Skip to main content
  • Poster presentation
  • Open access
  • Published:

ATP consumption in molecular reactions of neuronal signaling

The human brain consumes 106 times less energy than the currently fastest supper computer [1], while maintaining a comparable performance in many demanding task [2]. These energetic efficiency has been suggested to result from primitive computations on a molecular level [3]. However, while the importance of ion channels on energy efficiency has been the primary focus of research [4, 5], most computations occur at the molecular level prior to the amplification step and prior to the information transmission. We calculate the amount of energy consumed by such computations and compare their structural and functional properties. As a starting point, we chose the molecular reactions involved in long term depression and using our stochastic model [6] estimate the molecular energy consumption. To compare our feedback loop we investigate the energy consumption of millions of feedback loops in molecular signaling. For the first time we are able to go beyond the current size limit of 15 steps [7] and, using a computer cluster, detect feedback loops with hundreds of molecular reactions. We find that the number of ATPs consumed is related with size of positive feedback loop. We conclude that the energy consumed by the long term depression is only marginally above the physical limit of storing information and higher than its silicon equivalent of random access memory. Hence, this study provides the first systematic attempt to investigate the energy consumption of information-storing primitive computations and points towards energy efficient motifs for synthetic biology.


  1. Niven JE, Laughlin SB: Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology. 2008, 211 (11): 1792-1804. 10.1242/jeb.017574.

    Article  CAS  PubMed  Google Scholar 

  2. Ferrucci DA: Introduction to "This is Watson". Ibm Journal of Research and Development. 2012, 56 (3-4): 15-

    Google Scholar 

  3. Mead C: Neuromorphic Electronic Systems. Proceedings of the Ieee. 1990, 78 (10): 1629-1636. 10.1109/5.58356.

    Article  Google Scholar 

  4. Sengupta B, Stemmler M, Laughlin SB, Niven JE: Action Potential Energy Efficiency Varies Among Neuron Types in Vertebrates and Invertebrates. Plos Computational Biology. 2010, 6 (7):

  5. Alle H, Roth A, Geiger JRP: Energy-Efficient Action Potentials in Hippocampal Mossy Fibers. Science. 2009, 325 (5946): 1405-1408. 10.1126/science.1174331.

    Article  CAS  PubMed  Google Scholar 

  6. Antunes G, De Schutter E: A Stochastic Signaling Network Mediates the Probabilistic Induction of Cerebellar Long-Term Depression. J Neurosci. 2012, 32 (27):

  7. Ma'ayan A, Cecchi GA, Wagner J, Rao AR, Iyengar R, Stolovitzky G: Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks. Proc Natl Acad Sci U S A. 2008, 105 (49): 19235-19240. 10.1073/pnas.0805344105.

    Article  PubMed Central  PubMed  Google Scholar 

Download references


Both are funded by OIST GU, Japan.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Nikon Rasumov.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rasumov, N., De Schutter, E. ATP consumption in molecular reactions of neuronal signaling. BMC Neurosci 15 (Suppl 1), P179 (2014).

Download citation

  • Published:

  • DOI: