PubMed Health⌕ Search

PubMed · 14513560

Mobility-shift analysis with microfluidics chips.

Abstract

Electrophoretic mobility shift analysis (EMSA) is a well-characterized and widely used technique for the analysis of proten-DNA interaction and the analysis of transcription factor combinatorics. Currently implemented EMSA generally involves the time-consuming use of radiolabeled DNA and polyacrylamide gel electrophoresis. We are studying the bionanoscience of self-assembling supramolecular protein-nucleic nanostructures. We have undertaken these studies because they promise to enhance our understanding of assemblies formed during prebiotic evolution, provide tools for analysis of biological processes like DNA recombination, and may lead to the development of nanoscale biosensors designed for site-specific molecular targeting. During the course of that work, we noted that EMSA of these complex structures could be effectively implemented with microfluidics chips designed for the separation of DNA fragments. In this report we compare the two techniques and demonstrate that the microfluidics system is also capable of resolving complex mixtures produced by decorating DNA recombination intermediates with mixtures of DNA binding proteins. Moreover, the microfluidics chip system improves EMSA by permitting analysis with smaller samples, avoiding the use of radiolabeling, and reducing the time involved to a matter of minutes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jarrod Clark, Taras Shevchuk, Piotr M Swiderski, Rajesh Dabur, Laura E Crocitto, Yaroslav I Buryanov, Steven S Smith. 2003. Mobility-shift analysis with microfluidics chips.. https://doi.org/10.2144/03353rr01

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

KEEP EXPLORING

Related citations

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↗

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↗

Robust estimation in mixed linear models with non-monotone missingness.

We introduce a model to account for abrupt changes among repeated measures with non-monotone missingness. Development of likelihood inferences for such models is hard because it involves intractable integration to obtain the marginal likelihood. We use hierarchical likelihood to overcome such difficulty. Abrupt changes among repeated measures can be well described by introducing random effects in the dispersion. A simulation study shows that the resulting estimator is efficient, robust against misspecification of fatness of tails. For illustration we use a schizophrenic behaviour data presented by Rubin and Wu.

Computer Simulation↗