Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain-machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain's structure.

Biocomputing Model Using Tripartite Synapses Provides Reliable Neuronal Logic Gating With Spike Pattern Diversity.

Biocomputing technologies exploit biological communication mechanisms involving cell-cell signal propagation to perform computations. Researchers recently worked toward realising logic gates made by neurons to develop novel devices such as organic neuroprostheses or brain implants made by cells, herein termed living implants. Several challenges arise from this approach, mainly associated with the stochastic nature and noise of neuronal communication. Since astrocytes play a crucial role in the regulation of neurons activity, there is a possibility whereby astrocytes can be engineered to control synapses favouring reliable biocomputing. This work proposes a mathematical model of neuronal logic gates involving neurons and astrocytes, realising OR and AND gating. We use a shallow coupling of both the Izhikevich and Postnov models to characterise gating responses with spike pattern variability and astrocyte synaptic regulation. Logic operation error ratio and accuracy assess the AND and OR gates' performances at different synaptic Gaussian noise levels. Our results demonstrate that the astrocyte regulating activity can effectively be used as a denoising mechanism, paving the way for highly reliable biocomputing implementations.

Toward Interdisciplinary Synergies in Molecular Communications: Perspectives from Synthetic Biology, Nanotechnology, Communications Engineering and Philosophy of Science.

Within many chemical and biological systems, both synthetic and natural, communication via chemical messengers is widely viewed as a key feature. Often known as molecular communication, such communication has been a concern in the fields of synthetic biologists, nanotechnologists, communications engineers, and philosophers of science. However, interactions between these fields are currently limited. Nevertheless, the fact that the same basic phenomenon is studied by all of these fields raises the question of whether there are unexploited interdisciplinary synergies. In this paper, we summarize the perspectives of each field on molecular communications, highlight potential synergies, discuss ongoing challenges to exploit these synergies, and present future perspectives for interdisciplinary efforts in this area.

Engineering calcium signaling of astrocytes for neural-molecular computing logic gates.

This paper proposes the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of [Formula: see text] ion flows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling [Formula: see text] signals that flow through the population. A reinforced learning platform is also presented in the paper to optimize the [Formula: see text] activated level and time slot of input signals [Formula: see text] into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the effectiveness of the reinforced learning platform, a [Formula: see text] signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the [Formula: see text] activated level and time slot of input signals [Formula: see text] is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural-Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions.

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and impact society as a whole. Novel interdisciplinary techniques must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developed, known as Molecular Communications. This new emerging area uses elements of classical communication systems to molecular signalling and communication found inside and outside biological systems, characterizing the signalling processes between cells and viruses. In this paper, we provide an extensive and detailed discussion on how molecular communications can be integrated into the viral infectious diseases research, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on molecular communications models for viral infection (intra-body and extra-body), a deep analysis on their effects on immune response, how experimental can be used by the molecular communications community, as well as open issues and future directions.

Analysis of the Information Capacity of Neuronal Molecular Communications Under Demyelination and Remyelination.

Demyelination of neurons can compromise the communication performance between the cells as the absence of myelin attenuates the action potential propagated through the axonal pathway. In this work, we propose a hybrid experimental and simulation model for analyzing the demyelination effects on neuron communication. The experiment involves locally induced demyelination using Lysolecithin and from this, a myelination index is empirically estimated from analysis of cell images. This index is then coupled with a modified Hodgkin-Huxley computational model to simulate the resulting impact that the de/myelination processes has on the signal propagation along the axon. The effects of signal degradation and transfer of neuronal information are simulated and quantified at multiple levels, and this includes (1) compartment per compartment of a single neuron, (2) bipartite synapse and the effects on the excitatory post-synaptic potential, and (3) a small network of neurons to understand how the impact of de/myelination has on the whole network. By using the myelination index in the simulation model, we can determine the level of attenuation of the action potential concerning the myelin quantity, as well as the analysis of internal signalling functions of the neurons and their impact on the overall spike firing rate. We believe that this hybrid experimental and in silico simulation model can result in a new analysis tool that can predict the gravity of the degeneration through the estimation of the spiking activity and vice-versa, which can minimize the need for specialised laboratory equipment needed for single-cell communication analysis.

Feed-Forward and Feedback Control in Astrocytes for Ca 2+-Based Molecular Communications Nanonetworks.

