Non-Linear Classifiers for Wet-Neuromorphic Computing using Gene Regulatory Neural Network.

The Gene Regulatory Network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an Artificial Neural Network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where TF concentration relative to half-maximal RNA concentration and gene-product copy number influences transcription and translation via weighted multiplication before undergoing a non-linear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a Gene Regulatory Neural Network (GRNN). In this paper, we establish non-linear classifiers for molecular machine learning using the inherent sigmoidal non-linear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in the GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multi-layer sub-GRNNs and a sub-GRNN extracted from the E. Coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements.

Revealing gene regulation-based neural network computing in bacteria.

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction dynamics that can be embedded in the GRN as weights, converting a GRN to gene regulatory neural network (GRNN). Focusing on Pseudomonas aeruginosa, we extracted the GRNN associated with a well-known virulence factor, pyocyanin production, using an introduced weight extraction technique based on transcriptomic data and proving its computing accuracy using wet-lab experimental data. As part of our analysis, we evaluated the structural changes in the GRNN based on mutagenesis to determine its varying computing behavior. Furthermore, we model the ecosystem-wide cell-cell communications to analyze its impact on computing based on environmental as well as population signals, where we determine the impact on the computing reliability. Subsequently, we establish that the individual GRNNs can be clustered to collectively form computing units with similar behaviors to single-layer perceptrons with varying sigmoidal activation functions spatio-temporally within an ecosystem. We believe that this will lay the groundwork toward molecular machine learning systems that can see artificial intelligence move toward non-silicon devices, or living artificial intelligence, as well as giving us new insights into bacterial natural computing.

Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges.

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.

Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2.

Respiratory viruses including Respiratory Syncytial Virus, influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause serious and sometimes fatal disease in thousands of people annually. Understanding virus propagation dynamics within the respiratory system is critical because new insights will increase our understanding of virus pathogenesis and enable infection patterns to be more predictable in vivo, which will enhance our ability to target vaccine and drug delivery. This study presents a computational model of virus propagation within the respiratory tract network. The model includes the generation network branch structure of the respiratory tract, biophysical and infectivity properties of the virus, as well as air flow models that aid the circulation of the virus particles. As a proof of principle, the model was applied to SARS-CoV-2 by integrating data about its replication-cycle, as well as the density of Angiotensin Converting Enzyme expressing cells along the respiratory tract network. Using real-world physiological data associated with factors such as the respiratory rate, the immune response and virus load that is inhaled, the model can improve our understanding of the concentration and spatiotemporal dynamics of the virus. We collected experimental data from a number of studies and integrated them with the model in order to show in silico how the virus load propagates along the respiratory network branches.

A Graph-Based Molecular Communications Model Analysis of the Human Gut Bacteriome.

Alterations in the human Gut Bacteriome (GB) can be associated with human health issues, such as type-2 diabetes and obesity. Both external and internal factors can drive changes in the composition and in interactions of the human GB, impacting negatively on the host cells. This paper focuses on the human GB metabolism and proposes a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human GB network system. Our results show that regulation of molecular inputs strongly affects bacterial population growth and creates an unbalanced network, as shown by shifts in the node weights based on the produced molecular signals. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human GB network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviours of the human GB depending on molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial population growth and exchange of metabolites.

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.

A Voxel Model to Decipher the Role of Molecular Communication in the Growth of Glioblastoma Multiforme.

Glioblastoma Multiforme (GBM), the most malignant human tumour, can be defined by the evolution of growing bio-nanomachine networks within an interplay between self-renewal (Grow) and invasion (Go) potential of mutually exclusive phenotypes of transmitter and receiver cells. Herein, we present a mathematical model for the growth of GBM tumour driven by molecule-mediated inter-cellular communication between two populations of evolutionary bio-nanomachines representing the Glioma Stem Cells (GSCs) and Glioma Cells (GCs). The contribution of each subpopulation to tumour growth is quantified by a voxel model representing the end to end inter-cellular communication models for GSCs and progressively evolving invasiveness levels of glioma cells within a network of diverse cell configurations. Mutual information, information propagation speed and the impact of cell numbers and phenotypes on the communication output and GBM growth are studied by using analysis from information theory. The numerical simulations show that the progression of GBM is directly related to higher mutual information and higher input information flow of molecules between the GSCs and GCs, resulting in an increased tumour growth rate. These fundamental findings contribute to deciphering the mechanisms of tumour growth and are expected to provide new knowledge towards the development of future bio-nanomachine-based therapeutic approaches for GBM.

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.

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.

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.

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.

Genetic similarity of biological samples to counter bio-hacking of DNA-sequencing functionality.

We present the work towards strengthening the security of DNA-sequencing functionality of future bioinformatics systems against bio-computing attacks. Recent research has shown how using common tools, a perpetrator can synthesize biological material, which upon DNA-analysis opens a cyber-backdoor for the perpetrator to hijack control of a computational resource from the DNA-sequencing pipeline. As DNA analysis finds its way into practical everyday applications, the threat of bio-hacking increases. Our wetlab experiments establish that malicious DNA can be synthesized and inserted into E. coli, a common contaminant. Based on that, we propose a new attack, where a hacker to reach the target hides the DNA with malicious code on common surfaces (e.g., lab coat, bench, rubber glove). We demonstrated that the threat of bio-hacking can be mitigated using dedicated input control techniques similar to those used to counter conventional injection attacks. This article proposes to use genetic similarity of biological samples to identify material that has been generated for bio-hacking. We considered freely available genetic data from 506 mammary, lymphocyte and erythrocyte samples that have a bio-hacking code inserted. During the evaluation we were able to detect up to 95% of malicious DNAs confirming suitability of our method.

Analysis of Light Propagation on Physiological Properties of Neurons for Nanoscale Optogenetics.

Miniaturization of implantable devices is an important challenge for future brain-computer interface applications, and in particular for achieving precise neuron stimulation. For stimulation that utilizes light, i.e., optogenetics, the light propagation behavior and interaction at the nanoscale with elements within the neuron is an important factor that needs to be considered when designing the device. This paper analyzes the effect of light behavior for a single neuron stimulation and focuses on the impact from different cell shapes. Based on the Mie scattering theory, the paper analyzes how the shape of the soma and the nucleus contributes to the focusing effect resulting in an intensity increase, which ensures that neurons can assist in transferring light through the tissue toward the target cells. At the same time, this intensity increase can in turn also stimulate neighboring cells leading to interference within the neural circuits. This paper also analyzes the ideal placements of the device with respect to the angle and position within the cortex that can enable axonal biophoton communications, which can contain light within the cell to avoid the interference.

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.

Towards Concurrent Data Transmission: Exploiting Plasmid Diversity by Bacterial Conjugation.

The progress of molecular communication (MC) is tightly connected to the progress of nanomachine design. State-of-the-art states that nanomachines can be built either from novel nanomaterials by the help of nanotechnology or they can be built from living cells which are modified to function as intended by synthetic biology. With the growing need of the biomedical applications of MC, we focus on developing bio-compatible communication systems by engineering the cells to become MC nanomachines. Since this approach relies on modifying cellular functions, the improvements in the performance can only be achieved by integrating new biological properties. A previously proposed model for molecular communication is using bacteria as information carriers between transmitters and receivers, also known as bacterial nanonetworks. This approach has suggested encoding information into the plasmids inserted into the bacteria which leads to extra overhead for the receivers to decode and analyze the plasmids to obtain the encoded information. Another scheme, which is proposed in this paper, is to determine the digital information transmitted based on the quantity of bacteria emitted. While this scheme has its simplicity, the major drawback is the low-data rate resulting from the long propagation of the bacteria. To improve the performance, this paper proposes a distributed modulation scheme utilizing three bacterial properties, namely, engineering of plasmids, conjugation, and bacterial motility. In particular, genetic engineering allows us to engineer the different combinations of genes representing the different series of bits. When compared with binary density modulation and the M-ary density modulation, it is shown that the distributed modulation scheme outperforms the other two approaches in terms of bit error probability as well as the achievable rate for varying quantity of bacteria transmitted, distances, as well as time slot length.

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.

The Impact of Social Behavior on the Attenuation and Delay of Bacterial Nanonetworks.

Molecular communication (MC) is a new paradigm for developing communication systems that exchanges information through the transmission and reception of molecules. One proposed model for MC is using bacteria to carry information encoded into DNA plasmids, and this is termed bacterial nanonetworks. However, a limiting factor in the models that have been studied so far is the environment considered only in ideal conditions with a single population. This is far from realistic in natural environments, where bacteria coexist in multiple populations of same and different species, resulting in a very complex social community. This complex community has social interactions that include cooperation, cheating, as well as competition. In this paper, the effects of these social interactions on the information delivery in bacterial nanonetworks are studied in terms of delay, attenuation and data rate. The numerical results show that the cooperative behavior of bacteria improves the performance of delay and attenuation leading to a higher data rate, and this performance can be degraded once their behavior switches towards cheating. The competitive social behavior shows that the performance can degrade delay as well as attenuation leading to slower data rates, as the population with the encoded DNA plasmids are prevented from reaching the receiver. The analysis of social interactions between the bacteria will pave the way for efficient design of bacterial nanonetworks enabling applications such as intrabody sensing, drug delivery, and environmental control against pollution and biological hazards.