Dataset Information

The nanomaterial toolkit for neuroengineering.

ABSTRACT: There is a growing interest in developing effective tools to better probe the central nervous system (CNS), to understand how it works and to treat neural diseases, injuries and cancer. The intrinsic complexity of the CNS has made this a challenging task for decades. Yet, with the extraordinary recent advances in nanotechnology and nanoscience, there is a general consensus on the immense value and potential of nanoscale tools for engineering neural systems. In this review, an overview of specialized nanomaterials which have proven to be the most effective tools in neuroscience is provided. After a brief background on the prominent challenges in the field, a variety of organic and inorganic-based nanomaterials are described, with particular emphasis on the distinctive properties that make them versatile and highly suitable in the context of the CNS. Building on this robust nano-inspired foundation, the rational design and application of nanomaterials can enable the generation of new methodologies to greatly advance the neuroscience frontier.

SUBMITTER: Shah S

PROVIDER: S-EPMC5271150 | biostudies-other | 2016

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

The nanomaterial toolkit for neuroengineering.

Shah Shreyas S

Nano convergence 20161020 1

There is a growing interest in developing effective tools to better probe the central nervous system (CNS), to understand how it works and to treat neural diseases, injuries and cancer. The intrinsic complexity of the CNS has made this a challenging task for decades. Yet, with the extraordinary recent advances in nanotechnology and nanoscience, there is a general consensus on the immense value and potential of nanoscale tools for engineering neural systems. In this review, an overview of special ...[more]

PMID: 28191435

Similar Datasets

Project description:Molecular modeling and simulation are invaluable tools for nanoscience that predict mechanical, physicochemical, and thermodynamic properties of nanomaterials and provide molecular-level insight into underlying mechanisms. However, building nanomaterial-containing systems remains challenging due to the lack of reliable and integrated cyberinfrastructures. Here we present Nanomaterial Modeler in CHARMM-GUI, a web-based cyberinfrastructure that provides an automated process to generate various nanomaterial models, associated topologies, and configuration files to perform state-of-the-art molecular dynamics simulations using most simulation packages. The nanomaterial models are based on the interface force field, one of the most reliable force fields (FFs). The transferability of nanomaterial models among the simulation programs was assessed by single-point energy calculations, which yielded 0.01% relative absolute energy differences for various surface models and equilibrium nanoparticle shapes. Three widely used Lennard-Jones (LJ) cutoff methods are employed to evaluate the compatibility of nanomaterial models with respect to conventional biomolecular FFs: simple truncation at r = 12 Å (12 cutoff), force-based switching over 10 to 12 Å (10-12 fsw), and LJ particle mesh Ewald with no cutoff (LJPME). The FF parameters with these LJ cutoff methods are extensively validated by reproducing structural, interfacial, and mechanical properties. We find that the computed density and surface energies are in good agreement with reported experimental results, although the simulation results increase in the following order: 10-12 fsw <12 cutoff < LJPME. Nanomaterials in which LJ interactions are a major component show relatively higher deviations (up to 4% in density and 8% in surface energy differences) compared with the experiment. Nanomaterial Modeler's capability is also demonstrated by generating complex systems of nanomaterial-biomolecule and nanomaterial-polymer interfaces with a combination of existing CHARMM-GUI modules. We hope that Nanomaterial Modeler can be used to carry out innovative nanomaterial modeling and simulations to acquire insight into the structure, dynamics, and underlying mechanisms of complex nanomaterial-containing systems.

Dataset Information

The nanomaterial toolkit for neuroengineering.

Publications

The nanomaterial toolkit for neuroengineering.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets