ArticleMolecular biology of the cell2014
Modularity and predictability in cell signaling and decision making.
Article in Molecular biology of the cell, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed.
- DNA damage response inhibition is an early event in cadmium-induced breast carcinogenesis.Toxicology and applied pharmacology · 2025Article
- Reframing macrophage diversity with network motifs.Trends in immunology · 2023Review
- Dynamics and Sensitivity of Signaling Pathways.Current pathobiology reports · 2022Article
- A modular approach for modeling the cell cycle based on functional response curves.PLoS computational biology · 2021Article
- Regulation and dynamics of force transmission at individual cell-matrix adhesion bonds.Science advances · 2020Article
- Parallel Tempering with Lasso for model reduction in systems biology.PLoS computational biology · 2020Article
- Empowering statistical methods for cellular and molecular biologists.Molecular biology of the cell · 2019Article
- Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response.PLoS computational biology · 2018Article
- A Precise Cdk Activity Threshold Determines Passage through the Restriction Point.Molecular cell · 2018Article
- Non-Lytic Egression of Infectious Bursal Disease Virus (IBDV) Particles from Infected Cells.PloS one · 2017Article
- The significance of the 1-kinase/1-phosphatase activities of the PPIP5K family.Advances in biological regulation · 2017Review
- Positive Feedback Keeps Duration of Mitosis Temporally Insulated from Upstream Cell-Cycle Events.Molecular cell · 2016Article
- Switch-like Transitions Insulate Network Motifs to Modularize Biological Networks.Cell systems · 2016Article
- Half dozen of one, six billion of the other: What can small- and large-scale molecular systems biology learn from one another?Genome research · 2015Article
- Review
- From Structural Variation of Gene Molecules to Chromatin Dynamics and Transcriptional Bursting.Genes · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cells make decisions to differentiate, divide, or apoptose based on multiple signals of internal and external origin. These decisions are discrete outputs from dynamic networks comprised of signaling pathways. Yet the validity of this decomposition of regulatory proteins into distinct pathways is unclear because many regulatory proteins are pleiotropic and interact through cross-talk with components of other pathways. In addition to the deterministic complexity of interconnected networks, there is stochastic complexity arising from the fluctuations in concentrations of regulatory molecules. Even within a genetically identical population of cells grown in the same environment, cell-to-cell variations in mRNA and protein concentrations can be as high as 50% in yeast and even higher in mammalian cells. Thus, if everything is connected and stochastic, what hope could we have for a quantitative understanding of cellular decisions? Here we discuss the implications of recent advances in genomics, single-cell, and single-cell genomics technology for network modularity and cellular decisions. On the basis of these recent advances, we argue that most gene expression stochasticity and pathway interconnectivity is nonfunctional and that cellular decisions are likely much more predictable than previously expected.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.