Synaptic plasticity depends on the gliotransmitters' concentration in the synaptic channel. And, an abnormal concentration of gliotransmitters is linked to neurodegenerative diseases, including Alzheimer's, Parkinson's, and epilepsy. In this paper, a theoretical investigation of the cause of the abnormal concentration of gliotransmitters and how to achieve its control is presented through a Ca 2+-signalling-based molecular communications framework. A feed-forward and feedback control technique is used to manipulate IP 3 values to stabilize the concentration of Ca 2+ inside the astrocytes. The theoretical analysis of the given model aims i) to stabilize the Ca 2+ concentration around a particular desired level in order to prevent abnormal gliotransmitters' concentration (extremely high or low concentration can result in neurodegeneration), ii) to improve the molecular communication performance that utilizes Ca 2+ signalling, and maintain gliotransmitters' regulation remotely. It shows that the refractory periods from Ca 2+ can be maintained to lower the noise propagation resulting in smaller time-slots for bit transmission, which can also improve the delay and gain performances. The proposed approach can potentially lead to novel nanomedicine solutions for the treatment of neurodegenerative diseases, where a combination of nanotechnology and gene therapy approaches can be used to elicit the regulated Ca 2+ signalling in astrocytes, ultimately improving neuronal activity.

Modeling of Modulated Exosome Release From Differentiated Induced Neural Stem Cells for Targeted Drug Delivery.

A novel implantable and externally controllable stem-cell-based platform for the treatment of Glioblastoma brain cancer has been proposed to bring hope to patients who suffer from this devastating cancer type. Induced Neural Stem Cells (iNSCs), known to have potent therapeutic effects through exosomes-based molecular communication, play a pivotal role in this platform. Transplanted iNSCs demonstrate long-term survival and differentiation into neurons and glia which then fully functionally integrate with the existing neural network. Recent studies have shown that specific types of calcium channels in differentiated neurons and astrocytes are inhibited or activated upon cell depolarization leading to the increased intracellular calcium concentration levels which, in turn, interact with mobilization of multivesicular bodies and exosomal release. In order to provide a platform towards treating brain cancer with the optimum therapy dosage, we propose mathematical models to compute the therapeutic exosomal release rate that is modulated by cell stimulation patterns applied from the external wearable device. This study serves as an initial and required step in the evaluation of controlled exosomal secretion and release via induced stimulation with electromagnetic, optical and/or ultrasonic waves.

Reconfigurable Filtering of Neuro-Spike Communications Using Synthetically Engineered Logic Circuits.

High-frequency firing activity can be induced either naturally in a healthy brain as a result of the processing of sensory stimuli or as an uncontrolled synchronous activity characterizing epileptic seizures. As part of this work, we investigate how logic circuits that are engineered in neurons can be used to design spike filters, attenuating high-frequency activity in a neuronal network that can be used to minimize the effects of neurodegenerative disorders such as epilepsy. We propose a reconfigurable filter design built from small neuronal networks that behave as digital logic circuits. We developed a mathematical framework to obtain a transfer function derived from a linearization process of the Hodgkin-Huxley model. Our results suggest that individual gates working as the output of the logic circuits can be used as a reconfigurable filtering technique. Also, as part of the analysis, the analytical model showed similar levels of attenuation in the frequency domain when compared to computational simulations by fine-tuning the synaptic weight. The proposed approach can potentially lead to precise and tunable treatments for neurological conditions that are inspired by communication theory.

Utilizing Neurons for Digital Logic Circuits: A Molecular Communications Analysis.

With the advancement of synthetic biology, several new tools have been conceptualized over the years as alternative treatments for current medical procedures. As part of this work, we investigate how synthetically engineered neurons can operate as digital logic gates that can be used towards bio-computing inside the brain and its impact on epileptic seizure-like behaviour. We quantify the accuracy of logic gates under high firing rates amid a network of neurons and by how much it can smooth out uncontrolled neuronal firings. To test the efficacy of our method, simulations composed of computational models of neurons connected in a structure that represents a logic gate are performed. Our simulations demonstrate the accuracy of performing the correct logic operation, and how specific properties such as the firing rate can play an important role in the accuracy. As part of the analysis, the mean squared error is used to quantify the quality of our proposed model and predict the accurate operation of a gate based on different sampling frequencies. As an application, the logic gates were used to smooth out epileptic seizure-like activity in a biological neuronal network, where the results demonstrated the effectiveness of reducing its mean firing rate. Our proposed system has the potential to be used in future approaches to treating neurological conditions in the brain.

Quality and Capacity Analysis of Molecular Communications in Bacterial Synthetic Logic Circuits.

