Team:York
Biology
Genetically engineering C. reinhardtii and E. coli in order to form a co-culture to allow the creation of biofuels.
Hardware
Using optical diffraction to create and probe 3D images through holography in order to monitor co-cultures.
Software
Taking images using the hardware and turning this into a holograph, then analysing this image to optimise co-cultures.
With the decreasing supply but increasing demand for fossil fuels, biofuels are a renewable alternative to help cope with our growing energy needs. We aim to develop and optimize a stable microbial co-culture system whereby the source of energy is light, and carbon flows from CO₂ in the atmosphere to synthesise a biofuel. This simple synthetic microbial community will comprise Chlamydomonas reinhardtii, an algae, that will produce sugars through photosynthesis to feed the biofuel-producing Escherichia coli, ideally resulting in a growth system that could reduce the cost of feedstock materials for biofuel production. However, their differing growth rates would likely result in an unstable system in which one organism might outgrow the other.
To monitor the ratio of C. reinhardtii to E. coli, we will be using Digital Holographic Microscopy (DHM). This involves illuminating a sample of the co-culture with a laser and observing the diffraction pattern formed by the microbes. This pattern is sensitive to the wavelength of the laser light, the distance from the co-culture sample and the shape, size and position of the microbes. The mathematical and physical relationship between these quantities is well described, so we can calculate what the cross-section of the sample would look like at various levels. This allows us to form a stack of 2D images which, when combined, represent the 3D sample. We can then analyse this stack of images to track the number of each type of microbe present.
Images are taken from a Raspberry Pi computer and sent to a Windows machine where a piece of MATLAB code forms a stack of 2D images which, when combined, represent the 3D sample. We can then analyse this stack of images to establish the number of each type of microorganism present in the co-culture. From this information we will be able to compare these results to our mathematical model, where we can then modify the conditions of the co-culture through the use of the Raspberry Pi in order to optimise the growth of the bacteria, which will then increase output of the ethanol from the system. The whole process is tied together through a Windows Application written in C#, allowing a user to fully control the system without any interaction with the code itself.
