Search arXiv⌕ Search

arXiv · q-bio/0504002

Brush Effects on DNA Chips: Thermodynamics, Kinetics and Design Guidlines

Abstract

In biology experiments, oligonucleotide microarrays are contacted with a solution of long nucleic acid (NA) targets. The hybridized probes thus carry long tails. When the surface density of the oligonucleotide probes is high enough, the progress of hybridization leads to the formation of a polyelectrolyte brush due to mutual crowding of the NA tails. The free energy penalty associated with the brush modifies both the hybridization isotherms and the rate equations: the attainable hybridization is lowered significantly as is the hybridization rate. While the equilibrium hybridization fraction, $x_{eq}$, is low, the hybridization follows a Langmuir type isotherm, $x_{eq}/(1-x_{eq}) = c_t K$ where $c_t$ is the target concentration and $K$ is the equilibrium constant smaller than its bulk value by a factor $(n/N)^{2/5}$ due to wall effects where $n$ and $N$ denote the number of bases in the probe and the target. At higher $x_{eq}$, when the brush is formed, the leading correction is $x_{eq}/(1-x_{eq}) = c_t K \exp [ - const' (x_{eq}^{2/3} - x_B^{2/3})]$ where $x_B$ corresponds to the onset of the brush regime. The denaturation rate constant in the two regimes are identical. However, the hybridization rate constant in the brush regime is lower, the leading correction being $\exp [- const' (x^{2/3} - x_B^{2/3})]$.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. Halperin, A. Buhot, E. B. Zhulina. 2005-04-04. Brush Effects on DNA Chips: Thermodynamics, Kinetics and Design Guidlines. https://doi.org/10.1529/biophysj.105.063479

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

KEEP EXPLORING

Related papers

Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models

We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. For model tuning, Genome-Factory supports both full and parameter-efficient fine-tuning across diverse genomic models. For inference, Genome-Factory enables both embedding extraction and DNA sequence generation. For benchmarking, we include two existing benchmarks and provide a flexible interface to incorporate additional benchmarks. For interpretability, Genome-Factory introduces an open-source biological interpreter based on a sparse auto-encoder. We validate the utility of Genome-Factory across three dimensions: (i) Compatibility with diverse models and fine-tuning methods; (ii) Benchmarking downstream performance using two open-source benchmarks; (iii) Biological interpretation of learned representations with DNABERT-2. These results highlight its practical value for real-world genomic analysis. GitHub: https://github.com/WeiminWu2000/Genome_Factory.

q-bio.GN↗

Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement

Tissue graph counterfactuals ask what a cell would express if its spatial neighborhood were altered, a question central to predicting cell behavior in tissues. Existing methods, however, present only specific instances of such queries. In this work, we first formalize tissue graph counterfactuals as a class of spatial interventions that either rewire connections between cells (edge perturbation) or modify the expression of their neighbors (node perturbation). We then introduce Cellina, a framework that uses supervised disentanglement to decompose a cell's intrinsic state from its spatial context, using the latter as a conditioning input for in-silico perturbation predictions. Across benchmarks spanning over 2.5 million spatially-resolved cells in observational and interventional screens, Cellina outperforms both spatially-informed and non-spatial competitors in in-silico perturbations, while training as fast as non-spatial methods. Moreover, we show that Cellina reveals cancer subdomains with distinct signaling programs in an unsupervised manner and enables targeted neighbor perturbation simulations.

q-bio.GN↗

Panoramic modelling of single-cell transcriptomes at scale with CellNet

Comprehensively characterizing cellular landscapes to elucidate cell diversity is a central goal in biology. Recent advances in single-cell profiling have created unprecedented opportunities for this endeavor by capturing diverse transcriptomes with remarkable breadth. However, current foundation models generally struggle to leverage such holistic and valuable information to represent a cell. Here we present CellNet, which enables panoramic modelling of single-cell transcriptomes at scale by integrating DNA-derived gene identities and measured RNA expression into a shared image representation. Pretrained on 231 million image-based cell profiles across 40 species and covering over one million genes, CellNet improves the recognition of rare and condition-associated cellular states, enables the study of genes beyond experimentally measured panels, and uncovers associations between poorly characterized non-coding RNAs and known biological programmes. CellNet paves the way for AI-enabled cell-biology discovery at the whole-transcriptome scale. The CellNet website is available at https://www.cellnet.top.

q-bio.GN↗