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The internet of Bio-Nano things
Abstract and Figures
The Internet of Things (IoT) has become an important research topic in the last decade, where things refer to interconnected machines and objects with embedded computing capabilities employed to extend the Internet to many application domains. While research and development continue for general IoT devices, there are many application domains where very tiny, concealable, and non-intrusive Things are needed. The properties of recently studied nanomaterials, such as graphene, have inspired the concept of Internet of NanoThings (IoNT), based on the interconnection of nanoscale devices. Despite being an enabler for many applications, the artificial nature of IoNT devices can be detrimental where the deployment of NanoThings could result in unwanted effects on health or pollution. The novel paradigm of the Internet of Bio-Nano Things (IoBNT) is introduced in this paper by stemming from synthetic biology and nanotechnology tools that allow the engineering of biological embedded computing devices. Based on biological cells, and their functionalities in the biochemical domain, Bio-NanoThings promise to enable applications such as intra-body sensing and actuation networks, and environmental control of toxic agents and pollution. The IoBNT stands as a paradigm-shifting concept for communication and network engineering, where novel challenges are faced to develop efficient and safe techniques for the exchange of information, interaction, and networking within the biochemical domain, while enabling an interface to the electrical domain of the Internet.
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... These characteristics of MC can be advantageous for environments like the human body, where the use of electromagnetic (EM) radiation for healthcare could result in unwanted effects on human tissue. The connection of MC to external networks such as the Internet leads to a novel communication paradigm called the Internet of Bio-Nano Things (IoBNT) [1,3]. IoBNT opens up a plethora of biomedical applications such as real-time intra-body sensing and actuation, targeted drug delivery (TDD), and tissue re-engineering [4]. ...
... The Internet of Things (IoT) presents a connected environment where real-life objects like sensors, actuators, and electronic devices can interact with each other through enabling technologies such as the Internet. The concept of IoT has recently been revised in the light of novel nanotechnology tools and synthetic biology, which have resulted in the fabrication of biologically embedded computing devices at the scale of nanometers (1-100 nm) called Bio-Nano things (BNT) [1]. The minute size puts constraints on their operational resources like computational complexity and storage capabilities. ...
... A reference architecture of IoBNT is presented in Figure 1. The IoBNT paradigm was first proposed by Akyildiz et al. [1] and followed by further research contributions in the design and technological aspects of IoBNT such as bio-cyber interfacing by Chude-Okonkwo et al. [5] and Nakano et al. [2], and molecular communication primitives by Nakano et al. [6,7] and and Felicetti et al. [8]; nanonetworks by Akyildiz et al. [3]; communication channel characteristics by Garralda et al. [9], Kuran et al. [10], and Gregori and Akyildiz [11]. The basic unit of IoBNT is the Bio-Nano Thing (BNT). ...
The Internet of bio-nano things (IoBNT) is an emerging paradigm employing nanoscale (~1–100 nm) biological transceivers to collect in vivo signaling information from the human body and communicate it to healthcare providers over the Internet. Bio-nano-things (BNT) offer external actuation of in-body molecular communication (MC) for targeted drug delivery to otherwise inaccessible parts of the human tissue. BNTs are inter-connected using chemical diffusion channels, forming an in vivo bio-nano network, connected to an external ex vivo environment such as the Internet using bio-cyber interfaces. Bio-luminescent bio-cyber interfacing (BBI) has proven to be promising in realizing IoBNT systems due to their non-obtrusive and low-cost implementation. BBI security, however, is a key concern during practical implementation since Internet connectivity exposes the interfaces to external threat vectors, and accurate classification of anomalous BBI traffic patterns is required to offer mitigation. However, parameter complexity and underlying intricate correlations among BBI traffic characteristics limit the use of existing machine-learning (ML) based anomaly detection methods typically requiring hand-crafted feature designing. To this end, the present work investigates the employment of deep learning (DL) algorithms allowing dynamic and scalable feature engineering to discriminate between normal and anomalous BBI traffic. During extensive validation using singular and multi-dimensional models on the generated dataset, our hybrid convolutional and recurrent ensemble (CNN + LSTM) reported an accuracy of approximately ~93.51% over other deep and shallow structures. Furthermore, employing a hybrid DL network allowed automated extraction of normal as well as temporal features in BBI data, eliminating manual selection and crafting of input features for accurate prediction. Finally, we recommend deployment primitives of the extracted optimal classifier in conventional intrusion detection systems as well as evolving non-Von Neumann architectures for real-time anomaly detection.
... This phenomenon is especially interesting in nanotechnology, where the complexity of individual entities might be very restricted by the choice of material, fabrication process, and size. Nanotechnology plays an important role in the Internet of Bio-Nano Things (IoBNT) [4]. ...
... In the intermediates, a conversion from Ato B-molecules takes place due to a firstorder reaction, which is assumed to be much slower than the diffusive process. 4 It is important to note that A-molecules can be converted into different B-molecules B ∈ {B + , B − }, where B + -and B − -molecules realize positive and negative matrix numbers, respectively. This is realized by filling the intermediates with a sufficiently large number of C + or C − molecules, which determine the sign s k,j of the respective intermediate compartment through the reactions A + C + k1 −−→ B + and A + C − k1 −−→ B − . ...
... In the remaining work, we refer to[11], which provides a comprehensive discussion on the matrix multiplication unit.4 This assumption will be only valid in the micro/nano-scale[11]. ...
p>Intelligent behavior is an emergent phenomenon observed in biological organisms across all scales. It describes the cooperative behavior of low complexity entities to accomplish complex tasks, which exceed their individual capabilities. This property is particularly important for the Internet of Bio-Nano Things (IoBNT), which consists of Bio-Nano Things (BNTs) used in the human body, where they face many restrictions, such as bio-compatibility and size constraints. In this paper, we present a novel BNT-architecture, called Molecular Nano Neural Networks (M3N), which allows the implementation of intelligence on the micro-/nano-scale. The proposed structure consists of compartments (low complexity entities) that are connected to each other to form a network. Based on reaction and diffusion of molecules in and between connected compartments, this network mimics an artificial neural network, which is an important step towards artificial intelligence in the IoBNT. We provide design guidelines for the proposed M3N and successfully validate it by applying a regression and classification task.</p
... This phenomenon is especially interesting in nanotechnology, where the complexity of individual entities might be very restricted by the choice of material, fabrication process, and size. Nanotechnology plays an important role in the Internet of Bio-Nano Things (IoBNT) [4]. ...
... In the intermediates, a conversion from Ato B-molecules takes place due to a firstorder reaction, which is assumed to be much slower than the diffusive process. 4 It is important to note that A-molecules can be converted into different B-molecules B ∈ {B + , B − }, where B + -and B − -molecules realize positive and negative matrix numbers, respectively. This is realized by filling the intermediates with a sufficiently large number of C + or C − molecules, which determine the sign s k,j of the respective intermediate compartment through the reactions A + C + k1 −−→ B + and A + C − k1 −−→ B − . ...
... In the remaining work, we refer to[11], which provides a comprehensive discussion on the matrix multiplication unit.4 This assumption will be only valid in the micro/nano-scale[11]. ...
p>Intelligent behavior is an emergent phenomenon observed in biological organisms across all scales. It describes the cooperative behavior of low complexity entities to accomplish complex tasks, which exceed their individual capabilities. This property is particularly important for the Internet of Bio-Nano Things (IoBNT), which consists of Bio-Nano Things (BNTs) used in the human body, where they face many restrictions, such as bio-compatibility and size constraints. In this paper, we present a novel BNT-architecture, called Molecular Nano Neural Networks (M3N), which allows the implementation of intelligence on the micro-/nano-scale. The proposed structure consists of compartments (low complexity entities) that are connected to each other to form a network. Based on reaction and diffusion of molecules in and between connected compartments, this network mimics an artificial neural network, which is an important step towards artificial intelligence in the IoBNT. We provide design guidelines for the proposed M3N and successfully validate it by applying a regression and classification task.</p
... mechanical pressure, voltage, temperature, light) or chemical signals (e.g. molecules, gas) [1]. ...
