Biomolecular Breadboards:Models: Difference between revisions
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MATLAB code: | MATLAB code (plotting commands omitted): | ||
% Set up the standard TXTL tubes | % Set up the standard TXTL tubes | ||
tube1 = txtl_extract('e1'); | tube1 = txtl_extract('e1'); | ||
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% Run a simulaton | % Run a simulaton | ||
[t_ode, x_ode, names] = sbiosimulate(Mobj, configsetObj); | [t_ode, x_ode, names] = sbiosimulate(Mobj, configsetObj); | ||
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[[Image:txtl-negautoreg.png|right|300px]] | [[Image:txtl-negautoreg.png|right|300px]] | ||
Revision as of 08:21, 11 September 2012
| Home | Protocols | DNA parts | Preliminary Data | Models | More Info |
This page contains information about models for simulation and analysis of biomolecular breadboard circuits.
TX-TL Toolbox for MATLAB
The TX-TL toolbox for MATLAB is a set of MATLAB functions that are designed to simplify the modeling of circuits used in the TX-TL cell free expression system. The source code for the toolbox is available via SourceForge.
Simple example
The following code sets up a simple simulation of a negatively autoregulated gene in the TXTL system:
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MATLAB code (plotting commands omitted): % Set up the standard TXTL tubes
tube1 = txtl_extract('e1');
tube2 = txtl_buffer('b1');
% Set up a tube that will contain our DNA
tube3 = txtl_newtube('circuit');
dna_tetR = txtl_dna(tube3, 'ptet', 'rbs', 'tetR', 100, 'linear');
dna_gamS = txtl_dna(tube3, 'p70', 'rbs', 'gamS', 10, 'plasmid');
% Mix the contents of the individual tubes and add some inducer
Mobj = txtl_combine([tube1, tube2, tube3], [6, 2, 2]);
txtl_addspecies(Mobj, 'aTc', 0.1);
% Run a simulaton
[t_ode, x_ode, names] = sbiosimulate(Mobj, configsetObj);
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Additional Models
In addition to the TX-TL toolbox, several additional models have been developed for individual breadboard projects.
GamS modeling
For linear DNA, GamS can be used to reduce the rate of degradation of DNA and increase the expression level (see preliminary data for more information). The figure below shows the results of a preliminary model that we are developing to capture the affects of gamS.
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Figure 1. Protein expression as a function of gamS concentration. The simulation results show the output of the model in 'protsynt81bis.m', generated with the script 'gamS_plot.m'.
