Abstract

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-efficient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfish's muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control of locomotion.

Data availability

No experimental data sets were generated in this study. Simulation parameters for all figures can be found in the manuscript and its supplements.Hydrodynamics simulations were performed with the IB2D package by Nicholas A. Battista (https://github.com/nickabattista/ib2d).

Article and author information

Author details

  1. Fabian Pallasdies

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    For correspondence
    fabianpallasdies@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5359-4699
  2. Sven Goedeke

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Wilhelm Braun

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9419-3311
  4. Raoul Memmesheimer

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    For correspondence
    rm.memmesheimer@uni-bonn.de
    Competing interests
    The authors declare that no competing interests exist.

Funding

The German Federal Ministry of Education and Research (01GQ1710)

  • Fabian Pallasdies
  • Sven Goedeke
  • Wilhelm Braun
  • Raoul Memmesheimer

SMARTSTART Joint Training Program of the Bernstein Network and the VolkswagenStiftung (SmartStart2)

  • Fabian Pallasdies

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

Reviewing Editor

  1. Ronald L Calabrese, Emory University, United States

Version history

  1. Received: July 10, 2019
  2. Accepted: December 22, 2019
  3. Accepted Manuscript published: December 23, 2019 (version 1)
  4. Version of Record published: February 3, 2020 (version 2)
  5. Version of Record updated: May 27, 2020 (version 3)

Copyright

© 2019, Pallasdies 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

  • 9,429
    Page views
  • 696
    Downloads
  • 21
    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)

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. Fabian Pallasdies
  2. Sven Goedeke
  3. Wilhelm Braun
  4. Raoul Memmesheimer
(2019)
From single neurons to behavior in the jellyfish Aurelia aurita
eLife 8:e50084.
https://doi.org/10.7554/eLife.50084

Share this article

https://doi.org/10.7554/eLife.50084

Further reading

    1. Computational and Systems Biology
    James D Brunner, Nicholas Chia
    Research Article

    The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter 'probiotic' treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual's microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

    1. Computational and Systems Biology
    Tae-Yun Kang, Federico Bocci ... Andre Levchenko
    Research Article

    Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell–cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip–Stalk cell patterning can undergo an order–disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.