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arXiv:2407.07106 · IEEE Transactions on Molecular Biological and Multi-Scale Communications

Molecular Quantum (MolQ) Communication Channel in the Gut-Brain Axis Synapse

Bitop Maitra, Özgür B. Akan

Published Sep 17, 2024• 5 citations
arXivHTMLPDF

Abstract

The gut-brain axis is the communication link between the gut and the brain. Although it is known that the gut-brain axis plays a pivotal role in homeostasis, its overall mechanism is still not known. However, for neural synapses, classical molecular communication is described by the formation of ligand-receptor complexes, which leads to the opening of ion channels. Moreover, there are some conditions that need to be fulfilled before the opening of the ion channel. In this study, we consider the gut-brain axis, where neurotransmitters diffuse through the synaptic cleft, considering molecular communication. On the vagus nerve (VN) membrane, i.e., the post-synaptic membrane of the synapse, it undergoes a quantum communication (QC), which initiates the opening of the ion channel, thus initiating the communication signal from the gut to the brain. It evolves a new paradigm of communication approach, Molecular Quantum (MolQ) communication. Based on the QC model, we theoretically analyze the output states, and QC is simulated considering the incoming neurotransmitter’s concentration and validated by analyzing the entropy and the mutual information of the input, i.e., neurotransmitter’s concentration, and output, i.e., ion channel opening.

DOI
10.1109/tmbmc.2024.3462727

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The gut-brain axis is the communication link between the gut and the brain. Although it is known that the gut-brain axis plays a pivotal role in homeostasis, its overall mechanism is still not known. However, for neural synapses, classical molecular communication is described by the formation of ligand-receptor complexes, which leads to the opening of ion channels. Moreover, there are some conditions that need to be fulfilled before the opening of the ion channel. In this study, we consider the gut-brain axis, where neurotransmitters diffuse through the synaptic cleft, considering molecular communication. On the vagus nerve (VN) membrane, i.e., the post-synaptic membrane of the synapse, it undergoes a quantum communication (QC), which initiates the opening of the ion channel, thus initiating the communication signal from the gut to the brain. It evolves a new paradigm of communication approach, Molecular Quantum (MolQ) communication. Based on the QC model, we theoretically analyze the output states, and QC is simulated considering the incoming neurotransmitter’s concentration and validated by analyzing the entropy and the mutual information of the input, i.e., neurotransmitter’s concentration, and output, i.e., ion channel opening.
The process of synaptic transmission is projected onto the territory of quantum mechanics. Thus, the electric interactions are seen as probabilistic events more than a deterministic one. In this manner, the neurotransmitter would obey quantum laws and their dynamics is governed by the evolution operator. Thus, synapse seen as an action of propagation would encompasses well to that the spatial propagator or Green function that might model the dyanamics of neurotransmitters along the cleft. This turns out to be instantaneous in coherence to the concept of neural transmission. Indeed under the quantum mechnics scenario it is found that the eigenvalues of energies of Hamiltonian consist in electric potentials between electrically charged neurotransmitters and receptors at the ion channels.
Abstract A computational model incorporating insights from quantum theory is proposed to describe and explain synaptic message transmission. We propose that together, neurotransmitters and their corresponding receptors, function as a physical “quantum decision tree” to “decide” whether to excite or inhibit the synapse. When a neurotransmitter binds to its corresponding receptor, it is the equivalent of randomly choosing different "strategies"; a “strategy" has two actions to take: excite or inhibit the synapse with a certain probability. The genetic programming can be applied for learning the observed data sequence to simulate the synaptic message transmission.
This chapter discusses several previously published results on possible applications of quantum information theory to neuroscience and will present a number of open questions that deserve further study. Applying the quantum information theory of consciousness to modeling the electric processes that occur in neuronal networks in the brain is an open scientific problem that deserves further study. The quantum behavior of individual ion channels is clearly manifested in the discrete changes observed in their channel conductance. Neurons communicate with each other at synaptic contacts that permit the transmission of electric or chemical signals from one neuron to another. Electrical synapses provide a direct electrical coupling between the two neurons through gap junctions that are nanopores constructed from connexin proteins. Multiple protein subunits, each possessing its own geometric shape, may further bind into protein complexes with a quaternary structure. Voltage-gated ion channels provide a typical example of protein complexes that are composed of multiple protein subunits.
Descriptions of neurotransmitter cycles in chemical synapses are generally accomplished in the field of macroscopic molecular biology. This paper proposes a new theoretical approach to model these cycles with methods of the non-relativistic quantum field theory (QFT) which is applicable on small neurotransmitters of nano size like amino acids or amines. The whole cycle is subdivided into the standard five phases: uptake, axonal transport, release and reception. Our ansatz is concentrated to quantum effects, which are relevant in molecular processes. Examples are quantization of momentums and energies of all small transmitters, definition of the density-based quantum information, quantization of molecular currents, because densities of them generate quantized particles. Our model of the neurotransmitter cycle of chemical synapses was created by the emphasis of possible essential quantum effects; therefore, we neglect many additional molecular aspects that do not lead us to quantum impacts. We elucidate the ramification of our quantum-based approach by the definition of particular Hamiltonians for each of the five phases of the neurotransmitter cycles and by the calculation of the corresponding molecular dynamics. The transformation from the particle representation to usual wave functions yields the probability to find at the same time neurotransmitters of different energy states at different positions. Our results have far-reaching implications and may initiate animated discussions. The validation or the disconfirmation of our hypothesis is still open.
Understanding neural excitability and action potential generation is a cornerstone of neuroscience, relying on the complex interplay of biophysical mechanisms governed by ion channels in neuronal membranes. Traditional approaches, such as the Hodgkin-Huxley model, have provided a robust framework for simulating these processes using classical computational techniques. However, the advent of quantum computing opens new avenues for exploring neuronal dynamics, particularly due to ion movement's inherently quantum mechanical properties at atomic scales. In this study, I investigated the intersection of quantum computing and membrane biophysics, evaluating how quantum algorithms could revolutionize simulations of ion channel behavior and neuronal signaling. I began by analyzing the limitations of classical methods in handling high-dimensional, nonlinear systems. Next, I introduced key quantum computing principles, including qubits, superposition, and entanglement, and their potential applications in solving molecular dynamics and differential equations relevant to neuronal activity. Despite promising theoretical advantages, practical challenges such as qubit coherence, error rates, and hardware scalability must be addressed before quantum models can outperform classical simulations. I also discussed hybrid classical-quantum approaches as a transitional strategy, leveraging the strengths of both paradigms. By bridging neuroscience, biophysics, and quantum information science, I aimed to stimulate interdisciplinary research toward more efficient and accurate models of neural excitability.