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Analysis of gene expression data remains one of the most promising avenues toward reconstructing genome-wide gene regulatory networks. However, the large dimensionality of the problem prohibits the fitting of explicit dynamical models of gene regulatory networks, whereas machine learning methods for dimensionality reduction such as clustering or principal component analysis typically fail to provide...
The generation of dynamic models of biological processes critically depends on the determination of precise cellular concentrations of biomolecules. Measurements of system-wide absolute protein levels are particularly valuable information in systems biology. Recently, mass spectrometry based proteomics approaches have been developed to estimate protein concentrations on a proteome-wide scale. However,...
During gene expression, protein-coding transcripts are shaped by multiple processing events: 5′ end capping, pre-mRNA splicing, RNA editing, and 3′ end cleavage and polyadenylation. These events are required to produce mature mRNA, which can be subsequently translated. Nearly all of these RNA processing steps occur during transcription, while the nascent RNA is still attached to the DNA template by...
Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conversion of RNA to cDNA prior to signal amplification and transcript quantification. Thus, its efficiency in transcript detection is unmatched by other methods...
Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify...
The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering...
Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction...
We quantify the strength of miRNA–target interactions with MIRZA, a recently introduced biophysical model. We show that computationally predicted energies of interaction correlate strongly with the energies of interaction estimated from biochemical measurements of Michaelis–Menten constants. We further show that the accuracy of the MIRZA model can be improved taking into account recently emerged experimental...
Gene expression profiling provides a tool to analyze the internal states of cells or organisms, and their responses to perturbations. While global measurements of mRNA levels have thus been widely used for many years, it is only through the recent development of the ribosome profiling technique that an analogous examination of global mRNA translation programs has become possible. Ribosome profiling...
The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can...
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