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ATP consumption in molecular signaling of CA1 Hippocampus neurons

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The human brain consumes 106 times less energy than the currently fastest super computer [1], while maintaining a comparable performance in many demanding tasks [2]. This 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 through neurons. We calculate the amount of energy consumed by such computations and compare their structural and functional properties. As a starting point, we chose 2000 reactions in the signaling pathways of CA1 hippocampal neurons [6]. As not every reaction consumes either one or zero ATPs, we undergo a wide literature search to identify the exact energy consumption of over 60 million of possible feedback loops. We find that the number of ATPs consumed is related with size of positive feedback loop. Hence, this study provides the first systematic and detailed attempt to investigate the energy consumption of information-storing primitive computations and points towards energy efficient motifs for synthetic biology.

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Acknowledgements

This work was supported by funding from the De Schutter Unit Okinawa Institute of Science and Technology Graduate University.

Author information

Correspondence to Nikon Rasumov.

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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 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Keywords

  • Energy Consumption
  • Molecular Level
  • Feedback Loop
  • Hippocampal Neuron
  • Energy Efficiency