PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15894231

In silico strategies for modeling membrane transporter function.

Cheng Chang·Abhijit Ray·Peter Swaan
View original source ↗Export metadata ↓

Abstract

Transporter proteins facilitate the transfer of solutes across the cell membrane and have an intricate role in drug absorption, distribution and excretion. Because of their substrate promiscuity, several transporters represent viable pharmacological targets for enhancing drug absorption, preventing drug toxicity or facilitating localized tissue delivery. However, the slow emergence of high-resolution structures for these proteins has hampered the intelligent design of transporter substrates. Nonetheless, currently available functional, as well as structural, data provide an attractive scaffold for generating fusion models that merge substrate-based SARs and protein-based homology structures. The resultant models offer features that extend single modality paradigms in predictive function.

Explore related subjects

Computer SimulationDrug DesignHumansLigandsMembrane Transport ProteinsModels, MolecularQuantitative Structure-Activity RelationshipJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cheng Chang, Abhijit Ray, Peter Swaan. 2005-05-01. In silico strategies for modeling membrane transporter function.. https://doi.org/10.1016/s1359-6446(05)03429-x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗
PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗
PubMed2007

Degradation of supercoiled plasmid DNA within a capillary device.

Supercoiled plasmid DNA is susceptible to fluid stress in large-scale manufacturing processes. A capillary device was used to generate controlled shear conditions and the effects of different stresses on plasmid DNA structure were investigated. Computational fluid dynamics (CFD) analysis was employed to characterize the flow environment in the capillary device and different analytical techniques were used to quantify the DNA breakage. It was found that the degradation of plasmid DNA occurred at the entrance of the capillary and that the shear stress within the capillary did not affect the DNA structure. The degradation rate of plasmids was well correlated with the average elongational strain rate or the pressure drop at the entrance region. The conclusion may also be drawn that laminar shear stress does not play a significant role in plasmid DNA degradation.

Computer Simulation↗