Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction

  1. Nadiatou T Miningou Zobon
  2. Joanna Jędrzejewska-Szmek
  3. Kim T Blackwell  Is a corresponding author
  1. George Mason University, United States
  2. Nencki Institute of Experimental Biology of Polish Academy of Sciences, Poland


Long-lasting long-term potentiation (L-LTP) is a cellular mechanism of learning and memory storage. Studies have demonstrated a requirement for extracellular signal-regulated kinase (ERK) activation in L-LTP produced by a diversity of temporal stimulation patterns. Multiple signaling pathways converge to activate ERK, with different pathways being required for different stimulation patterns. To answer whether and how different temporal patterns select different signaling pathways for ERK activation, we developed a computational model of five signaling pathways (including two novel pathways) leading to ERK activation during L-LTP induction. We show that calcium and cAMP work synergistically to activate ERK and that stimuli given with large inter-trial intervals activate more ERK than shorter intervals. Furthermore, these pathways contribute to different dynamics of ERK activation. These results suggest that signaling pathways with different temporal sensitivity facilitate ERK activation to diversity of temporal patterns.

Data availability

All model files are freely available on https://github.com/neurord/ERK/releases/tag/1.0.0All programs to analyze simulation output are available on https://github.com/neurord/NeuroRDanal/releases/tag/2.0.0.Programs for the statistical analysis and random forest analysis are available on https://github.com/neurord/ERK/tree/master/Analysis.These URLs are provided in the manuscript methods section. Model files are available from modelDB, accession number 267073.

Article and author information

Author details

  1. Nadiatou T Miningou Zobon

    George Mason University, Fairfax, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Joanna Jędrzejewska-Szmek

    Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
    Competing interests
    The authors declare that no competing interests exist.
  3. Kim T Blackwell

    George Mason University, Fairfax, United States
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4711-2344


National Institutes of Health (R01MH 117964)

  • Kim T Blackwell

National Science Foundation (1515686)

  • Kim T Blackwell

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

Reviewing Editor

  1. Upinder Singh Bhalla, Tata Institute of Fundamental Research, India

Publication history

  1. Preprint posted: November 4, 2020 (view preprint)
  2. Received: November 5, 2020
  3. Accepted: August 3, 2021
  4. Accepted Manuscript published: August 10, 2021 (version 1)
  5. Version of Record published: August 13, 2021 (version 2)


© 2021, Miningou Zobon 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.


  • 1,106
    Page views
  • 123
  • 3

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)

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

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

  1. Nadiatou T Miningou Zobon
  2. Joanna Jędrzejewska-Szmek
  3. Kim T Blackwell
Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction
eLife 10:e64644.

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Kai J Sandbrink, Pranav Mamidanna ... Alexander Mathis
    Research Article

    Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical role of proprioception is representing the body state. We hypothesize that the proprioceptive system could also be critical for high-level tasks such as action recognition. To test this theory, we pursued a task-driven modeling approach, which allowed us to isolate the study of proprioception. We generated a large synthetic dataset of human arm trajectories tracing characters of the Latin alphabet in 3D space, together with muscle activities obtained from a musculoskeletal model and model-based muscle spindle activity. Next, we compared two classes of tasks: trajectory decoding and action recognition, which allowed us to train hierarchical models to decode either the position and velocity of the end-effector of one's posture or the character (action) identity from the spindle firing patterns. We found that artificial neural networks could robustly solve both tasks, and the networks'units show tuning properties similar to neurons in the primate somatosensory cortex and the brainstem. Remarkably, we found uniformly distributed directional selective units only with the action-recognition-trained models and not the trajectory-decoding-trained models. This suggests that proprioceptive encoding is additionally associated with higher-level functions such as action recognition and therefore provides new, experimentally testable hypotheses of how proprioception aids in adaptive motor control.

    1. Computational and Systems Biology
    Yujian Wen, Jielong Huang ... Hao Zhu
    Tools and Resources Updated

    Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality. Causal discovery (also called causal inference) infers causal interactions between objects from observational data. Reported causal discovery methods and single-cell datasets make applying causal discovery to single cells a promising direction. However, evaluating and choosing causal discovery methods and developing and performing proper workflow remain challenges. We report the workflow and platform CausalCell (http://www.gaemons.net/causalcell/causalDiscovery/) for performing single-cell causal discovery. The workflow/platform is developed upon benchmarking four kinds of causal discovery methods and is examined by analyzing multiple single-cell RNA-sequencing (scRNA-seq) datasets. Our results suggest that different situations need different methods and the constraint-based PC algorithm with kernel-based conditional independence tests work best in most situations. Related issues are discussed and tips for best practices are given. Inferred causal interactions in single cells provide valuable clues for investigating molecular interactions and gene regulations, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions.