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Biomolecular nano-flow-sensor to measure near-surface flow.

ABSTRACT: We have proposed and experimentally demonstrated that the measurement of the near-surface flow at the interface between a liquid and solid using a 10 nm-sized biomolecular motor of F1-ATPase as a nano-flow-sensor. For this purpose, we developed a microfluidic test-bed chip to precisely control the liquid flow acting on the F1-ATPase. In order to visualize the rotation of F1-ATPase, several hundreds nanometer-sized particle was immobilized at the rotational axis of F1-ATPase to enhance the rotation to be detected by optical microscopy. The rotational motion of F1-ATPase, which was immobilized on an inner surface of the test-bed chip, was measured to obtain the correlation between the near-surface flow and the rotation speed of F1-ATPase. As a result, we obtained the relationship that the rotation speed of F1-ATPase was linearly decelerated with increasing flow velocity. The mechanism of the correlation between the rotation speed and the near-surface flow remains unclear, however the concept to use biomolecule as a nano-flow-sensor was proofed successfully.(See supplementary material 1).

SUBMITTER: Lee SW

PROVIDER: S-EPMC2894360 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Biomolecular nano-flow-sensor to measure near-surface flow.

Lee Sang-Wook SW Kinoshita Haruyuki H Noji Hiroyuki H Fujii Teruo T Yamamoto Takatoki T

Nanoscale research letters 20091114 2

We have proposed and experimentally demonstrated that the measurement of the near-surface flow at the interface between a liquid and solid using a 10 nm-sized biomolecular motor of F1-ATPase as a nano-flow-sensor. For this purpose, we developed a microfluidic test-bed chip to precisely control the liquid flow acting on the F1-ATPase. In order to visualize the rotation of F1-ATPase, several hundreds nanometer-sized particle was immobilized at the rotational axis of F1-ATPase to enhance the rotati ...[more]

PMID: 20672045

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Project description:This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high-order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high-order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high-order PDEs. As a consequence, the time integration of high-order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two-dimensional and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and a standard approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, that is, surface area, surface-enclosed volume, solvation free energy, and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform-based surface method, we solve the Poisson-Nernst-Planck equations with a PDE transform surface of a protein. Second-order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform-based surface generation method, we apply it to the construction of an excessively large biomolecule, a virus surface capsid. Virus surface morphologies of different resolutions are attained by adjusting the propagation time. Therefore, the present PDE transform provides a multiresolution analysis in the surface visualization. Extensive numerical experiment and comparison with an established surface model indicate that the present PDE transform is a robust, stable, and efficient approach for biomolecular surface generation in Cartesian meshes.

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Biomolecular nano-flow-sensor to measure near-surface flow.

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Biomolecular nano-flow-sensor to measure near-surface flow.

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