosensor under it&rsquo;s designed  conditions. If this is consistent with the experimental observations, we can  confirm that the FrmR and promotor&nbsp;PfrmAB tuning networks  operate in the same way as originally assumed.[1]<br/>
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The main modeling environment used in the project is  the MATLAB SimBiology toolkit, a plug-in designed for biology of computational  systems.The toolbox transforms the basic mathematics of system biology into a  graphical interface, making it an ideal choice for gene conditioning network  modeling. SimBiology will be used with deterministic differential equation  solver methods. In other words, each time the simulation is run, the  calculations are the same and give the same solution. This means that there is  no randomized result. In real life, the number has a certain degree of random  variation, which means a random model is useful. However, on the cell  population, these random variants yielded definitive results on average.</p>
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      <p><a href="https://2017.igem.org/Team:Nanjing-China/Model/ch2o">Click here to see more about the model of CH<sub>2</sub>O</a></p>
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       <p>Our cellular model develops on the  characterization of the hoxBC-hoxJ and hoxA regulation pathway as well as predicting  the behavior of our Hydrogen biosensor under its designed conditions, through  which we can learn more details about the procession of hoxBC-hoxJ and hoxA regulation  pathway[1]. <br />
 
       <p>Our cellular model develops on the  characterization of the hoxBC-hoxJ and hoxA regulation pathway as well as predicting  the behavior of our Hydrogen biosensor under its designed conditions, through  which we can learn more details about the procession of hoxBC-hoxJ and hoxA regulation  pathway[1]. <br />
 
The main modelling environment utilized in  this project is the MATLAB SimBiology toolbox, a plugin designed specifically  for computational systems biology. SimBiology will be utilized with a  deterministic differential equation solver method. However in real life, there  is some degree of random variation in the quantities - this is where a  stochastic model would be useful. However, over a cell population these random  variations average out to give deterministic results.</p>
 
The main modelling environment utilized in  this project is the MATLAB SimBiology toolbox, a plugin designed specifically  for computational systems biology. SimBiology will be utilized with a  deterministic differential equation solver method. However in real life, there  is some degree of random variation in the quantities - this is where a  stochastic model would be useful. However, over a cell population these random  variations average out to give deterministic results.</p>
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<p><a href="https://2017.igem.org/Team:Nanjing-China/Model/h2">Click here to see more about the model of H<sub>2</sub></a></p>
 
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      <div align="center"><img src="https://static.igem.org/mediawiki/2017/c/c7/T-Nanjing-China-project-h2s.png" width="40%" /></div>
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      <p>The cellular model develops on the  characterization of the <em>sqr</em> and <em>sqrR</em> regulation pathway, aiming at  exploring the property of the designed H2S  detector system. <br />
 +
        The main modeling environment utilized  in this project is the MATLAB SimBiology toolbox, a plugin designed  specifically for computational systems biology. SimBiology will be utilized  with a deterministic differential equation solver method. In real life, there  is random variation in quantities to some degree - this is where a stochastic model  would be useful. But these random variations can average out over a cell  population to give deterministic results.</p>
 +
        <p><a href="https://2017.igem.org/Team:Nanjing-China/Model/h2s">Click here to see more about the model of H<sub>2</sub>S</a></p>
 
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Latest revision as of 14:37, 30 October 2017

Team:Nanjing-China - 2017.igem.org

Our cell model examines the characteristics of the FrmR and promotor PfrmAB regulatory pathways. By this model, we predicted the behaviour of our biosensor under it’s designed conditions. If this is consistent with the experimental observations, we can confirm that the FrmR and promotor PfrmAB tuning networks operate in the same way as originally assumed.[1]
The main modeling environment used in the project is the MATLAB SimBiology toolkit, a plug-in designed for biology of computational systems.The toolbox transforms the basic mathematics of system biology into a graphical interface, making it an ideal choice for gene conditioning network modeling. SimBiology will be used with deterministic differential equation solver methods. In other words, each time the simulation is run, the calculations are the same and give the same solution. This means that there is no randomized result. In real life, the number has a certain degree of random variation, which means a random model is useful. However, on the cell population, these random variants yielded definitive results on average.

Click here to see more about the model of CH2O

Our cellular model develops on the characterization of the hoxBC-hoxJ and hoxA regulation pathway as well as predicting the behavior of our Hydrogen biosensor under its designed conditions, through which we can learn more details about the procession of hoxBC-hoxJ and hoxA regulation pathway[1].
The main modelling environment utilized in this project is the MATLAB SimBiology toolbox, a plugin designed specifically for computational systems biology. SimBiology will be utilized with a deterministic differential equation solver method. However in real life, there is some degree of random variation in the quantities - this is where a stochastic model would be useful. However, over a cell population these random variations average out to give deterministic results.

Click here to see more about the model of H2

The cellular model develops on the characterization of the sqr and sqrR regulation pathway, aiming at exploring the property of the designed H2S detector system.
The main modeling environment utilized in this project is the MATLAB SimBiology toolbox, a plugin designed specifically for computational systems biology. SimBiology will be utilized with a deterministic differential equation solver method. In real life, there is random variation in quantities to some degree - this is where a stochastic model would be useful. But these random variations can average out over a cell population to give deterministic results.

Click here to see more about the model of H2S

Address

Life Science Department
#163 Xianlin Blvd, Qixia District
Nanjing University
Nanjing, Jiangsu Province
P.R. of China
Zip: 210046

Email

nanjing_china@163.com

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