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Cell Systems
This journal offers authors two options (open access or subscription) to publish research

Sep 27, 2017

Volume 5Issue 3p157-304
Open Archive
On the cover: A colony of mouse embryonic stem cells is stained for the transcription factors Oct4 (green) and Nanog (red) and nuclei (blue) and depicted three times for artistic purposes. These factors are stochastically expressed within the colony, indicating that the ES cell identity is highly dynamic. Using tools from statistical mechanics and computational methods developed by Chan et al. (pp. 251–267), Stumpf et al. (pp. 268–282) find differentiation is well described as a stochastic process with “memory.”...
On the cover: A colony of mouse embryonic stem cells is stained for the transcription factors Oct4 (green) and Nanog (red) and nuclei (blue) and depicted three times for artistic purposes. These factors are stochastically expressed within the colony, indicating that the ES cell identity is highly dynamic. Using tools from statistical mechanics and computational methods developed by Chan et al. (pp. 251–267), Stumpf et al. (pp. 268–282) find differentiation is well described as a stochastic process with “memory.”

Editorial

  • Harnessing MOOCs for the Practice of Science

    • H. Craig Mak
    In 2016, 58 million people worldwide signed up to take a MOOC (massively open online class), up from 35 million the previous year according to Class Central, an online catalog for MOOCs. Among the most popular offerings—termed micromasters, nanodegrees, certifications, and specializations—are collections of new courses that provide practical, narrowly focused curricula relevant to specific lines of work. Often, these have been developed in partnership with companies and promise career advancement or a smoother path to employment upon completion.

Cell Systems Call

  • Principles of Systems Biology, No. 21

    This month: relating single cells to populations (Cao/Packer, Wu/Altschuler, O’Brien, Friedman), an excess of ribosomes (Barkai), human pathology atlas (Uhlen), signatures of feedback (Rahi), and major genome redesign (Baumgart).

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Commentary

  • Guidelines for Developing Successful Short Advanced Courses in Systems Medicine and Systems Biology

    • David Gomez-Cabrero,
    • Francesco Marabita,
    • Sonia Tarazona,
    • Isaac Cano,
    • Josep Roca,
    • Ana Conesa,
    • Philippe Sabatier,
    • Jesper Tegnér
    Systems medicine and systems biology have inherent educational challenges. These have largely been addressed either by providing new masters programs or by redesigning undergraduate programs. In contrast, short courses can respond to a different need: they can provide condensed updates for professionals across academia, the clinic, and industry. These courses have received less attention. Here, we share our experiences in developing and providing such courses to current and future leaders in systems biology and systems medicine. We present guidelines for how to reproduce our courses, and we offer suggestions for how to select students who will nurture an interdisciplinary learning environment and thrive there.

Focus on RECOMB

  • Algorithmic Advances and Applications from RECOMB 2017

    This Cell Systems Call features invited summaries of 32 of the 38 papers accepted for presentation in the “regular track” at the 2017 Research in Computational Molecular Biology conference. The editors have categorized the summaries into cancer genomics, genetics, mass spectrometry, metagenomics, network analysis, phylogenetics, sequence annotation, sequence informatics, single-cell data analysis, and structural biology.
  • Identification of Human Lineage-Specific Transcriptional Coregulators Enabled by a Glossary of Binding Modules and Tunable Genomic Backgrounds

    • Luca Mariani,
    • Kathryn Weinand,
    • Anastasia Vedenko,
    • Luis A. Barrera,
    • Martha L. Bulyk
    Motif enrichment analysis of ChIP-seq data can elucidate the molecular mechanisms by which transcription factors regulate gene expression in a tissue-specific manner. In the current study, we created a glossary of the intrinsic DNA binding specificity of ∼40% of human TFs, developed a method to construct matched genomic background sequences, and showed that the combination of these two tools improves the identification of TF binding modes within regulatory regions.
  • Folding Membrane Proteins by Deep Transfer Learning

    • Sheng Wang,
    • Zhen Li,
    • Yizhou Yu,
    • Jinbo Xu
    A deep transfer learning method is presented to predict membrane protein contact map by learning sequence-structure relationships from non-membrane proteins, which overcomes the challenge that there are not many solved membrane protein structures for deep learning model training. The predicted contacts are pretty accurate and can help predict correct folds and accurate 3D models for ∼40% and ∼20% of 510 non-redundant membrane proteins, respectively.
  • Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning

    • Sai Zhang,
    • Hailin Hu,
    • Jingtian Zhou,
    • Xuan He,
    • Tao Jiang,
    • Jianyang Zeng
    ROSE provides a deep learning-based framework for estimating the likelihood of ribosome stalling from the mRNA sequence. Cotranslational events and regulatory factors related to ribosome stalling can be deciphered by ROSE trained on ribosome profiling data. ROSE will facilitate further studies on mRNA translation and protein biogenesis.
  • Network-Based Coverage of Mutational Profiles Reveals Cancer Genes

    • Borislav H. Hristov,
    • Mona Singh
    Cancer-relevant genes, including those rarely mutated across samples, can be effectively identified by considering per-individual mutational profiles in the context of interaction networks and uncovering small connected subnetworks of genes, presumably participating in shared processes, that together are altered across (i.e., “cover”) a large fraction of individuals.
  • Optimized Sequence Library Design for Efficient In Vitro Interaction Mapping

    • Yaron Orenstein,
    • Robert Puccinelli,
    • Ryan Kim,
    • Polly Fordyce,
    • Bonnie Berger
    We present a new compact sequence design that covers all k-mers utilizing joker characters and develop an efficient algorithm to generate such designs. We show through simulations and experimental validation that these sequence designs are useful for identifying high-affinity binding sites at significantly reduced cost and space.

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