Synthetic logic circuits have been proposed as potential solutions for theranostics of biotechnological problems. One proposed model is the engineering of bacteria cells to create logic gates, and the communication between the bacteria populations will enable the circuit operation. In this paper, we analyze the quality of bacteria-based synthetic logic circuit through molecular communications that represent communication along a bus between three gates. In the bacteria-based synthetic logic circuit, the system receives environmental signals as molecular inputs and will process this information through a cascade of synthetic logic gates and free diffusion channels. We analyze the performance of this circuit by evaluating its quality and its relationship to the channel capacity of the molecular communications links that interconnect the bacteria populations. Our results show the effect of the molecular environmental delay and molecular amplitude differences over both the channel capacity and circuit quality. Furthermore, based on these metrics, we also obtain an optimum region for the circuit operation resulting in an accuracy of 80% for specific conditions. These results show that the performance of synthetic biology circuits can be evaluated through molecular communications, and lays the groundwork for combined systems that can contribute to future biomedical and biotechnology applications.

Molecular Communications Pulse-Based Jamming Model for Bacterial Biofilm Suppression.

Studies have recently shown that the bacteria survivability within biofilms is responsible for the emergence of superbugs. The combat of bacterial infections, without enhancing its resistance to antibiotics, includes the use of nanoparticles to quench the quorum sensing of these biofilm-forming bacteria. Several sequential and parallel multi-stage communication processes are involved in the formation of biofilms. In this paper, we use proteomic data from a wet lab experiment to identify the communication channels that are vital to these processes. We also identified the main proteins from each channel and propose the use of jamming signals from synthetically engineered bacteria to suppress the production of those proteins. This biocompatible technique is based on synthetic biology and enables the inhibition of biofilm formation. We analyze the communications performance of the jamming process by evaluating the path loss for a number of conditions that include different engineered bacterial population sizes, distances between the populations, and molecular signal power. Our results show that sufficient molecular pulse-based jamming signals are able to prevent the biofilm formation by creating lossy communications channels (almost -3 dB for certain scenarios). From these results, we define the main design parameters to develop a fully operational bacteria-based jamming system.

Computational Models for Trapping Ebola Virus Using Engineered Bacteria.

The outbreak of the Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing the Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60 percent of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into a future treatment system whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.

Wireless Optogenetic Nanonetworks for Brain Stimulation: Device Model and Charging Protocols.

In recent years, numerous research efforts have been dedicated toward developing efficient implantable devices for brain stimulation. However, there are limitations and challenges with the current technologies. They include neuron population stimulation instead of single neuron level, the size, the biocompatibility, and the device lifetime reliability in the patient's brain. We have recently proposed the concept of wireless optogenetic nanonetworking devices (WiOptND) that could address the problem of long term deployment, and at the same time target single neuron stimulation utilizing ultrasonic as a mode for energy harvesting. In addition, a number of charging protocols are also proposed, in order to minimize the quantity of energy required for charging, while ensuring minimum number of neural spike misfirings. These protocols include the simple charge and fire, which requires the full knowledge of the raster plots of neuron firing patterns, and the predictive sliding detection window, and its variant Markov-chain based time-delay patterns, which minimizes the need for full knowledge of neural spiking patterns as well as number of ultrasound charging frequencies. Simulation results exhibit a drop for the stimulation ratio of ~ 25% and more stable trend in its efficiency ratio (standard deviation of ~0.5%) for the Markov-chain based time-delay patterns protocol compared with the baseline change and fire. The results show the feasibility of utilizing WiOptND for long-term implants in the brain, and a new direction toward precise stimulation of neurons in the cortical microcolumn of the brain cortex.

Using information metrics and molecular communication to detect cellular tissue deformation.

Calcium-signaling-based molecular communication has been proposed as one form of communication for short range transmission between nanomachines. This form of communication is naturally found within cellular tissues, where Ca(2+) ions propagate and diffuse between cells. However, the naturally flexible structure of cells usually leads to the cells dynamically changing shape under strain. Since the interconnected cells form the tissue, a change in shape of one cell will change the shape of the neighboring cells and the tissue as a whole. This will in turn dramatically impair the communication channel between the nanomachines. We propose a process for nanomachines utilizing Ca(2+) based molecular communication to infer and detect the state of the tissue, which we term the Molecular Nanonetwork Inference Process. The process employs a threshold based classifier that identifies its threshold boundaries based on a training process. The inference/detection mechanism allows the destination nanomachine to determine: i) the type of tissue deformation; ii) the amount of tissue deformation; iii) the amount of Ca(2+) concentration emitted from the source nanomachine; and iv) its distance from the destination nanomachines. We evaluate the use of three information metrics: mutual information, mutual information with generalized entropy and information distance. Our analysis, which is conducted on two different topologies, finds that mutual information with generalized entropy provides the most accurate inferencing/detection process, enabling the classifier to obtain 80% of accuracy on average.