Modeling and Modifying Response of Biochemical Processes for Biocomputing and Biosensing Signal Processing

Switchable Biocatalytic Reactions Controlled by Interfacial pH Changes Produced by Orthogonal Biocatalytic Processes

Enzymes immobilized on a nano-structured surface were used to switch the activity of one enzyme by a local pH change produced by another enzyme. Immobilized amyloglucosidase (AMG) and trypsin were studied as examples of the pH-dependent switchable "target enzymes." The reactions catalyzed by co-immobilized urease or esterase were increasing or decreasing the local pH, respectively, thus operating as "actuator enzymes." Both kinds of the enzymes, producing local pH changes and changing biocatalytic activity with the pH variation, were orthogonal in terms of the biocatalytic reactions; however, their operation was coupled with the local pH produced near the surface with the immobilized enzymes. The "target enzymes" (AMG and trypsin) were changed reversibly between the active and inactive states by applying input signals (urea or ester, substrates for the urease or esterase operating as the "actuator enzymes") and washing them out with a new portion of the background solution. The developed approach can potentially lead to switchable operation of several enzymes, while some of them are inhibited when the others are activated upon receiving external signals processed by the "actuator enzymes." More complex systems with branched biocatalytic cascades can be controlled by orthogonal biocatalytic reactions activating selected pathways and changing the final output.

Rate-equation modelling and ensemble approach to extraction of parameters for viral infection-induced cell apoptosis and necrosis

We develop a theoretical approach that uses physiochemical kinetics modelling to describe cell population dynamics upon progression of viral infection in cell culture, which results in cell apoptosis (programmed cell death) and necrosis (direct cell death). Several model parameters necessary for computer simulation were determined by reviewing and analyzing available published experimental data. By comparing experimental data to computer modelling results, we identify the parameters that are the most sensitive to the measured system properties and allow for the best data fitting. Our model allows extraction of parameters from experimental data and also has predictive power. Using the model we describe interesting time-dependent quantities that were not directly measured in the experiment and identify correlations among the fitted parameter values. Numerical simulation of viral infection progression is done by a rate-equation approach resulting in a system of "stiff" equations, which are solved by using a novel variant of the stochastic ensemble modelling approach. The latter was originally developed for coupled chemical reactions.