Dataset Information

Modeling the Device Behavior of Biological and Synthetic Nanopores with Reduced Models.

ABSTRACT: Biological ion channels and synthetic nanopores are responsible for passive transport of ions through a membrane between two compartments. Modeling these ionic currents is especially amenable to reduced models because the device functions of these pores, the relation of input parameters (e.g., applied voltage, bath concentrations) and output parameters (e.g., current, rectification, selectivity), are well defined. Reduced models focus on the physics that produces the device functions (i.e., the physics of how inputs become outputs) rather than the atomic/molecular-scale physics inside the pore. Here, we propose four rules of thumb for constructing good reduced models of ion channels and nanopores. They are about (1) the importance of the axial concentration profiles, (2) the importance of the pore charges, (3) choosing the right explicit degrees of freedom, and (4) creating the proper response functions. We provide examples for how each rule of thumb helps in creating a reduced model of device behavior.

SUBMITTER: Boda D

PROVIDER: S-EPMC7711659 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling the Device Behavior of Biological and Synthetic Nanopores with Reduced Models.

Boda Dezső D Valiskó Mónika M Gillespie Dirk D

Entropy (Basel, Switzerland) 20201105 11

Biological ion channels and synthetic nanopores are responsible for passive transport of ions through a membrane between two compartments. Modeling these ionic currents is especially amenable to reduced models because the device functions of these pores, the relation of input parameters (e.g., applied voltage, bath concentrations) and output parameters (e.g., current, rectification, selectivity), are well defined. Reduced models focus on the physics that produces the device functions (i.e., the ...[more]

PMID: 33287027

Similar Datasets

Project description:Purpose: The goals of this study were to establish in vivo models that recapitulated the heterogeneous behavior of osteosarcoma and to develop an approach to distinguish genes expressed from tumor cells apart from those expressed from host cells in next generation sequencing (NGS) data from xenograft tumors. Methods: We used cell lines derived from two spontaneous canine osteosarcoma tumors with distinctly different biological behaviors for heterotypic in vivo modeling. NGS was used to compare the transcriptome profiles of the xenograft tumors to the transcriptome profiles of the parental cell lines and to study the tumor microenvironment of the xenograft tumors. Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to a HISAT2 index for mapping which was built from a multi-sequence fasta file containing both the canine (canFam3) and murine (mm10) genomes with the HISAT2 aligner (2.0.2-beta) using default parameters. Rsubread featureCounts (1.4.6) was used to estimate transcript abundance using default parameters. The Bioconductor package ‘edgeR’ (version 3.12.1) was used for differential gene expression analysis and Partek Genomic Suite (version 6.6) was used for clustering and visualization. Qiagen's Ingenuity Pathway Analysis application (version 01-04) and Thomson Reuter's MetaCore software suite were used for pathway and upstream regulator analysis with differentially expressed genes. Results: We document that orthotopic canine osteosarcoma xenografts preserve the biological, molecular, and heterotypic biology observed in the tumors from which they were derived. The data also provide insight into the tumor-host interactions and identify targets that could play a role in treatment strategies. Conclusions: Our study represents the first detailed analysis of xenograft transcriptomes with an approach that allows for accurate examination of tumor-intrinsic properties, as well as organotypic tumor-stromal interaction that may influence tumor progression.

Project description:The effect of capillary pressure on the vapor–liquid two-phase equilibrium calculation has been extensively studied for the past two decades. However, the calculation accuracy is often weakened by the false assumptions and inherent flaws present in the modeling process. In this work, a modified Young–Laplace equation proposed by Tan and Piri [Tan, S.; Piri, M. Equation-of-State Modeling of Confined-Fluid Phase Equilibria in Nanopores. Fluid Phase Equilibr. 2015,393, 48–63.] is coupled with volume-translated Peng–Robinson equation of state to study the effect of capillary pressure on the two-phase equilibrium calculation in confined nanopores. In order to successfully apply the modified Young–Laplace equation during the vapor–liquid equilibrium calculation process, this study models the tuning parameter ? in the modified Young–Laplace equation (as proposed by Tan and Piri for perturbed-chain statistical associating fluid theory equation of state) for several pure hydrocarbons and their mixtures by matching experimental data collected from the literature. The tuning parameter ? can be expressed as a unique function for each pure substance or mixture. It is found that the tuning parameter ? shows a quadratic polynomial relationship with temperature, and the value of ? is always less than one. The ? can become negative under certain circumstances, which adjusts the capillary pressure to a lower value. It increases with an increasing pore radius; this is different from the results obtained by Tan and Piri which showed that the tuning parameter ? decreases with an increasing pore radius. The above rules apply to the tuning parameter ? obtained for both pure substances and mixtures. Using the two-phase equilibrium calculation coupled with the modified Young–Laplace equation, the calculated vapor pressures for pure substances and two-phase boundaries for mixtures match very well with the experimental data. Implementation of the modified Young–Laplace equation greatly improves the accuracy of the two-phase equilibrium calculation considering the capillarity effect. Such a modeling strategy could be integrated into a reservoir simulator to conduct more accurate flow simulations for tight/shale reservoirs.

Dataset Information

Modeling the Device Behavior of Biological and Synthetic Nanopores with Reduced Models.

Publications

Modeling the Device Behavior of Biological and Synthetic Nanopores with Reduced Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets