Difference between revisions of "Team:Imperial College/Dryover"

 
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                 <h4 class="center">Modelling</h4>
 
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                 <p3>Spatially controlled gene expression requires that the stimulated region is tightly bounded. To verify the feasibility of generating a localised region of redox state of an inducer molecule, we created and solved diffusion models, from steady-state analytical models to time-dependent numerical models using more complicated geometry. Once our diffusion models showed a positive outcome we worked on a transcription-translation model of our genetic construct ‘Patterning Circuit 1’, which we fitted to experimental data in order to estimate unknown model parameters and assess the performance of our constructs.</p3>
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                 <p3>Spatially controlled gene expression requires that the stimulated region is tightly bounded. To verify the feasibility of generating a localised region of redox state using an inducer molecule, we created and solved diffusion models, from steady-state analytical models to time-dependent numerical models using more complicated geometry. Once our diffusion models showed a positive outcome we worked on a transcription-translation model of our genetic construct ‘Patterning Circuit 1’, which we fitted to experimental data in order to estimate unknown model parameters and assess the performance of our constructs.</p3>
 
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            <h3>Experimental Set-up</h3>
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Latest revision as of 01:10, 18 October 2018

Overview

Our Aims

Due to the highly integrated and multidisciplinary nature of the project we have undertaken, the non-biological aspects of experimental design and engineering were incredibly important. Thus, we performed extensive modelling, numerous characterisation experiments and involved and thorough engineering design in order to reinforce the synthetic biology of our project.

Modelling

Spatially controlled gene expression requires that the stimulated region is tightly bounded. To verify the feasibility of generating a localised region of redox state using an inducer molecule, we created and solved diffusion models, from steady-state analytical models to time-dependent numerical models using more complicated geometry. Once our diffusion models showed a positive outcome we worked on a transcription-translation model of our genetic construct ‘Patterning Circuit 1’, which we fitted to experimental data in order to estimate unknown model parameters and assess the performance of our constructs.

Electrochemistry Testing

In addition to modelling, we worked on making a cheap electrochemistry setup. The previous work on electronic control of gene expression by Tschirhart et al. (2017) used a very expensive electrochemistry setup. The electrodes used in the aforementioned research cost about £1000, whereas the electrodes we have used to characterize and stimulate our system cost less than £5. This translates to a cost reduction of over 99%, whilst still retaining the performance to be able to successfully characterise the electrochemistry of, and drive gene expression within, our system.

Integration

Once the proof of concept was completed, we combined our modelling results with our experience with the basic electrochemisty setup to create a state-of-the-art programmable electrode array for spatial control of gene expression. This electrode array is controllable from any Android phone via an Arduino bluetooth link, raising the field of precision control over genetic circuits to new heights of accessibility.