Faculty Bibliography

A recently experimentally observed biochemical "threshold filtering" mechanism by processes catalyzed by the enzyme malate dehydrogenase is explained in terms of a model that incorporates an unusual mechanism of inhibition of this enzyme that has a reversible mechanism of action. Experimental data for a system in which the output signal is produced by biocatalytic processes of the enzyme glucose dehydrogenase are analyzed to verify the model's validity. We also establish that fast reversible conversion of the output product to another compound, without the additional inhibition, cannot on its own result in filtering.

We report the first study of a network of connected enzyme-catalyzed reactions, with added chemical and enzymatic processes that incorporate the recently developed biochemical filtering steps into the functioning of this biocatalytic cascade. New theoretical expressions are derived to allow simple, few-parameter modeling of network components concatenated in such cascades, both with and without filtering. The derived expressions are tested against experimental data obtained for the realized network's responses, measured optically, to variations of its input chemicals' concentrations with and without filtering processes. We also describe how the present modeling approach captures and explains several observations and features identified in earlier studies of enzymatic processes when they were considered as potential network components for multistep information/signal processing systems.

We report the first systematic study of designed two-input biochemical systems as information processing gates with favorable noise transmission properties accomplished by modifying the gate's response from a convex shape to sigmoid in both inputs. This is realized by an added chemical "filter" process, which recycles some of the output back into one of the inputs. We study a system involving the biocatalytic function of the enzyme horseradish peroxidase, functioning as an AND gate. We consider modularity properties, such as the use of three different input chromogens that when oxidized yield signal detection outputs for various ranges of the primary input, hydrogen peroxide. We also examine possible uses of different filter effect chemicals (reducing agents) to induce the sigmoid response. A modeling approach is developed and applied to our data, allowing us to describe the enzymatic kinetics in the framework of a formulation suitable for evaluating the noise-handling properties of the studied systems as logic gates for information processing steps.

A biomolecular system representing the first realization of associative memory based on enzymatic reactions in vitro has been designed. The system demonstrated "training" and "forgetting" features characteristic of memory in biological systems, but presently realized in simple biocatalytic cascades.

We report a study of a system which involves an enzymatic cascade realizing an AND logic gate, with an added photochemical processing of the output, allowing the gate's response to be made sigmoid in both inputs. New functional forms are developed for quantifying the kinetics of such systems, specifically designed to model their response in terms of signal and information processing. These theoretical expressions are tested for the studied system, which also allows us to consider aspects of biochemical information processing such as noise transmission properties and control of timing of the chemical and physical steps.

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.

We report a new approach to achieve growth of highly crystalline nickel nanoparticles over an extended range of sizes (up to 100 nm in diameter) and time scales (up to several hours) by diffusional transport of constituent atoms. The experimental procedure presented offers control of the morphology of the resulting particles and yields base metal nanocrystals suitable for epitaxial deposition of noble metal shells and the preparation of materials with improved catalytic properties. The reported precipitation system also provides a good model for testing a diffusion-driven growth mechanism developed specifically for the reduction process described.

We investigate performance and optimization of the "digital" bioanalytical response. Specifically, we consider the recently introduced approach of a partial input conversion into inactive compounds, resulting in the "branch point effect" similar to that encountered in biological systems. This corresponds to an "intensity filter," which can yield a binary-type sigmoid-response output signal of interest in information and signal processing and in biosensing applications. We define measures for optimizing the response for information processing applications based on the kinetic modeling of the enzymatic reactions involved, and apply the developed approach to the recently published data for glucose detection.

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.

We report a realization of an associative memory signal/information processing system based on simple enzyme-catalyzed biochemical reactions. Optically detected chemical output is always obtained in response to the triggering input, but the system can also "learn" by association, to later respond to the second input if it is initially applied in combination with the triggering input as the "training" step. This second chemical input is not self-reinforcing in the present system, which therefore can later "unlearn" to react to the second input if it is applied several times on its own. Such processing steps realized with (bio)chemical kinetics promise applications of bioinspired/memory-involving components in "networked" (concatenated) biomolecular processes for multisignal sensing and complex information processing.