... Soon after, the binding between EVs and FPs evolves into fusion, with a rate constant that we denote as b f . The evolution of EVs from the extracellular space to the EV-cell fusion can be schematized by the following set of reactions, similarly to (1): ...
Cells communicate with each other exploiting a variety of chemical signals. Among them, Extracellular Vesicles (EVs) have attracted large interest by the scientific community. In fact, thanks to the advances in bio-nano-technology and the possibility of engineering EVs, they are envisioned as a perfect means for distributing biological information among receiving cells. However, deciphering the molecular mechanisms that regulate the delivery of EV cargo is, today, a necessary, yet challenging, step toward the exploitation of EV signaling to support innovative and efficient therapeutic protocols, alternative to current drug delivery technologies. In particular, very little information is currently available on the processes of EV fusion, which is the EV internalization process occurring when the EV membrane dissolves into the plasma membrane of the target cell, and the EV content is released into the cytosol. In order to understand the dynamics of this process, this paper introduces an analytical model of the evolution of the fusion process. Moreover, since the measurement of the biological parameters driving the fusion process is far to be achieved, in this paper we use the model as a tool to infer likely values of such parameters from parameters that are measurable with current technology.
... The reproducibility of the chemical reactions presented in equations (1), (3), and (4) as well as their responses in pH and absorbance was assessed by replacing all solutions with fresh ones every two weeks at the latest, by measuring their pH and absorbance, and by comparing these values with the values of previous measurements. Solutions were rejected and re-prepared if the measurements did not match. ...
Signal processing over the molecular domain is critical for analysing, modifying, and synthesising chemical signals in molecular communication systems. However, the lack of chemical signal processing blocks and the wide use of electronic devices to process electrical signals in existing molecular communication platforms can hardly meet the biocompatible, non-invasive, and size-miniaturised requirements of applications in various fields, e.g., medicine, biology, and environment sciences. To tackle this, here we design and construct a liquid-based microfluidic molecular communication platform for performing chemical concentration signal processing and digital signal transmission over distances. By specifically designing chemical reactions and microfluidic geometry, the transmitter of our platform is capable of shaping the emitted signals, and the receiver is able to threshold, amplify, and detect the chemical signals after propagation. By encoding bit information into the concentration of sodium hydroxide, we demonstrate that our platform can achieve molecular signal modulation and demodulation functionalities, and reliably transmit text messages over long distances. This platform is further optimised to maximise data rate while minimising communication error. The presented methodology for real-time chemical signal processing can enable the implementation of signal processing units in biological settings and then unleash its potential for interdisciplinary applications.
... Such powerful webs of synthetic cells might also establish strong connections with living cells and originate the so-called Internet of Bio-Nano Things (IoBNTs; Stano et al., 2023). The hybrid and collective intelligence of the IoBNTs promises to have a plethora of applications (Akyildiz, et al., 2015;Kuscu and Unluturk, 2021), such as diagnosis and therapies for human health and control and cleaning in natural ecosystems or urban areas. In the IoBNTs, even two- (Kagan et al., 2022) or three-dimensional cultures of human brain cells (brain organoids; Smirnova et al., 2023) might be involved. ...
Inspired by some traits of human intelligence, it is proposed that wetware approaches based on molecular, supramolecular, and systems chemistry can provide valuable models and tools for novel forms of robotics and AI, being constituted by soft matter and fluid states as the human nervous system and, more generally, life, is. Bottom-up mimicries of intelligence range from the molecular world to the multicellular level, i.e., from the Ångström ( 10 − 10 meters) to the micrometer scales ( 10 − 6 meters), and allows the development of unconventional chemical robotics. Whereas conventional robotics lets humans explore and colonise otherwise inaccessible environments, such as the deep oceanic abysses and other solar system planets, chemical robots will permit us to inspect and control the microscopic molecular and cellular worlds. This article suggests that systems made of properly chosen molecular compounds can implement all those modules that are the fundamental ingredients of every living being: sensory, processing, actuating, and metabolic networks. Autonomous chemical robotics will be within reach when such modules are compartmentalised and assembled. The design of a strongly intertwined web of chemical robots, with or without the involvement of living matter, will give rise to collective forms of intelligence that will probably reproduce, on a minimal scale, some sophisticated performances of the human intellect and will implement forms of “general AI.” These remarkable achievements will require a productive interdisciplinary collaboration among chemists, biotechnologists, computer scientists, engineers, physicists, neuroscientists, cognitive scientists, and philosophers to be achieved. The principal purpose of this paper is to spark this revolutionary collaborative scientific endeavour.
