IGEM:IMPERIAL/2008/New/Genetic Circuit: Difference between revisions

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<math>\frac{d[protein]}{dt} = Transcription - d_2[protein]</math>
<math>\frac{d[protein]}{dt} = Transcription - d_2[protein]</math>


[[medai:ConcentrationsODE.m|ODE]]
[[media:ConcentrationsODE.m|ODE]]




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Genetic Circuit

Authors: Erika

Editors: Erika

Erika's to do list:

Upload F2620 citation.

Change internal links to redirect to new wiki

Get justification of equations working

Why model the genetic circuit?

An accurate mathematical description of genetic circult behaviour is one of the foundations of synthetic biology. Such descriptions are an integral component of part submission to the registry, as exemplified by the canonical characterised part F2620. <citation needed>. The ability to capture part behaviour as a mathematical relationship between input and output is useful for future re-use of the part modification of integration into novel genetic circuits.

Modelling Constitutive Gene Expression

A simple synthesis-degradation model is assumed for the modelling of the expression of a protein under the control of a constitutive promoter, with the same model assumed for all four promoter-RBS constructs. The synthesis-degradation model assumes a steady state level of mRNA.

[math]\displaystyle{ \frac{d[protein]}{dt} = k_{1} - d_{1}[protein] }[/math]

In this case, [math]\displaystyle{ [protein] }[/math] represents the concentration of GFP, [math]\displaystyle{ k_{1} }[/math] represents the rate of sythesis and [math]\displaystyle{ d_{1} }[/math] represents the degradation rate. We can easily simulate this synthesis-degradation model using matlab:
ODE
Simulation File

  • Parameter scan on [math]\displaystyle{ k_1 }[/math]

    Parameter scan on [math]\displaystyle{ k_1 }[/math]

  • Parameter scan on [math]\displaystyle{ d_1 }[/math]

    Parameter scan on [math]\displaystyle{ d_1 }[/math]

We can also solve this ODE analytically.
At [math]\displaystyle{ \frac{d[protein]}{dt}=0 }[/math], [math]\displaystyle{ [protein]=\frac{k_1}{d_1} }[/math] and you can see this relationship in the parameter scan graphs.
From the wetlab experiments it is likely that we will obtain steady-state data for each of the four promoter-RBS constructs. If we assume the same rate of degradation of GFP in each case, we can have some measure of the relative rate of transcription through each promoter which will help us with the selection of the most appropriate promoter to use for Phase 2. In order to obtain an absolute measure of transcription (as opposed to a relative measure of transcriptional strength) we require constitutive expression in terms of molecules per cell (as opposed to fluorescene in arbitrary units).
Note from the parameter scan graphs:

  • In the case where [math]\displaystyle{ k_1 = 0 }[/math], no GFP is sythesised.
  • In the case where [math]\displaystyle{ d_1 = 0 }[/math], the concentration of protein does not reach a steady state.

Modelling Inducible Gene Expression

The repressor is constitutively expressed. Hence we can assume the constitutive expression model from the previous characterisation step.

[math]\displaystyle{ \frac{d[R]}{dt} = k_{1} - d_{1}[R] }[/math]

When the inducer is added it binds reversibly to the repressor.

[math]\displaystyle{ R + I \Leftrightarrow RI }[/math]

Repressor only binds to the promoter when it is in its unbound form, hence transcription will be a function of free repressor concentration.

[math]\displaystyle{ Transcription = \frac{\beta.{[R]}^n}{{K_m}^n+[R]^n} }[/math]

And overall protein expression can be described as

[math]\displaystyle{ \frac{d[protein]}{dt} = Transcription - d_2[protein] }[/math]

ODE



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