1. Computational and Systems Biology
  2. Physics of Living Systems
Download icon

Receptor-based mechanism of relative sensing and cell memory in mammalian signaling networks

  1. Eugenia Lyashenko
  2. Mario Niepel
  3. Purushottam D Dixit
  4. Sang Kyun Lim
  5. Peter K Sorger
  6. Dennis Vitkup  Is a corresponding author
  1. Columbia University, United States
  2. Harvard Medical School, United States
Research Article
  • Cited 6
  • Views 2,655
  • Annotations
Cite this article as: eLife 2020;9:e50342 doi: 10.7554/eLife.50342

Abstract

Detecting relative rather than absolute changes in extracellular signals enables cells to make decisions in fluctuating environments. However, how mammalian signaling networks store the memories of past stimuli and subsequently use them to compute relative signals, i.e. perform fold change detection, is not well understood. Using the growth factor-activated PI3K-Akt signaling pathway, we develop computational and analytical models, and experimentally validate a novel mechanism of relative sensing in mammalian cells. This mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. We show the robustness and specificity of the relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. Our results suggest that similar mechanisms of memory and fold change detection are likely to be important across diverse signaling cascades and biological contexts.

Data availability

All data used in this study and the code used for simulations is available at https://github.com/dixitpd/FoldChange

Article and author information

Author details

  1. Eugenia Lyashenko

    Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Mario Niepel

    HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1415-6295
  3. Purushottam D Dixit

    Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sang Kyun Lim

    HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Peter K Sorger

    HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3364-1838
  6. Dennis Vitkup

    Department of Systems Biology, Columbia University, New York, United States
    For correspondence
    dv2121@cumc.columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4259-8162

Funding

National Institutes of Health (R01CA201276)

  • Eugenia Lyashenko
  • Purushottam D Dixit
  • Dennis Vitkup

National Institutes of Health (U54CA209997)

  • Eugenia Lyashenko
  • Purushottam D Dixit
  • Dennis Vitkup

National Institutes of Health (U54HL127365)

  • Mario Niepel
  • Sang Kyun Lim
  • Peter K Sorger

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Uri Alon, Weizmann Institute of Science, Israel

Publication history

  1. Received: July 19, 2019
  2. Accepted: December 18, 2019
  3. Accepted Manuscript published: January 21, 2020 (version 1)
  4. Version of Record published: February 27, 2020 (version 2)

Copyright

© 2020, Lyashenko et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,655
    Page views
  • 355
    Downloads
  • 6
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    Michael S Lauer, Deepshikha Roychowdhury
    Research Article Updated

    Previous reports have described worsening inequalities of National Institutes of Health (NIH) funding. We analyzed Research Project Grant data through the end of Fiscal Year 2020, confirming worsening inequalities beginning at the time of the NIH budget doubling (1998–2003), while finding that trends in recent years have reversed for both investigators and institutions, but only to a modest degree. We also find that career-stage trends have stabilized, with equivalent proportions of early-, mid-, and late-career investigators funded from 2017 to 2020. The fraction of women among funded PIs continues to increase, but they are still not at parity. Analyses of funding inequalities show that inequalities for investigators, and to a lesser degree for institutions, have consistently been greater within groups (i.e. within groups by career stage, gender, race, and degree) than between groups.

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Hannah R Meredith et al.
    Research Article

    Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.