... R. Schober is with the Institute for Digital Communications, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany (e-mail: robert.schober@fau.de). ology [10], [16] is expected to enable sophisticated MC systems in the future, capable of performing the complex sensing, computation, and communication tasks needed for realizing the Internet of Bio-nano Things [17]. ...
Various applications of molecular communications (MC) are event-triggered, and, as a consequence, the prevalent Shannon capacity may not be the right measure for performance assessment. Thus, in this paper, we motivate and establish the identification capacity as an alternative metric. In particular, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC), subject to an average and a peak molecule release rate constraint, which serves as a model for MC systems employing molecule counting receivers. It is established that the number of different messages that can be reliably identified for this channel scales as 2(nlogn)R, where n and R are the codeword length and coding rate, respectively. Lower and upper bounds on the DI capacity of the DTPC are developed. The obtained large capacity of the DI channel sheds light on the performance of natural DI systems such as natural olfaction, which are known for their extremely large chemical discriminatory power in biology. Furthermore, numerical results for the empirical miss-identification and false identification error rates are provided for finite length codes. This allows us to characterize the behaviour of the error rate for increasing codeword lengths, which complements our theoretically-derived scale for asymptotically large codeword lengths.
... Real-time data on performance and environmental conditions help optimise resource allocation, reduce energy consumption, and enhance operational efficiency. Moreover, smart infrastructure supports predictive modelling and risk assessment, enabling proactive decision-making and reducing the likelihood of failures or disasters [50]. ...
Nanomaterials hold immense potential for transforming the field of civil engineering, offering enhanced performance and durability to infrastructure materials. However, their successful implementation faces several challenges and limitations that must be addressed. This abstract highlights the critical challenges associated with nanomaterials in civil engineering, including high production costs, scaling up production, health and safety risks, long-term performance and stability, standardisation and regulation, integration with existing construction practices, lack of comprehensive data and knowledge, and the need for multidisciplinary collaboration. Overcoming these challenges requires optimised manufacturing techniques, safety measures, extensive research, standardised protocols, and cooperation among researchers, engineers, manufacturers, regulators, and policymakers. Addressing these issues will pave the way for the safe and effective utilisation of nanomaterials in civil engineering, unlocking their potential to create sustainable, resilient, and innovative infrastructure systems.
Particulate Drug Delivery Systems (PDDS) are therapeutic methods that use nanoparticles to achieve their healing effects at the exact time, concentration level of drug nanoparticles, and location in the body, while minimizing the effects on other healthy locations. The Molecular Communication (MC) paradigm, where the transmitted message is the drug injection process, the channel is the cardiovascular system, and the received message is the drug reception process, has been investigated as a tool to study nanoscale biological and medical systems in recent years. In this paper, the various noise effects that cause uncertainty in the cardiovascular system are analyzed, modeled, and evaluated from the information theory perspective. Analytical MC noises are presented to include all end-to-end noise effects, from the drug injection, to the absorption of drug nanoparticles by the diseased cells, in the presence of a time-varying and turbulent blood flow. The PDDS capacity is derived analytically including all these noise effects and the constraints on the drug injection. The proposed MC noise is validated by using the kinetic Monte-Carlo simulation technique. Analytical expressions of the noise and the capacity are derived, and MC is presented as a framework for the optimization of particulate drug delivery systems (PDDS).
Molecular communications is a new paradigm that enables nanomachines to communicate within a biological environment. One form of molecular communications is calcium (Ca$^{2+}$) signaling, which occurs naturally in living biological cells. Ca$^{2+}$ signaling enables cells in a tightly packed tissue structure to communicate at short ranges with neighboring cells. The achievable mutual information of Ca$^{2+}$ signaling between tissue embedded nanomachines is investigated in this paper, focusing in particular on the impact that the deformation of the tissue structure has on the communication channel. Based on this analysis, a number of transmission protocols are proposed; nanomachines can utilize these to communicate using Ca $^{2+}$ signaling. These protocols are static time-slot configuration, dynamic time-slot configuration, dynamic time-slot configuration with silent communication, and improved dynamic time-slot configuration with silent communication (IDTC-SC). The results of a simulation study show that IDTC-SC provides the maximum data rate when tissues experience frequent deformation.
