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Modeling Molecular Biosensors: Use of eNose & Neural Network System M. Ruiz, J. Lilly, C. Hillbrand, V. Kerksey, E. Farrar, A. Glass, M. Gyamareh, R. Cuero Prairie View A&M University, A Member of the Texas A&M University System, CARC P.O. Box 4079, Prairie View, Texas RESEARCH INSTRUCTOR/PI: Raul Cuero, Ph.D. (email: [email protected] or [email protected]) Abstract Synthetic biology will only be practical when it is related at the cellular level. Biosensors are functional molecules and/or cells including microbial cells that allow detection of the presence of different molecules and/or metal ions such as iron (Fe II), copper (Cu II), nickel (Ni II), and vanadium (V II) u, Ni, V, and other elements, even at detection levels, beyond the limits of conventional methods. Because of the high sensitivity combined with an excellent selectivity, ion channels and cytochrome c can be ideal to be used in biosensor devices. ATP cellular energy source plays an important role on the effect of ion channels on any biosensor. Bacterial cells were transformed with pLES2 vector, with or without promoters (PI and P II from Acidithiobacillus ferroxidans rus operon), and a Lac1 regulated device coding a monomeric yellow fluorescence protein as reporter. The machine was constructed based on the MIT biobrick, containing different parts, and assembled along with newly constructed sequences. Different types of ion channels proteins were assembled to the device. The designed device was standardized to for its specific detection of different levels of Fe II, Ni, V. Transformed cells were grown at different concentrations (0, 0.1, 5, and 10 µ/ml). The response of biosensor was measured by the expression of reporter protein, DNA and ATP fluorescence and/or their concentrations, bacterial growth. The machine was correctly assembled and transformed in E. coli, which was observed through agarose gel electrophoresis of the plasmid and the total DNA. The viability of the machine was also confirmed by the detection of the three metals concentrations, simultaneously, the fluorescence of the DNA, the reporter protein, and the ATP. An eNose is an analytic device originally used for detecting chemicals and their concentrations in vapor. It functions using an array of broadly tuned chemical sensors that interact with a broad range of chemicals with varying strengths. These sensors exhibit some sort of response, which allows the algorithm of the eNose to be applied to more than just the analysis of vapor chemicals. Information from the eNose is connected to an artificial neural network which analyses the data and returns the specific chemical as well as its concentration. Conclusions 1. The genetic machine was assembled, effectively. 2. The genetic machine was assembled with the ion channels showed the highest sensing for the three metals (Fe, V, N), and was able to detect the lowest concentration of the metal. 3. The ATP concentration in the devices showed direct correlation to the DNA expression. 4. Synthetic biology will only be practical when it is related at the cellular level. 5. The eNose and neural network are the appropriate modeling systems for achieving the specificity of the types of metals and their concentration. Objective 1. Use synthetic biology in order to develop a microbial ion-channel-biosensor machine assembling new protein promoter sequences for uptake of different metals, simultaneously. Thus detecting three different metals (Fe II, V, Ni) simultaneously. 2. Correlate ATP concentration with ion channels effect and, consequently with enhancement of sensing by the machine or device. 3. Use of a computational modeling and eNose system to identify the specificity of the metals sensor. The bacterial sensor device is showing enhanced yellow reporter protein (EYFP). References 1. Quintero, A., S. Garcia, C. Guevara, C. Rincon, C. Ospina, P. Guevara, and R. CUERO. 2007. Microbial Biosensor Device for Iron Detection, Under UV Irradiation.IET Synthetic Biology. Vol 1-2: 71-73. 2. Ion Channels in Biological Membranes. 2/19. 2007 Description of the Work In order to achieve the denoted objectives, a genetically engineering machine was assembled for detection three different metals (Fe II, Ni, V). Different gene sequences including protein reporters and promoters, were assembled with different types of ion channels, for enhanced uptake of the three metals. The gene sequences along with standard parts from MIT biobrick were constructed and assembled in vector pLES2 into a chassis (E. coli, competent cells DH10B, INVITROGEN). Sequences for the three metals were synthesized. Promoters PI and PII were used. Sequences for two different ion channels were also synthesized and assembled to the biobrick. Enzymes digestions and ligations were carried out according the instruction by INVITROGEN. The flow of data should be the following: Ions in solution → Sensor → Neural Network → A neural network is capable of modeling relationships among data by learning from examples. “Modeling is the process of formulating a finite set of interrelated rules (or the construction of a finite set of interconnected mechanisms) by which one can guarantee or explain the (possibly infinite) set of observed data.” (L.P Veelenturf, Analysis and applications of Artificial Neural Networks). The fundamental eNose algorithm relies on the equations: r i = f ij (c ij ) r i = response of species i as a function of concentration c ij = concentration of species i in j th sensor The different responses define the different “sensors”. If there are m sensors, there are m different estimations of the concentration; therefore an overall estimate can be given by taking the average The idea is that if the sample is the j th candidate, the series of estimates c ij , c 2j , c mj will all be close to each other, but if the sample is not, the sensors will produce an array of totally different estimates. So, the key factor is the variance If σ j is small enough, then the sample is the j th candidate. But if it large, it must be different. DNA concentration was determined by spectrophotometer, and fluorescence of DNA, and ATP was determined by using the TD-20-20 Luminometer (Turner Biosystems, (USA)25 Metal Ion Ion concentration c j = 1 m c ij + c 2 j + ...... c mj ( ) σ j = 1 m−1 c 1j − c j ( ) 2 + c 2 j − c j ( ) 2 + .... c mj − c j ( ) 2 Current Work In naturally occurring environments, the ions exist in combinations. It is because of this, the initial ligations included the combination of metal ion proteins. In order to introduce specificity, as well as successfully apply the eNose to our project, we must now include data from ligations that include the individual metal ion proteins. The final flow diagram will be eNose Modeling To the right is a flow diagram of the neural network taking only extrinsic factors into consideration. It allows the prediction of concentration only, but not specificity. The transfer function used for back propagation is g( x) = 2 f ( x) −1 = 2 1+ exp(−σx) −1 Results – Constructed Devices
