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Transcriptional regulatory network inference methods have been studied for years. Most of them relie on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this…

基因组学 · 定量生物学 2012-08-03 Jianlong Qi , Tom Michoel

Evolutionary trajectories and phenotypic states available to cell populations are ultimately dictated by intermolecular interactions between DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in…

种群与进化 · 定量生物学 2014-08-19 Allan Haldane , Michael Manhart , Alexandre V. Morozov

Transcription factor binding to the surface of DNA regulatory regions is one of the primary causes of regulating gene expression levels. A probabilistic approach to model protein-DNA interactions at the sequence level is through Position…

生物物理 · 物理学 2015-11-18 Jacob Clifford , Christoph Adami

Transcription factor proteins bind specific DNA sequences to control the expression of genes. They contain DNA binding domains which belong to several super-families, each with a specific mechanism of DNA binding. The total number of…

生物大分子 · 定量生物学 2010-07-27 Shalev Itzkovitz , Tsvi Tlusty , Uri Alon

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Due to the limitation of the previous database, especially…

生物大分子 · 定量生物学 2021-11-24 Junkang Wei , Siyuan Chen , Licheng Zong , Xin Gao , Yu Li

The speed of site-specific binding of transcription factor (TFs) proteins with genomic DNA seems to be strongly retarded by the randomly occurring sequence traps. Traps are those DNA sequences sharing significant similarity with the…

亚细胞过程 · 定量生物学 2016-07-22 G. Niranjani , R. Murugan

Transcription factors (TFs) are key regulators of gene expression. Based on the classical scenario in which the TF search process switches between one-dimensional motion along the DNA molecule and free Brownian motion in the nucleus, we…

基因组学 · 定量生物学 2010-10-15 Godefroy Malherbe , David Holcman

Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions…

机器学习 · 计算机科学 2016-11-17 Ritambhara Singh , Jack Lanchantin , Gabriel Robins , Yanjun Qi

The regulation of a gene depends on the binding of transcription factors to specific sites located in the regulatory region of the gene. The generation of these binding sites and of cooperativity between them are essential building blocks…

统计力学 · 物理学 2007-05-23 Johannes Berg , Stana Willmann , Michael Lässig

The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules. However,…

生物大分子 · 定量生物学 2022-11-15 Boyuan Liu

Specific binding of proteins to DNA is one of the most common ways in which gene expression is controlled. Although general rules for the DNA-protein recognition can be derived, the ambiguous and complex nature of this mechanism precludes a…

生物大分子 · 定量生物学 2007-12-17 E. Moroni , M. Caselle , F. Fogolari

Transcription factors (TFs) are regulatory proteins that bind DNA in promoter regions of the genome and either promote or repress gene expression. Here we predict analytically that enhanced homo-oligonucleotide sequence correlations, such…

生物大分子 · 定量生物学 2011-11-15 Itamar Sela , David B. Lukatsky

Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing…

生物大分子 · 定量生物学 2023-06-29 Yufan Liu , Boxue Tian

Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various pathways. Functional interactions between proteins result in…

生物物理 · 物理学 2016-11-21 Anne-Florence Bitbol , Robert S. Dwyer , Lucy J. Colwell , Ned S. Wingreen

Multi-sample microarray experiments have become a standard experimental method for studying biological systems. A frequent goal in such studies is to unravel the regulatory relationships between genes. During the last few years, regression…

应用统计 · 统计学 2008-12-18 Nancy R. Zhang , Mary C. Wildermuth , Terence P. Speed

Cooperativity plays an important role in the action of proteins bound to DNA. A simple, mechanical mechanism for cooperativity, in the form of a tension-mediated interaction between proteins bound to DNA at two different locations is…

软凝聚态物质 · 物理学 2009-10-31 Joseph Rudnick , Robijn Bruinsma

The problem of detecting a binding site -- a substring of DNA where transcription factors attach -- on a long DNA sequence requires the recognition of a small pattern in a large background. For short binding sites, the matching probability…

基因组学 · 定量生物学 2009-11-13 Daniela Bianchi , Brunello Tirozzi

The accurate prediction of protein-RNA binding affinity remains an unsolved problem in structural biology, limiting opportunities in understanding gene regulation and designing RNA-targeting therapeutics. A central obstacle is the…

Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to…

生物大分子 · 定量生物学 2025-01-06 Rong Han , Xiaohong Liu , Tong Pan , Jing Xu , Xiaoyu Wang , Wuyang Lan , Zhenyu Li , Zixuan Wang , Jiangning Song , Guangyu Wang , Ting Chen

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the…

量子物理 · 物理学 2018-03-02 Richard Y. Li , Rosa Di Felice , Remo Rohs , Daniel A. Lidar