The goal of a Drug Delivery System (DDS) is to convey a drug where the medication is needed, while, at the same time, preventing the drug from affecting other healthy parts of the body. Drugs composed of micro or nano-sized particles (particulate DDS) that are able to cross barriers which prevent large particles from escaping the bloodstream are used in the most advanced solutions.Molecular Communication (MC) is used as an abstraction of the propagation of drug particles in the body. MC is a new paradigm in communication research where the exchange of information is achieved through the propagation of molecules. Here, the transmitter is the drug injection, the receiver is the drug delivery and the channel is realized by the transport of drug particles, thus enabling the analysis and design of a particulate DDS using communication tools. This is achieved by modeling the MC channel as two separate contributions, namely, the cardiovascular network model and the drug propagation network. The cardiovascular network model allows to analytically compute the blood velocity profile in every location of the cardiovascular system given the flow input by the heart. The drug propagation network model allows the analytical expression of the drug delivery rate at the targeted site given the drug injection rate. Numerical results are also presented to assess the flexibility and accuracy of the developed model. The study of novel optimization techniques for a more effective and less invasive drug delivery will be aided by this model, while paving the way for novel communication techniques for Intra-Body communication Networks (IBN).
Background
Realizing constructive applications of synthetic biology requires continued development of enabling technologies as well as policies and practices to ensure these technologies remain accessible for research. Broadly defined, enabling technologies for synthetic biology include any reagent or method that, alone or in combination with associated technologies, provides the means to generate any new research tool or application. Because applications of synthetic biology likely will embody multiple patented inventions, it will be important to create structures for managing intellectual property rights that best promote continued innovation. Monitoring the enabling technologies of synthetic biology will facilitate the systematic investigation of property rights coupled to these technologies and help shape policies and practices that impact the use, regulation, patenting, and licensing of these technologies.
Results
We conducted a survey among a self-identifying community of practitioners engaged in synthetic biology research to obtain their opinions and experiences with technologies that support the engineering of biological systems. Technologies widely used and considered enabling by survey participants included public and private registries of biological parts, standard methods for physical assembly of DNA constructs, genomic databases, software tools for search, alignment, analysis, and editing of DNA sequences, and commercial services for DNA synthesis and sequencing. Standards and methods supporting measurement, functional composition, and data exchange were less widely used though still considered enabling by a subset of survey participants.
Conclusions
The set of enabling technologies compiled from this survey provide insight into the many and varied technologies that support innovation in synthetic biology. Many of these technologies are widely accessible for use, either by virtue of being in the public domain or through legal tools such as non-exclusive licensing. Access to some patent protected technologies is less clear and use of these technologies may be subject to restrictions imposed by material transfer agreements or other contract terms. We expect the technologies considered enabling for synthetic biology to change as the field advances. By monitoring the enabling technologies of synthetic biology and addressing the policies and practices that impact their development and use, our hope is that the field will be better able to realize its full potential.
Molecular communication (MC) is a promising bio-inspired paradigm for the interconnection of autonomous nanotechnology-enabled devices, or nanomachines, into nanonetworks. MC realizes the exchange of information through the transmission, propagation, and reception of molecules, and it is proposed as a feasible solution for nanonetworks. This idea is motivated by the observation of nature, where MC is successfully adopted by cells for intracellular and intercellular communication. MC-based nanonetworks have the potential to be the enabling technology for a wide range of applications, mostly in the biomedical, but also in the industrial and surveillance fields. The focus of this article is on the most fundamental type of MC, i.e., diffusion-based MC, where the propagation of information-bearing molecules between a transmitter and a receiver is realized through free diffusion in a fluid. The objectives of the research presented in this article are to analyze an MC link from the point of view of communication engineering and information theory, and to provide solutions to the modeling and design of MC-based nanonetworks. First, a deterministic model is realized to study each component, as well as the overall diffusion-based- MC link, in terms of gain and delay. Second, the noise sources affecting a diffusion-based-MC link are identified and statistically modeled. Third, upper/lower bounds to the capacity are derived to evaluate the information-theoretic performance of diffusion-based MC. Fourth, an analysis of the interference produced by multiple diffusion-based MC links in a nanonetwork is provided. This research provides fundamental results that establish a basis for the modeling, design, and realization of future MC-based nanonetworks, as novel technologies and tools are being developed.
