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Register a new userTwo-Input Enzymatic Logic Gates Made Sigmoid by Modifications of the Biocatalytic Reaction Cascades
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Computing based on biochemical processes is a newest rapidly developing field of unconventional information and signal processing. In this paper we present results of our research in the field of biochemical computing and summarize the obtained numerical and experimental data for implementations of the standard two-input OR and AND gates with double-sigmoid shape of the output signal. This form of response was obtained as a function of the two inputs in each of the realized biochemical systems. The enzymatic gate processes in the first system were activated with two chemical inputs and resulted in optically detected chromogen oxidation, which happens when either one or both of the inputs are present. In this case, the biochemical system is functioning as the OR gate. We demonstrate that the addition of a filtering biocatalytic process leads to a considerable reduction of the noise transmission factor and the resulting gate response has sigmoid shape in both inputs. The second system was developed for functioning as an AND gate, where the output signal was activated only by a simultaneous action of two enzymatic biomarkers. This response can be used as an indicator of liver damage, but only if both of these of the inputs are present at their elevated, pathophysiological values of concentrations. A kinetic numerical model was developed and used to estimate the range of parameters for which the experimentally realized logic gate is close to optimal. We also analyzed the system to evaluate its noise-handling properties.
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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 gates response from 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.
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 to make the gates response 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.
Several different enzymes display an apparent diffusion coefficient that increases with the concentration of their substrate. Moreover, their motion becomes directed in substrate gradients. Currently, there are several competing models for these transport dynamics. Here, we analyze whether the enzymatic reactions can generate a significant feedback from enzyme transport onto the substrate profile. We find that this feedback can generate spatial patterns in the enzyme distribution, with just a single-step catalytic reaction. However, patterns are formed only for a subclass of transport models. For such models, nonspecific repulsive interactions between the enzyme and the substrate cause the enzyme to accumulate in regions of low substrate concentration. Reactions then amplify local substrate fluctuations, causing enzymes to further accumulate where substrate is low. Experimental analysis of this pattern formation process could discriminate between different transport models.
Biochemical reactions are fundamentally noisy at a molecular scale. This limits the precision of reaction networks, but also allows fluctuation measurements which may reveal the structure and dynamics of the underlying biochemical network. Here, we study non-equilibrium reaction cycles, such as the mechanochemical cycle of molecular motors, the phosphorylation cycle of circadian clock proteins, or the transition state cycle of enzymes. Fluctuations in such cycles may be measured using either of two classical definitions of the randomness parameter, which we show to be equivalent in general microscopically reversible cycles. We define a stochastic period for reversible cycles and present analytical solutions for its moments. Furthermore, we associate the two forms of the randomness parameter with the thermodynamic uncertainty relation, which sets limits on the timing precision of the cycle in terms of thermodynamic quantities. Our results should prove useful also for the study of temporal fluctuations in more general networks.
We describe modeling approaches to a network of connected enzyme-catalyzed reactions, with added (bio)chemical processes that introduce biochemical filtering steps into the functioning of such a biocatalytic cascade. Theoretical expressions are derived that allow simple, few-parameter modeling of processes concatenated in such cascades, both with and without filtering. The modeling approach captures and explains features identified in earlier studies of enzymatic processes considered as potential network components for multi-step information/signal processing systems.
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