IGEM:IMPERIAL/2008/New/Genetic Circuit

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= Genetic Circuit =
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&nbsp; <font size=6px color=#E5EBFF><b>Genetic Circuit</b></font>
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== Why model the genetic circuit? ==
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An accurate mathematical description of the genetic circuit is essential for projects involving synthetic biology. Such descriptions are an integral component of part submission to the Registry, as exemplified by the canonical characterisation of part F2620 <cite>#1</cite>. The ability to capture part behaviour as a mathematical relationship between input and output is useful for future re-use of the part and modification of integration into novel genetic circuits.
An accurate mathematical description of the genetic circuit is essential for projects involving synthetic biology. Such descriptions are an integral component of part submission to the Registry, as exemplified by the canonical characterisation of part F2620 <cite>#1</cite>. The ability to capture part behaviour as a mathematical relationship between input and output is useful for future re-use of the part and modification of integration into novel genetic circuits.
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Revision as of 13:45, 27 September 2008






  Genetic Circuit


An accurate mathematical description of the genetic circuit is essential for projects involving synthetic biology. Such descriptions are an integral component of part submission to the Registry, as exemplified by the canonical characterisation of part F2620 [1]. The ability to capture part behaviour as a mathematical relationship between input and output is useful for future re-use of the part and modification of integration into novel genetic circuits.

Modelling Constitutive Gene Expression

Constitutive expression of antibiotic resistance (AB) and GFP. GFP brick is part E0040, GFPmut3b. Terminator is part B0015, the double-stop.
Constitutive expression of antibiotic resistance (AB) and GFP. GFP brick is part E0040, GFPmut3b. Terminator is part B0015, the double-stop.

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.

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

In this case, [protein] represents the concentration of GFP, k1 represents the rate of sythesis and d1 represents the degradation rate. We can easily simulate this synthesis-degradation model using matlab:
ODE
Simulation File

Parameter scan on k1

Parameter scan on d1

We can also solve this ODE analytically. Consider the steady-state behaviour of [protein].

\frac{d[protein]}{dt}=0 \Rightarrow [protein]_{steady-state}=\frac{k_1}{d_1}

This relationship can be seen 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 k1 = 0, no GFP is sythesised.
  • In the case where d1 = 0, 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.

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

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

R + I \Leftrightarrow RI

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

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

And overall protein expression can be described as

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

ODE

Simulation

  1. Canton B, Labno A, and Endy D. . pmid:18612302. PubMed HubMed [1]


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