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Register a new userExtended Linear Response for Bioanalytical Applications Using Multiple Enzymes
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We develop a framework for optimizing a novel approach to extending the linear range of bioanalytical systems and biosensors by utilizing two enzymes with different kinetic responses to the input chemical as their substrate. Data for the flow-injection amperometric system devised for detection of lysine based on the function of L-Lysine-alpha-Oxidase and Lysine-2-monooxygenase are analyzed. Lysine is a homotropic substrate for the latter enzyme. We elucidate the mechanism for extending the linear response range and develop optimization techniques for future applications of such systems.
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The first realization of a biomolecular OR gate function with double-sigmoid response (sigmoid in both inputs) is reported. Two chemical inputs activate the enzymatic gate processes resulting in the output signal: chromogen oxidation, which occurs when either one of the inputs or both are present (corresponding to the OR binary function), and can be optically detected. High-quality gate functioning in handling of sources of noise is enabled by filtering involving pH control with an added buffer. The resulting gate response is sigmoid in both inputs when proper system parameters are chosen, and the gate properties are theoretically analyzed within a model devised to evaluate its noise-handling properties.
Processes involving multi-input multi-step reaction cascades are used in developing novel biosensing, biocomputing, and decision making systems. In various applications different changes in responses of the constituent processing steps (reactions) in a cascade are desirable in order to allow control of the systems response. Here we consider conversion of convex response to sigmoid by intensity filtering, as well as threshold filtering, and we offer a general overview of this field of research. Specifically, we survey rate equation modelling that has been used for enzymatic reactions. This allows us to design modified biochemical processes as network components with responses desirable in applications.
We report the first realization of a biomolecular AND gate function with double-sigmoid response (sigmoid in both inputs). Two enzyme biomarker inputs activate the gate output signal which can then be used as indicating liver injury, but only when both of these inputs have elevated pathophysiological concentrations, effectively corresponding to logic-1 of the binary gate functioning. At lower, normal physiological concentrations, defined as logic-0 inputs, the liver-injury output levels are not obtained. High-quality gate functioning in handling of various sources of noise, on time scales of relevance to potential applications is enabled by utilizing filtering effected by a simple added biocatalytic process. The resulting gate response is sigmoid in both inputs when proper system parameters are chosen, and the gate properties are theoretically analyzed within a model devised to evaluate its noise-handling properties.
Enzymes within biochemical pathways are often colocalized, yet the consequences of specific spatial enzyme arrangements remain poorly understood. We study the impact of enzyme arrangement on reaction efficiency within a reaction-diffusion model. The optimal arrangement transitions from a cluster to a distributed profile as a single parameter, which controls the probability of reaction versus diffusive loss of pathway intermediates, is varied. We introduce the concept of enzyme exposure to explain how this transition arises from the stochastic nature of molecular reactions and diffusion.
Enzymes and proteins are live driven biochemicals, which has a dramatic impact over the environment, in which it is active. So, therefore, it is highly looked-for to build such a robust and highly accurate automatic and computational model to accurately predict enzymes nature. In this study, a novel split amino acid composition model named piSAAC is proposed. In this model, protein sequence is discretized in equal and balanced terminus to fully evaluate the intrinsic correlation properties of the sequence. Several state-of-the-art algorithms have been employed to evaluate the proposed model. A 10-folds cross-validation evaluation is used for finding out the authenticity and robust-ness of the model using different statistical measures e.g. Accuracy, sensitivity, specificity, F-measure and area un-der ROC curve. The experimental results show that, probabilistic neural network algorithm with piSAAC feature extraction yields an accuracy of 98.01%, sensitivity of 97.12%, specificity of 95.87%, f-measure of 0.9812and AUC 0.95812, over dataset S1, accuracy of 97.85%, sensitivity of 97.54%, specificity of 96.24%, f-measure of 0.9774 and AUC 0.9803 over dataset S2. Evident from these excellent empirical results, the proposed model would be a very useful tool for academic research and drug designing related application areas.
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