The Experts below are selected from a list of 237429 Experts worldwide ranked by ideXlab platform
Shoji Takada - One of the best experts on this subject based on the ideXlab platform.
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particle Filter Method to integrate high speed atomic force microscopy measurements with biomolecular simulations
Journal of Chemical Theory and Computation, 2020Co-Authors: Sotaro Fuchigami, Toru Niina, Shoji TakadaAbstract:High-speed atomic force microscopy (HS-AFM) can be used to observe the structural dynamics of biomolecules at the single-molecule level in real time under near-physiological conditions; however, its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions, albeit with some artifacts. Here, to integrate HS-AFM data and coarse-grained molecular dynamics (CG-MD) simulations, we develop a particle Filter Method that implements a sequential Bayesian data assimilation approach. We test the Method in a twin experiment. First, we generate a reference HS-AFM movie from the CG-MD trajectory of a test molecule, a nucleosome; this serves as the "experimental measurement". Then, we perform a particle Filter simulation with 512 particles, which captures the large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing particle Filter simulations with 8-8192 particles, we find that using greater numbers of particles consistently increases the likelihood of the whole AFM movie. By comparing the likelihoods for different ionic concentrations and time scale mappings, we find that the "true" concentration and time scale mapping can be inferred as the largest likelihood of the whole AFM movie but not that of each AFM image. The particle Filter Method provides a general approach for integrating HS-AFM data with MD simulations.
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the particle Filter Method to integrate high speed atomic force microscopy measurement with biomolecular simulations
bioRxiv, 2020Co-Authors: Sotaro Fuchigami, Toru Niina, Shoji TakadaAbstract:The high-speed atomic force microscopy (HS-AFM) can observe structural dynamics of biomolecules at single-molecule level in real time near physiological condition, but its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions albeit with some artifact. Here, in order to integrate the HS-AFM data and coarse-grained (CG)-MD simulations, we develop a particle Filter Method, one of the sequential Bayesian data assimilation approaches. We tested the Method in a twin experiment. We first made a reference HS-AFM movie from a CG-MD trajectory of a test molecule, a nucleosome, which serves as an "experimental measurement". Then, we performed the particle Filter simulation with 512 particles that captured large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing the particle Filter simulations with 8 - 8192 particles, we found that the use of more particles consistently results in larger likelihood for the whole AFM movie. By comparing the likelihoods from different ionic concentrations and from different timescales, we found that the "true" concentration and timescale can be inferred as the largest likelihood of the whole AFM movie, but not that of each AFM image. The particle Filter Method provides a general approach to integrate the HS-AFM data with MD simulations.
Sotaro Fuchigami - One of the best experts on this subject based on the ideXlab platform.
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particle Filter Method to integrate high speed atomic force microscopy measurements with biomolecular simulations
Journal of Chemical Theory and Computation, 2020Co-Authors: Sotaro Fuchigami, Toru Niina, Shoji TakadaAbstract:High-speed atomic force microscopy (HS-AFM) can be used to observe the structural dynamics of biomolecules at the single-molecule level in real time under near-physiological conditions; however, its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions, albeit with some artifacts. Here, to integrate HS-AFM data and coarse-grained molecular dynamics (CG-MD) simulations, we develop a particle Filter Method that implements a sequential Bayesian data assimilation approach. We test the Method in a twin experiment. First, we generate a reference HS-AFM movie from the CG-MD trajectory of a test molecule, a nucleosome; this serves as the "experimental measurement". Then, we perform a particle Filter simulation with 512 particles, which captures the large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing particle Filter simulations with 8-8192 particles, we find that using greater numbers of particles consistently increases the likelihood of the whole AFM movie. By comparing the likelihoods for different ionic concentrations and time scale mappings, we find that the "true" concentration and time scale mapping can be inferred as the largest likelihood of the whole AFM movie but not that of each AFM image. The particle Filter Method provides a general approach for integrating HS-AFM data with MD simulations.
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the particle Filter Method to integrate high speed atomic force microscopy measurement with biomolecular simulations
bioRxiv, 2020Co-Authors: Sotaro Fuchigami, Toru Niina, Shoji TakadaAbstract:The high-speed atomic force microscopy (HS-AFM) can observe structural dynamics of biomolecules at single-molecule level in real time near physiological condition, but its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions albeit with some artifact. Here, in order to integrate the HS-AFM data and coarse-grained (CG)-MD simulations, we develop a particle Filter Method, one of the sequential Bayesian data assimilation approaches. We tested the Method in a twin experiment. We first made a reference HS-AFM movie from a CG-MD trajectory of a test molecule, a nucleosome, which serves as an "experimental measurement". Then, we performed the particle Filter simulation with 512 particles that captured large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing the particle Filter simulations with 8 - 8192 particles, we found that the use of more particles consistently results in larger likelihood for the whole AFM movie. By comparing the likelihoods from different ionic concentrations and from different timescales, we found that the "true" concentration and timescale can be inferred as the largest likelihood of the whole AFM movie, but not that of each AFM image. The particle Filter Method provides a general approach to integrate the HS-AFM data with MD simulations.
