Dataset Information

Stochastic Binding Dynamics of a Photoswitchable Single Supramolecular Complex.

ABSTRACT: In this work, a real-time precise electrical method to directly monitor the stochastic binding dynamics of a single supramolecule based on the host-guest interaction between a cyclodextrin and an azo compound is reported. Different intermolecular binding states during the binding process are distinguished by conductance signals detected from graphene-molecule-graphene single-molecule junctions. In combination with theoretical calculations, the reciprocating and unidirectional motions in the trans form as well as the restrained reciprocating motion in the cis form due to the steric hindrance is observed, which could be reversibly switched by visible and UV irradiation. The integration of individual supramolecules into nanocircuits not only offers a facile and effective strategy to probe the dynamic process of supramolecular systems, but also paves the way to construct functional molecular devices toward real applications such as switches, sensors, and logic devices.

SUBMITTER: Su D

PROVIDER: S-EPMC9069358 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Stochastic Binding Dynamics of a Photoswitchable Single Supramolecular Complex.

Su Dingkai D Zhou Shuyao S Masai Hiroshi H Liu Zihao Z Zhou Ce C Yang Chen C Li Zhizhou Z Tsuda Susumu S Liu Zhirong Z Terao Jun J Guo Xuefeng X

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20220302 13

In this work, a real-time precise electrical method to directly monitor the stochastic binding dynamics of a single supramolecule based on the host-guest interaction between a cyclodextrin and an azo compound is reported. Different intermolecular binding states during the binding process are distinguished by conductance signals detected from graphene-molecule-graphene single-molecule junctions. In combination with theoretical calculations, the reciprocating and unidirectional motions in the tran ...[more]

PMID: 35233985

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Project description:A few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua, and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

Dataset Information

Stochastic Binding Dynamics of a Photoswitchable Single Supramolecular Complex.

Publications

Stochastic Binding Dynamics of a Photoswitchable Single Supramolecular Complex.

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