Optimization of Enzymatic Logic Gates and Networks for Noise Reduction and Stability

Biochemical computing attempts to process information with biomolecules and biological objects. In this work we review our results on analysis and optimization of single biochemical logic gates based on enzymatic reactions, and a network of three gates, for reduction of the "analog" noise buildup. For a single gate, optimization is achieved by analyzing the enzymatic reactions within a framework of kinetic equations. We demonstrate that using co-substrates with much smaller affinities than the primary substrate, a negligible increase in the noise output from the logic gate is obtained as compared to the input noise. A network of enzymatic gates is analyzed by varying selective inputs and fitting standardized few-parameters response functions assumed for each gate. This allows probing of the individual gate quality but primarily yields information on the relative contribution of the gates to noise amplification. The derived information is then used to modify experimental single gate and network systems to operate them in a regime of reduced analog noise amplification.Comment: 7 pages in PD

Enzymatic AND Logic Gates Operated Under Conditions Characteristic of Biomedical Applications

Experimental and theoretical analyses of the lactate dehydrogenase and glutathione reductase based enzymatic AND logic gates in which the enzymes and their substrates serve as logic inputs are performed. These two systems are examples of the novel, previously unexplored, class of biochemical logic gates that illustrate potential biomedical applications of biochemical logic. They are characterized by input concentrations at logic 0 and 1 states corresponding to normal and abnormal physiological conditions. Our analysis shows that the logic gates under investigation have similar noise characteristics. Both significantly amplify random noise present in inputs, however we establish that for realistic widths of the input noise distributions, it is still possible to differentiate between the logic 0 and 1 states of the output. This indicates that reliable detection of abnormal biomedical conditions is indeed possible with such enzyme-logic systems.Comment: PDF, 29 page

Realization and Properties of Biochemical-Computing Biocatalytic XOR Gate Based on Enzyme Inhibition by a Substrate

We consider a realization of the XOR logic gate in a process biocatalyzed by an enzyme (here horseradish peroxidase: HRP), the function of which can be inhibited by a substrate (hydrogen peroxide for HRP), when the latter is inputted at large enough concentrations. A model is developed for describing such systems in an approach suitable for evaluation of the analog noise amplification properties of the gate. The obtained data are fitted for gate quality evaluation within the developed model, and we discuss aspects of devising XOR gates for functioning in "biocomputing" systems utilizing biomolecules for information processing

Artificial Muscle Reversibly Controlled by Enzyme Reactions

Chemically induced actuation of a polypyrrole (Ppy) artificial muscle was controlled by biocatalytic reactions, resulting in changes in the redox state of the polymer film mediated by soluble redox species. The biocatalytic process triggered by diaphorase in the presence of NADH resulting in the reduction of the Ppy film was reflected by the potential shift in the negative direction generated in the film. Conversely, the biocatalytic process driven by laccase in the presence of O₂ resulted in the oxidation of the Ppy film, thus yielding the positive potential shift. Both reactions produced opposite bending of the Ppy flexible strip, allowing reversible actuation controlled by the biocatalytic processes. The biocatalytic reactions governing the chemical actuator can be extended to multistep cascades processing various patterns of biochemical signals and mimicking logic networks. The present chemical actuator exemplifies the first mechanochemical device controlled by biochemical means with the possibility to scale up the complexity of the biochemical signal-processing system