Solid-supported lipid multilayers: structure factor and fluctuations.

D Constantin; U Mennicke; C Li; T Salditt

doi:10.1140/epje/i2003-10063-1

Home/Archive/PubMed

PubMed · 15007664

Solid-supported lipid multilayers: structure factor and fluctuations.

D Constantin·U Mennicke·C Li·T Salditt

View original source ↗Export metadata ↓

Abstract

We present a theoretical description of the thermal fluctuations in a solid-supported stack of lipid bilayers, for the case of vanishing surface tension gamma = 0 and in the framework of continuous smectic elasticity. The model is successfully used to model the reflectivity profile of a thin (16 bilayers) DMPC sample under applied osmotic pressure and the diffuse scattering from a thick (800 bilayers) stack. We compare our model to previously existing theories.

Explore related subjects

Computer Simulation Dimyristoylphosphatidylcholine Elasticity Lipid Bilayers Macromolecular Substances Membrane Fluidity Membranes, Artificial Models, Molecular Molecular Conformation Reproducibility of Results Sensitivity and Specificity Surface Tension Temperature X-Ray Diffraction Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

D Constantin, U Mennicke, C Li, T Salditt. 2003. Solid-supported lipid multilayers: structure factor and fluctuations.. https://doi.org/10.1140/epje%2Fi2003-10063-1

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↗

PubMed2005

Non-parametric paired two-sample tests for censored survival data incorporating longitudinal covariate information.

In this manuscript, we present non-parametric two-sample tests for paired censored survival data incorporating longitudinal covariate information. These tests take advantage of information collected at baseline and post-baseline to provide efficiency gains when censoring is uninformative. Additionally, these methods adjust for potential bias from informative censoring that is captured by the baseline and longitudinal covariates. Finite sample properties are investigated with simulation, and we illustrate methodology with an example from the Early Treatment Diabetic Retinopathy Study.

Computer Simulation↗