Team:Peking/Modeling/Analysis

From 2010.igem.org

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(Mechanisms of Minimal IOA Networks and Key Parameters Analysis)
(Mechanisms of Minimal IOA Networks and Key Parameters Analysis)
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[[Image:Nfuction1.jpg]]<br>
[[Image:Nfuction1.jpg]]<br>
Solve the equations:<br>
Solve the equations:<br>
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<math>
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And the concentration of B node is actually part of the slope coefficient the linear equation, we can imagine that the function of B node is to lower the concentration of A straightly without the interference of other factors as well as control the concentration of A more precisely and more freely to make the parameter restriction easier to achieve and at the same time the output range is not too small. As we know, the stochastic error may make vague the linear relationship when the values of y axis are too near. The lower concentration X2 is, the steeper the line is, and so the bigger the range is, which in biology means that the bioreporter is more sensitive to certain environmental signal. Through modifying the parameters of node B, we can get a proper concentration of A node to achieve a good r. In all, the node B is a proportion node.<br>
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% feaaguart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn
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% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr
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% 4rNCHbWexLMBbXgBd9gzLbvyNv2CaeHbl7mZLdGeaGqiVu0Je9sqqr
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% 0-yqaqpepae9pg0FirpepeKkFr0xfr-xfr-xb9adbaqaaeGaciGaai
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\[{X_2} = \frac{{{\beta _{\rm{0}}}}}{{{\alpha _2}}}\]
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</math>
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Revision as of 12:52, 25 October 2010

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   Analyses and Results

Mechanisms of Minimal IOA Networks and Key Parameters Analysis

Aimed at answering the question why the two topologies defined above (Figure 8) is functional in IOA, we unravel their mechanisms using the ODE equations in this part, also getting the parameter restrictions of each topology.
NCL Topology
When the network has built steady state:
Nfuction1.jpg
Solve the equations:
And the concentration of B node is actually part of the slope coefficient the linear equation, we can imagine that the function of B node is to lower the concentration of A straightly without the interference of other factors as well as control the concentration of A more precisely and more freely to make the parameter restriction easier to achieve and at the same time the output range is not too small. As we know, the stochastic error may make vague the linear relationship when the values of y axis are too near. The lower concentration X2 is, the steeper the line is, and so the bigger the range is, which in biology means that the bioreporter is more sensitive to certain environmental signal. Through modifying the parameters of node B, we can get a proper concentration of A node to achieve a good r. In all, the node B is a proportion node.