Recent advances in synthetic biology, in particular towards the engineering of DNA-based circuits, are providing tools to program man-designed functions within biological cells, thus paving the way for the realization of biological nanoscale devices, known as nanomachines. By stemming from the way biological cells communicate in the nature, Molecular Communication (MC), i.e., the exchange of information through the emission, propagation, and reception of molecules, has been identified as the key paradigm to interconnect these biological nanomachines into nanoscale networks, or nanonetwork. The design of MC nanonetworks built upon biological circuits is particularly interesting since cells possess many of the elements required to realize this type of communication, thus enabling the design of cooperative functions in the biological environment. In this paper, a systems-theoretic modeling is realized by analyzing a minimal subset of biological circuit elements necessary to be included in an MC nanonetwork design where the message-bearing molecules are propagated via free diffusion between two cells. The obtained system-theoretic models stem from the biochemical processes underlying cell-to-cell MC, and are analytically characterized by their transfer functions, attenuation and delay experienced by an information signal exchanged by the communicating cells. Numerical results are presented to evaluate the obtained analytical expressions as functions of realistic biological parameters.
Artificial cellular systems are minimal systems that mimic certain properties of natural cells, including signaling pathways, membranes, and metabolic pathways. These artificial cells (or protocells) can be constructed following a synthetic biology approach by assembling biomembranes, synthetic gene circuits, and cell‐free expression systems. As artificial cells are built from bottom‐up using minimal and a defined number of components, they are more amenable to predictive mathematical modeling and engineered controls when compared with natural cells. Indeed, artificial cells have been implemented as drug delivery machineries and in situ protein expression systems. Furthermore, artificial cells have been used as biomimetic systems to unveil new insights into functions of natural cells, which are otherwise difficult to investigate owing to their inherent complexity. It is our vision that the development of artificial cells would bring forth parallel advancements in synthetic biology, cell‐free systems, and in vitro systems biology.
This article is categorized under: Nanotechnology Approaches to Biology > Cells at the Nanoscale
Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
This article presents a branch of research where the use of molecules to encode and transmit information among nanoscale devices (nanomachines) is investigated as a bio-inspired viable solution to realize nano-communication networks. Unlike traditional technologies, molecular communication is a radically new paradigm, which demands novel solutions, including the identification of naturally existing molecular communication mechanisms, the establishment of the foundations of a molecular information theory, or the development of architectures and networking protocols for nanomachines. The tight connection of this cutting edge engineering research field with biology will ultimately enable both the bio-inspired study of molecular nanonetwork architectures and their realization with tools already available in nature. The testbed described in this article, which is based on a microfluidic device hosting intercommunicating populations of genetically engineered bacteria, is a clear example of this research direction.
This paper reviews recent developments in the design and application of two types of optical nanosensor, those based on: (1) localized surface plasmon resonance (LSPR) spectroscopy and (2) surface-enhanced Raman scattering (SERS). The performance of these sensors is discussed in the context of biological and chemical sensing. The first section addresses the LSPR sensors. Arrays of nanotriangles were evaluated and characterized using realistic protein/ligand interactions. Isolated, single nanoparticles were used for chemosensing and performed comparably to the nanoparticle array sensors. In particular, we highlight the effect of nanoparticle morphology on sensing response. The second section details the use of SERS sensors using metal film over nanosphere (MFON) surfaces. The high SERS enhancements and long-term stability of MFONs were exploited in order to develop SERS-based sensors for two important target molecules: a Bacillus anthracis biomarker and glucose in a serum protein mixture.