Imperial/Dry Lab/Software

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==Data Analysis==
==Data Analysis==
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* '''Chassis Characterisation with the Classic Promoter Model'''
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The routine allows you to load the results of several experiments at the same time. These data are then analysed as described in section 3. Once the best fit is identified, the results are returned and the corresponding graphs are plotted.
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* '''Chassis Characterisation with the Resource dependent  Model'''
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The routine allows you to load the results of several experiments at the same time. These data are then analysed as described in Simulations & Results. Once the best fit is identified, the results are returned and the corresponding graphs are plotted.
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Warning: we reduced the subspace of parameters to browse in a way that may not fit your data. The method for doing so is explained in this pdf . We strongly recommend you use a similar method for your data unless you are willing to let your computer run for a very long time (and trust the ODE solver in Matlab…)
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== Analysis of the Degradation of a Protein ==
== Analysis of the Degradation of a Protein ==

Revision as of 11:44, 26 October 2007

Useful Tools

During this summer the Imperial iGEM team has developed several routines in MATLAB. We are happy to share them with the rest of Synthetic Biology Community and hope they will prove useful.

Data Analysis

  • Chassis Characterisation with the Classic Promoter Model

The routine allows you to load the results of several experiments at the same time. These data are then analysed as described in section 3. Once the best fit is identified, the results are returned and the corresponding graphs are plotted.

  • Chassis Characterisation with the Resource dependent Model

The routine allows you to load the results of several experiments at the same time. These data are then analysed as described in Simulations & Results. Once the best fit is identified, the results are returned and the corresponding graphs are plotted.

Warning: we reduced the subspace of parameters to browse in a way that may not fit your data. The method for doing so is explained in this pdf . We strongly recommend you use a similar method for your data unless you are willing to let your computer run for a very long time (and trust the ODE solver in Matlab…)

Analysis of the Degradation of a Protein

Constitutive synthesis of a Protein

The experimental setup involves tagging a constitutive promoter with a fluorescent protein; the resultant fluorescence is then recorded and it is up to the user now to perform some analysis. This routine loads the experimental data as an excel sheet, allows for graphic visualization of the results, and if need be, elimination of some samples which may seem suspect (outliers). The synthesis rate and degradation term of the milieu are defined/estimated by the user. The routine was developed to impart control to the user over all the operations.

Infector Detector

In the design phase, two possible system constructs were proposed, as a solution to the problem of detecting AHL-producing biofilm.
Our modelling team established that the system is governed by a set of energy-dependent coupled ODEs, which hold true for both system constructs. Numerous simulations were consequently performed on the system, for a particular set of biologically plausible parameters.
The routines employed in these simulations are now presented.

ID_EnergyODE.m

This function presents the vector of energy-dependent ODEs governing Infector Detector(ID). The set of representative parameters is passed globally.

ID_Sim_Transfer.m

This script allows for user-defined simulation of the transfer functions of both system constructs, which can either be visualized independently, or simultaneously, for comparison. The routine defines a set of parameter values and simulates the dynamic behaviour of the system by invoking the ODE set, ID_EnergyODE, for use by MATLAB's ode15s solver. The transfer functions, [GFP] vs [AHL], are computed and plotted on a semi-logarithmic scale. The user maintains control over the range of [AHL] over which the computation should occur.

ID_Sim_InputVar.m

A user-defined routine, performing simulations of the dynamic behaviour of the system, where the user maintains control over which inputs are to be investigated. e.g. varying initial [LuxR] of construct 2, to observe its resultant behaviour, in terms of GFP expression and/or Energy depletion.


Download ID Routines

The above routines may be downloaded together in zipped form. Please access the following link.


Cell-by-Date

Cell-by-Date is a Temperature-Time-Integrator, which serves to expose cold-chain breaks in the propagation of highly-perishible foodstuffs, e.g. freshly ground-beef. An investigation of its behaviour was necessarily first performed by way of simulations; the following routines were employed in this process.

CBD_EnergyODE.m

This function presents the vector of energy-dependent ODEs governing Cell-by-Date(CBD). The representative parameters are passed globally.

CBD_Sim.m

Here, MATLAB's ode23 is employed in the time-solution of the vector of ODEs passed by the function,CBD_EnergyODE. Plots of resultant GFP expression and depletion of energy(E) are generated.


Download CBD Routines

The above routines may be downloaded together in zipped form. Please access the following link.