[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Ryan sikorski [@Ryansikorski10](/creator/twitter/Ryansikorski10) on x 35.2K followers Created: 2025-07-17 10:33:28 UTC Molecule Harvesting Transmitter Model for Molecular Communication Systems Publisher: IEEE Molecular communication (MC) enables synthetic communication at nano-scale. In Molecular Communication (MC) systems, molecules are exploited as carriers of information for the exchange of messages between transmitter and receiver nano-machines. Without meaningful mathematical modeling of the components design and optimization are very cumbersome and heuristic approaches must be adopted. Hence, developing mathematical models is an important first step for system design. In diffusive Molecular Communication (MC) systems, the transmitter nano-machine releases signaling molecules into a fluid environment, where the released molecules are transported via passive diffusion, without consuming extra energy. The transport of the signaling molecules can also be affected by external factors such as flow and chemical reaction networks. In particular, the impact of flow on the performance can be constructive or destructive depending on the direction, magnitude, and randomness of the flow. Furthermore, signaling molecules entering the channel can be consumed by chemical reaction networks and degraded, such that they do not reach the receiver nano-machine. Signaling molecules that do reach the receiver nano-machine can potentially be observed and exploited for decoding the conveyed message. [4] The transmitter nano-machine can mitigate to some extent the disruptive effects of the impairments introduced by the channel. For instance, one approach to mitigate the impact of degradation reactions and/or flow in an undesired direction is for the transmitter nano-machine to control the release of the signaling molecules by increasing the number of released molecules and prolonging the release duration. On the other hand, the production of signaling molecules is an energy consuming process in general. For instance, in nature, cells produce signaling molecules by consuming adenosine triphosphate (ATP). [5] As a result, although releasing more signaling molecules seems promising, this comes at the expense of a higher energy consumption. In nature, there are also other mechanisms for increasing the number of available signaling molecules. One relevant approach employed by neurons is via harvesting previously released signaling molecules. In particular, neurons are equipped with re-uptake units, e.g., dopamine transporters, responsible for harvesting of signaling (dopamine) molecules, see [6]. Thus, developing meaningful models for transmitters that can harvest signaling molecules is of particular importance for the design of synthetic MC systems and is the focus of this paper. [4] Channel Modeling for Diffusive Molecular Communication (MC) - A Tutorial Review The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. [5] Alberts; Essential Cell Biology 3rd Ed PDF DOWNLOAD [6] Basic Neurochemistry: Principles of Molecular, Cellular, and Medical Neurobiology: Eighth Edition Abstract only: This new edition continues to cover the basics of neurochemistry as in the earlier editions, along with expanded and additional coverage of new research from intracellular trafficking, stem cells, adult neurogenesis, regeneration, and lipid messengers. It contains expanded coverage of all major neurodegenerative and psychiatric disorders, including the neurochemistry of addiction, pain, and hearing and balance; the neurobiology of learning and memory; sleep; myelin structure, development, and disease; autism; and neuroimmunology.  XXXXX engagements  [Post Link](https://x.com/Ryansikorski10/status/1945793970769957222)
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Ryan sikorski @Ryansikorski10 on x 35.2K followers
Created: 2025-07-17 10:33:28 UTC
Molecule Harvesting Transmitter Model for Molecular Communication Systems Publisher: IEEE
Molecular communication (MC) enables synthetic communication at nano-scale. In Molecular Communication (MC) systems, molecules are exploited as carriers of information for the exchange of messages between transmitter and receiver nano-machines. Without meaningful mathematical modeling of the components design and optimization are very cumbersome and heuristic approaches must be adopted. Hence, developing mathematical models is an important first step for system design.
In diffusive Molecular Communication (MC) systems, the transmitter nano-machine releases signaling molecules into a fluid environment, where the released molecules are transported via passive diffusion, without consuming extra energy. The transport of the signaling molecules can also be affected by external factors such as flow and chemical reaction networks. In particular, the impact of flow on the performance can be constructive or destructive depending on the direction, magnitude, and randomness of the flow. Furthermore, signaling molecules entering the channel can be consumed by chemical reaction networks and degraded, such that they do not reach the receiver nano-machine. Signaling molecules that do reach the receiver nano-machine can potentially be observed and exploited for decoding the conveyed message. [4]
The transmitter nano-machine can mitigate to some extent the disruptive effects of the impairments introduced by the channel. For instance, one approach to mitigate the impact of degradation reactions and/or flow in an undesired direction is for the transmitter nano-machine to control the release of the signaling molecules by increasing the number of released molecules and prolonging the release duration. On the other hand, the production of signaling molecules is an energy consuming process in general. For instance, in nature, cells produce signaling molecules by consuming adenosine triphosphate (ATP). [5] As a result, although releasing more signaling molecules seems promising, this comes at the expense of a higher energy consumption.
In nature, there are also other mechanisms for increasing the number of available signaling molecules. One relevant approach employed by neurons is via harvesting previously released signaling molecules. In particular, neurons are equipped with re-uptake units, e.g., dopamine transporters, responsible for harvesting of signaling (dopamine) molecules, see [6]. Thus, developing meaningful models for transmitters that can harvest signaling molecules is of particular importance for the design of synthetic MC systems and is the focus of this paper.
[4] Channel Modeling for Diffusive Molecular Communication (MC) - A Tutorial Review
The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed.
[5] Alberts; Essential Cell Biology 3rd Ed
PDF DOWNLOAD
[6] Basic Neurochemistry: Principles of Molecular, Cellular, and Medical Neurobiology: Eighth Edition
Abstract only:
This new edition continues to cover the basics of neurochemistry as in the earlier editions, along with expanded and additional coverage of new research from intracellular trafficking, stem cells, adult neurogenesis, regeneration, and lipid messengers. It contains expanded coverage of all major neurodegenerative and psychiatric disorders, including the neurochemistry of addiction, pain, and hearing and balance; the neurobiology of learning and memory; sleep; myelin structure, development, and disease; autism; and neuroimmunology.
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