Team:Prairie View/Project

Prairie View Team is currently developing a molecular biosensor that will detect bivalent metals at different concetrations. The data collected from testing our biosensor will be incorporated with an Electronic-Nose(E-Nose)in attempt to gain specificity within the sensor.

Overall project

Our work is aimed at developing a biosensor for the detection of cations by introducing electron network system proteins into the chassy best suited to receive an enhanced energy flow. The flow of electrons is fueled through the network of oxidation reactions of bivalent metals across Ion channels and Cytochrome complexes. By facilitating the flow of electrons in a cell, we have showed an increase of ATP production. Bacterial cells use ATP for anabolic reactions, including the synthesis of DNA. Through our electron network system, we can enhance the expression of DNA, increasing cell replication along with the fluorescence of our reporters. Together, these incorporated proteins enhance the capacity of our sensor device.

Our goal is to establish specificity within the sensing device. To accomplish this, we have selected new vectors (BSK and pET) and have designed primers to replicate the parts used in previous experiments along with the addition of cytochrome C. Our approach in designing these parts is to assemble ligations with a more modular construction enabling specificity in our sensor. We can now test with each metal alone and in combinations with and without Cytochrome C & Ion Channels.

Our testing system is designed for training a computer model with the data on an accept/reject basis. The targeted divalent cations are accepted by the model because their electrical configurations allow oxidation for the electron network system. Introduced anions will be used for rejected data because their valence decline oxidation and consequently will not produce the ATP/fluorescent values that the model is trained to accept. This accept/reject basis enhances the degree of specificity of our sensor and of the computational model.