Team:UCL/Humans/Attributions

The UCL iGEM 2014 team would like to thank all our advisors who have assisted us throughout the project, and without whom the project would not have been possible. We would also like to thank all everyone else who has helped us realise this project, be it through invaluable advice or providing DNA, seeds, or other materials. These contributions have helped us enormously. All work on this wiki was carried out and all data collected by us unless stated otherwise. For a full list of our collaborations & acknowledgements, please visit our collaborations page.

One of the major hurdles in microfluidics is the fact that fluids are very difficult to mix at small volumes as a result of low Reynolds numbers. Reynolds number describes flow patterns in various flow situations; a low Reynolds number indicates laminar flow, whereas a large Reynolds number indicates turbulent flow. Turbulent flow allows highly effective mixing in comparison to laminar flow. However, in microfluidics several device designs are available, which (to a greater or lesser degree) allow sufficient mixing. The flip side of this is that diffusion is often the only method of mixing. This in itself can be very useful, particularly when investigating enzyme kinetics.

Since our project involves designing a novel bioprocess using whole-cell biocatalysts, microfluidics presents us with a unique and extremely useful advantage. When it comes to identifying, developing and optimising reactor designs and reaction constraints, this can be performed with ease and with low reagent cost as all variables are scaled down to a micro-level. Most importantly, the scale-down can be carried out without losing any of the accuracy or quantification of data output; this is due the number of sensors and control mechanisms which can be integrated into the microfluidic system.

We will use rapid polymer prototyping techniques to generate microfluidic chips that will allow us to test our reaction and aid in the construction of a realistic bioprocess, which can be successfully scaled-up for industrial use. As we optimise and change our bioprocess, we can also quickly design new microfluidic chips that can mimic its development on a micro-scale. For example, it is our goal to integrate multiple downstream steps, such as chromatography, in order to isolate potential useful products. Demonstrating this in a microfluidic system is less time-consuming and far more cost effective than doing so at a larger scale.

For our microfluidic bioreactor, we will be using a magnetic free floating bar as our mixing system. This is an effective method of mixing at a microfluidic scale, as demonstrated in the video on the right. This video is of a microfluidic chemostat bioreactor designed by Davies et al. 2014 UCL, using a free-floating bar to mix two dyes.

An example of one of our microfluidic devices designed on AutoCAD can be downloaded here. This device utilises the basic concept of mixing the cells and dyes, producing a single output stream; much alike to the bioprocessing concept. During the course of designing the microfluidic device, several key considerations must be taken into account: ability to withstand high pressure without leakage; materials of construction to be inert and transparent; size constraints of inlet and outlet piping; ability to accurately 3D-print the device.

Since our project involves designing a novel bioprocess using whole-cell biocatalysts, microfluidics presents us with a unique and extremely useful advantage. When it comes to identifying, developing and optimising reactor designs and reaction constraints, this can be performed with ease and with low reagent cost as all variables are scaled down to a micro-level. Most importantly, the scale-down can be carried out without losing any of the accuracy or quantification of data output; this is due the number of sensors and control mechanisms which can be integrated into the microfluidic system.

Since our project involves designing a novel bioprocess using whole-cell biocatalysts, microfluidics presents us with a unique and extremely useful advantage. When it comes to identifying, developing and optimising reactor designs and reaction constraints, this can be performed with ease and with low reagent cost as all variables are scaled down to a micro-level. Most importantly, the scale-down can be carried out without losing any of the accuracy or quantification of data output; this is due the number of sensors and control mechanisms which can be integrated into the microfluidic system.

Since our project involves designing a novel bioprocess using whole-cell biocatalysts, microfluidics presents us with a unique and extremely useful advantage. When it comes to identifying, developing and optimising reactor designs and reaction constraints, this can be performed with ease and with low reagent cost as all variables are scaled down to a micro-level. Most importantly, the scale-down can be carried out without losing any of the accuracy or quantification of data output; this is due the number of sensors and control mechanisms which can be integrated into the microfluidic system.

Since our project involves designing a novel bioprocess using whole-cell biocatalysts, microfluidics presents us with a unique and extremely useful advantage. When it comes to identifying, developing and optimising reactor designs and reaction constraints, this can be performed with ease and with low reagent cost as all variables are scaled down to a micro-level. Most importantly, the scale-down can be carried out without losing any of the accuracy or quantification of data output; this is due the number of sensors and control mechanisms which can be integrated into the microfluidic system.