Initially, we aimed to develop and optimize a stable microbial co-culture system whereby the source of energy would be light, and carbon would flow from CO₂ in the atmosphere to synthesise ethanol: acting as a surrogate for biofuel. This simple synthetic microbial community comprises Chlamydomonas reinhardtii, an algae, that produces sugars through photosynthesis to feed the ethanol-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 and, in trying to monitor this, we discovered that many methods of co-culture analysis require substantial improvement.

In response to this realisation, we aim to use Digital Inline Holographic Microscopy (DIHM) to analyse co-cultures. 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 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 then analyse this stack of images to track the number of each type of microbe present. This method of co-culture analysis has a great deal of potential in relation to counting organisms in co-cultures more quickly and less expensively than other methods.

To implement this, images are taken from a camera via Raspberry Pi computer and sent to a computer running Windows. Here, code written in MATLAB forms the aforementioned stack of 2D images. 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 are able to compare these results to our mathematical model. Then, we can then identify the best ways to modify the conditions of the co-culture in order to optimise the growth of the bacteria and, thence, the output of ethanol from the system. A Windows application has been written, in C#, which allows a user to fully control the system without any interaction with the code itself.