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Новости за 04.09.2019

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Deconvolving multiplexed protease signatures with substrate reduction and activity clustering

Plos.org 
En

by Qinwei Zhuang, Brandon Alexander Holt, Gabriel A. Kwong, Peng Qiu

Proteases are multifunctional, promiscuous enzymes that degrade proteins as well as peptides and drive important processes in health and disease. Current technology has enabled the construction of libraries of peptide substrates that detect protease activity, which provides valuable biological information. An ideal library would be orthogonal, such that each protease only hydrolyzes one unique substrate, however this is impractical due to off-target promiscuity (i.e.... Читать дальше...

Estimating information in time-varying signals

Plos.org 
En

by Sarah Anhala Cepeda-Humerez, Jakob Ruess, Gašper Tkačik

Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based... Читать дальше...

Executable pathway analysis using ensemble discrete-state modeling for large-scale data

Plos.org 
En

by Rohith Palli, Mukta G. Palshikar, Juilee Thakar

Pathway analysis is widely used to gain mechanistic insights from high-throughput omics data. However, most existing methods do not consider signal integration represented by pathway topology, resulting in enrichment of convergent pathways when downstream genes are modulated. Incorporation of signal flow and integration in pathway analysis could rank the pathways based on modulation in key regulatory genes. This implementation can be facilitated... Читать дальше...

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Plos.org 
En

by Alex X. Lu, Oren Z. Kraus, Sam Cooper, Alan M. Moses

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train... Читать дальше...



Changes in chromatin accessibility ensure robust cell cycle exit in terminally differentiated cells

Plos.org 
En

by Yiqin Ma, Daniel J. McKay, Laura Buttitta

During terminal differentiation, most cells exit the cell cycle and enter into a prolonged or permanent G0 in which they are refractory to mitogenic signals. Entry into G0 is usually initiated through the repression of cell cycle gene expression by formation of a transcriptional repressor complex called dimerization partner (DP), retinoblastoma (RB)-like, E2F and MuvB (DREAM). However, when DREAM repressive function is compromised during terminal differentiation... Читать дальше...


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