Our project aim is to provide a platform for multiple proteins to be expressed simultaneously at different levels. dCas9 will be targeted to repress a given promoter preceding a protein by using a short guide RNA (sgRNA) corresponding to this promoter. Our initial proof of concept system uses fluorescent proteins as the reporter signal and we have taken five promoter-sgRNA pairings from literature and constructed two plasmids which will give many different combinations of protein levels. One plasmid contains three promoters (although this will be expandable) joined up to three different fluorescent proteins. This plasmid will also express dCas9, which we will submit to the iGEM registry. The second plasmid will express 3 different sgRNAs which target each one of these promoters and, by having the option of a non-targeting sgRNA too, we have ON/OFF switches for these fluorescent proteins. In the future, we can expand this to different levels, rather than just two, by creating mismatches in the sgRNA seed region, which has been shown to reduce efficiency of repression by dCas9. Discernible combinations would only be limited by the reproducibility of signals and accuracy of the measurement. For future expansion, although there is a vast repertoire of fluorescent proteins, any other protein could also be substituted in and measured using other methods. In this way, this system could be used for a vast range of applications, from optimising production of proteins or a metabolic pathway to being used as a biological password. As our assembly method is all interchangeable so any protein can be linked to any promoter, and placed in any position in the plasmid. We have chosen to focus our project on using this random assortment to create a natural random number generator, with application as a biological key as we felt this is the most exciting to the public and shows the design’s mechanism the best. We envisage a system where these plasmids can be assembled randomly (as this is how we designed our system) to produce an enormous number of combinations, which is a valuable characteristic in security. The potential impact from this application is that we will be providing a new, more secure, form of key for accessing content. This is a hot topic at present; numerous major hacking incidents fixate online security at the forefront of many business’s concerns, as accounts are being hacked and sensitive information stolen. Many large companies are deviating away from conventional online character passwords, which are proving to be unreliable in the hands of the public. For instance, many banks are now developing physical biometric authentication procedures to correctly identify the true owner of an account. This new direction opens a market for biological “passwords”. An ideal system would be as decoupled from online software programs as possible, while maintaining the complexity and uniqueness of a biometric system. Cells are effectively living computers so we can programme cells to act as a changeable biometric password. Our product would have no human influence over the outcome of combinations due to our design, making it less prone to issues commonly associated with human carelessness such as using simple, easy to guess passwords. This system combines aspects comparable to the traditional mechanical key, a digital key, and biometric keys - a fourth alternative to the paradigm. Also, this track of research will hopefully be of interest to many and stimulate further research into developing tools for advanced biological computation. The parallels between computation and genetic regulation are astounding, unlocking the potential for fully realised programmable genetic systems would be invaluable to society in numerous ways. For the physical product, we plan on freeze-drying our “programmed” bacteria for storage in a device. We will optimise the conditions for this to optimise reproducibility and allow accurate authentication. The two signals need to have as low a variance as possible, otherwise the accuracy and completeness of emission signals is invalidated and fewer discernible combinations are possible. Therefore, we are using the data collected to form models to predict the outcomes of fluorescence under all possible conditions, and allow each key to be categorised separately by their output.