This work describes the theoretical and experimental approaches for monitoring the interfacial biomolecular reaction between immobilized antibody and the antigen binding partner using novel differential impedance spectroscopy. The prerequisite of any biosensor is the immobilization of macromolecules onto the surface of a transducer. It is clear that the function of most macromolecules changes from what is observed in solution once immobilization has occurred. In the worst case, molecules entirely lose their binding activity almost immediately after immobilization. Certain conditions (e.g., denaturation, interfacial effects based on ionic strength, surface charge, dielectric constants, etc.) at interfaces are responsible for alterations of binding activity; it is not clear whether a combination of such processes is understood. However, these processes in combination must be reliably modeled in order to predict the outcome for most macromolecules. This work presents the theoretical and practical means for elucidating the surface reactivity of biomolecular reagents using ion displacement model with antibodyantigen (Ab-Ag) reaction as the test case. The Ab-Ag reaction was directly monitored using a dual-channeled, impedance analyzer capable of 1 measurement/s using covalent immobilization chemistry and polymer-modified electrodes in the absence of a redox probe. The evidence of Ab-Ag binding was revealed through the evolution of differential admittance. The surface loading obtained using the covalent immobilization chemistry was 9.0 × 10 16 /cm 2 , whereas with polymer-modified electrodes, the surface loading was 9.0 × 10 15 /cm 2 , representing a 10 times increase in surface reactivity. The proposed approach may be applicable to monitoring other surface interfacial reactions such as DNA-DNA interactions, DNA-protein interactions, and DNA-small molecule interactions.

Techniques for learning automata have been adapted to automatically infer assumptions in assume-guarantee compositional verification. Learning, in this context, produces assumptions and modifies them using counterexamples obtained by model checking components separately. In this process, the interface alphabets between components, that constitute the alphabets of the assumption automata, are fixed: they include all actions through which the components communicate. This paper introduces alphabet refinement, a novel technique that extends the assumption learning process to also infer interface alphabets. The technique starts with only a subset of the interface alphabet and adds actions to it as necessary until a given property is shown to hold or to be violated in the system. Actions to be added are discovered by counterexample analysis. We show experimentally that alphabet refinement improves the current learning algorithms and makes compositional verification by learning assumptions more scalable than non-compositional verification.

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There are only a few tools suitable for measuring the extracellular pH of adherently growing mammalian cells with high spatial resolution, and none of them is widely used in laboratories around the world. Cell biologists very often limit themselves to measuring the intracellular pH with commercially available fluorescent probes. Therefore, we built a voltammetric pH microsensor and investigated its suitability for monitoring the extracellular pH of adherently growing mammalian cells. The voltammetric pH microsensor consisted of a 37 μm diameter carbon fiber microelectrode modified with reduced graphene oxide and syringaldazine. While graphene oxide was used to increase the electrochemically active surface area of our sensor, syringaldazine facilitated pH sensing through its pH-dependent electrochemical oxidation and reduction. The good sensitivity (60 ± 2.5 mV/pH unit), reproducibility (coefficient of variation ≤3% for the same pH measured with 5 different microsensors), and stability (pH drift around 0.05 units in 3 h) of the built voltammetric pH sensors were successfully used to investigate the acidification of the extracellular space of both cancer cells and normal cells. The results indicate that the developed pH microsensor and the perfected experimental protocol based on scanning electrochemical microscopy can reveal details of the pH regulation of cells not attainable with pH sensors lacking spatial resolution or which cannot be reproducibly positioned in the extracellular space.