Lorenz T. Biegler - One of the best experts on this subject based on the ideXlab platform.
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a trust region Filter Method for glass box black box optimization
Aiche Journal, 2016Co-Authors: John P Eason, Lorenz T. BieglerAbstract:Modern nonlinear programming solvers can be utilized to solve very large scale problems in chemical engineering. However, these Methods require fully open models with accurate derivatives. In this article, we address the hybrid glass box/black box optimization problem, in which part of a system is modeled with open, equation based models and part is black box. When equation based reduced models are used in place of the black box, NLP solvers may be applied directly but an accurate solution is not guaranteed. In this work, a trust region Filter algorithm for glass box/black box optimization is presented. By combining concepts from trust region Filter Methods and derivative free optimization, the Method guarantees convergence to first-order critical points of the original glass box/black box problem. The algorithm is demonstrated on three comprehensive examples in chemical process optimization. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3124–3136, 2016
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line search Filter Methods for nonlinear programming local convergence
Siam Journal on Optimization, 2005Co-Authors: Andreas Wachter, Lorenz T. BieglerAbstract:A line search Method is proposed for nonlinear programming using Fletcher and Leyffer's Filter Method, which replaces the traditional merit function. A simple modification of the Method proposed in a companion paper [SIAM J. Optim., 16 (2005), pp. 1--31] introducing second order correction steps is presented. It is shown that the proposed Method does not suffer from the Maratos effect, so that fast local convergence to second order sufficient local solutions is achieved.
Toru Niina - One of the best experts on this subject based on the ideXlab platform.
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particle Filter Method to integrate high speed atomic force microscopy measurements with biomolecular simulations
Journal of Chemical Theory and Computation, 2020Co-Authors: Sotaro Fuchigami, Toru Niina, Shoji TakadaAbstract:High-speed atomic force microscopy (HS-AFM) can be used to observe the structural dynamics of biomolecules at the single-molecule level in real time under near-physiological conditions; however, its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions, albeit with some artifacts. Here, to integrate HS-AFM data and coarse-grained molecular dynamics (CG-MD) simulations, we develop a particle Filter Method that implements a sequential Bayesian data assimilation approach. We test the Method in a twin experiment. First, we generate a reference HS-AFM movie from the CG-MD trajectory of a test molecule, a nucleosome; this serves as the "experimental measurement". Then, we perform a particle Filter simulation with 512 particles, which captures the large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing particle Filter simulations with 8-8192 particles, we find that using greater numbers of particles consistently increases the likelihood of the whole AFM movie. By comparing the likelihoods for different ionic concentrations and time scale mappings, we find that the "true" concentration and time scale mapping can be inferred as the largest likelihood of the whole AFM movie but not that of each AFM image. The particle Filter Method provides a general approach for integrating HS-AFM data with MD simulations.
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the particle Filter Method to integrate high speed atomic force microscopy measurement with biomolecular simulations
bioRxiv, 2020Co-Authors: Sotaro Fuchigami, Toru Niina, Shoji TakadaAbstract:The high-speed atomic force microscopy (HS-AFM) can observe structural dynamics of biomolecules at single-molecule level in real time near physiological condition, but its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions albeit with some artifact. Here, in order to integrate the HS-AFM data and coarse-grained (CG)-MD simulations, we develop a particle Filter Method, one of the sequential Bayesian data assimilation approaches. We tested the Method in a twin experiment. We first made a reference HS-AFM movie from a CG-MD trajectory of a test molecule, a nucleosome, which serves as an "experimental measurement". Then, we performed the particle Filter simulation with 512 particles that captured large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing the particle Filter simulations with 8 - 8192 particles, we found that the use of more particles consistently results in larger likelihood for the whole AFM movie. By comparing the likelihoods from different ionic concentrations and from different timescales, we found that the "true" concentration and timescale can be inferred as the largest likelihood of the whole AFM movie, but not that of each AFM image. The particle Filter Method provides a general approach to integrate the HS-AFM data with MD simulations.
Chao Gu - One of the best experts on this subject based on the ideXlab platform.
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a secant algorithm with line search Filter Method for nonlinear optimization
Applied Mathematical Modelling, 2011Co-Authors: Chao GuAbstract:Filter Methods were initially designed for nonlinear programming problems by Fletcher and Leyffer. In this paper we propose a secant algorithm with line search Filter Method for nonlinear equality constrained optimization. The algorithm yields the global convergence under some reasonable conditions. By using the Lagrangian function value in the Filter we establish that the proposed algorithm can overcome the Maratos effect without using second order correction step, so that fast local superlinear convergence to second order sufficient local solution is achieved. The primary numerical results are presented to confirm the robustness and efficiency of our approach.