The Nano Communication Networks

Published Date: 02 Nov 2017

Abstract — A nanonetwork or nanoscale network is a set of interconnected nanomachines, i.e., devices in the order of a few hundred nanometers or a few micrometers at most, which are able to perform only very simple tasks such as computing, data storing, sensing and actuation. Nanonetworks are expected to expand the capabilities of single nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and fuse information. Nanonetworks enable new applications of nanotechnology in biomedical field, environmental research, military technology and industrial and consumer goods applications. In this paper, the state of the art of informatics of cellular signaling (also called cell communication) in terms of computer science and signal processing is briefly reviewed. The framework of network informatics from the aspects of signal and information is proposed to explore the principles of nano communication.

I. INTRODUCTION

As a complex system, a cell is the home of enormous molecules where the signaling processes behave dynamically. Interactions among these molecules are complex and are normally represented by networks. With the chemical reactions caused by these networked molecules, nanomachines such as molecular motors and ion channels work within and between the cells. As a promising field at the frontier of science and technology, nano-communication by nanonetworks has emerged with the advance of nanotechnology and molecular biology. The cell provides a test bed for new ideas of nano-communication by nanonetworks. The goal of the research on the molecular informatics of nano-communication in cells is to systematically understand the network informatics of cell communication based on cellular signaling.

Fundamentals of cellular informatics

Terms in molecular biology

The basic terms in molecular biology are briefly given as follows:

DNA - deoxyribonucleic acid.

RNA - ribonucleic acid.

Protein - a molecular complex whose components are amino acids.

Cell - a compartment of the organism with the biological function.

Kinase - a protein (enzyme) in cells that can phosphorylate the signaling molecules in cells.

Phosphatase - a protein (enzyme) in cells that can detach a phosphate from a phosphorylated signaling molecule.

Phosphorylation/dephosphorylation - the cellular signaling processes for attaching/detaching a phosphate to/from a protein.

Detection and measurement of signals: instrumentation for nanobiosignal processing

Instrumentation and signaling processing are inevitable for industrial automation and control systems. Nowadays, the scope of instruments for measuring the structure of objects is extending into nanosized bio-objects in vitro and in vivo, e.g., the instruments for high-resolution imaging. The related key technologies of signal processing for nanoscale measurement includes AFM (atom force microscopy), Fmri (functional magnetic resonance imaging), FRET (fluorescent resonance energy transfer) and other technologies of high-resolution fluorescence, optical imaging, mass spectrometry and others. The quantum dot is a new technology deserving to be noticed.

As shown in Fig. 1, the idea of FRET is to let a special material called a molecular probe be attached to a protein such as GTPase (guanosine triphosphatase) in order to emit light (e.g., green) so that the state of these proteins can be detected. The visualization technology for fluorescent proteins in cells needs the following major operations:

Preparing the cell culture;

Attaching the object proteins to molecular probes;

Detecting the state-based spatio-temporal signals of the proteins;

Generating corresponding images from the detected signals.

Here the spatial information for concentration is very important; it can enhance the quantitative measurement performance for local concentration to study the effects of diffusion and reaction in cellular signaling processes. This may extend the bioinformatics simulation using the average values of molecular concentration into the one using the spatial concentration distribution.

Fig. 1. Principle of molecular fluorescence.

II. SYSTEM ARCHITECTURE

Cell communication

Cell communication (see Fig. 2) consists of two types: intra-cell communication and inter-cell communication; these influence the major functions of the cell. An arrow refers to the flux of molecules that carry out the processes of cell communication. The signaling molecules, called first messengers, act between different cells and are the media of cell-to-cell communication. The signaling molecules, called second messengers, move within the cell and deliver the signals between the membrane and the nucleus. The membrane is the place for transduction between the signals outside the cell and signals within the cell. The spatial distribution of the molecules provides information for modeling the diffusion process within the cell, which is often called passive transport, where Brownian motion is one of the best ways to formulate this diffusion process. Molecular motors in the cell act as a vehicle to deliver molecules, called cargos. From the interactions of molecules caused by diffusion-reaction and the cellular function influenced by motor proteins, the structure of the cellular signaling network can be inferred.

Fig. 2. Communication in the cell and among cells.

By using the Michaelis - Menten equation, the dynamic features of the intra-cell communication can be investigated quantitatively. The dynamical changes of signals are reflected in the variation of the topological structure of the networks. To observe how the cell communication process is carried out under a dynamical environment in nature may provide us with some hints to study the protocols of nanonetworks suggested in which factors such as data structure, message type, system model, operation, algorithm representation for protocol and correctness of message transmission need to be taken into consideration.

Abstract representation of cell communication

Biomolecular signaling in cells normally takes the form of concentration. This is in contrast to the digital communication we are familiar with. Based on the analog signals of molecular concentration, molecular networks adaptively regulate the biological functions.

To get a mathematical model for cellular communication needs information theory, since information theory is inseparable from communication systems. In cellular communications, an information-theoretical model is used to formulate the information transmission in cells where information is extracted from biomolecular processes. It is natural to use graphs to represent networks. Networking, analysis and synthesis of a network system are closely related sometimes. Structural features (e.g., modularity) and performance (e.g., robustness) are important factors when we study cell communication networks.

In order to extract dynamical information flow for the description of the topological structure of a signal

transduction network, linear network coding is used for the formal description of signal transduction networks. Linear network coding (denoted as LNC for short), initiated by Li, Yeung, and Cai, is an emerging and promising method for network informatics. Network coding is one of the ways to formulate the signaling pathway networks. The phosphorylation and dephosphorylation process is represented by a network coding model, as illustrated in Fig. 3.

Fig. 3. Phosphorylation and dephosphorylation process represented by a network coding model.

Formalization is a basic step for theoretical computer science models of biomolecular information processing. Its generalized form is given by a graph rewriting process: where and refer to a graph (or hypergraph); and denote the current moment and next moment, respectively.

Fig. 4 demonstrates a graph rewriting process for two states of the biochemical reactions. It is obvious that G include a tree. A string can also be represented by a graph where the nodes that represent the symbols in the string are connected in a line.

Fig. 4. Graph rewriting for two states of the biochemical reactions.

The major data structure used for biomolecular computing covers strings, trees, graphs, multi-sets and others. Based on the state of a molecule, the related computing process can be modeled as the state transition in automata. The information of biomolecules can be described by formal languages and automaton.

III. APPLICATIONS

Nano technologies promise new solutions for several applications in biomedical, industrial and military fields. At nano-scale, a nano-machine can be considered as the most basic functional unit. Nano-machines are tiny components consisting of an arranged set of molecules, which are able to perform very simple tasks. Nanonetworks. i.e., the interconnection of nano-machines are expected to expand the capabilities of single nano-machines by allowing them to cooperate and share information. Traditional communication technologies are not suitable for nanonetworks mainly due to the size and power consumption of transceivers, receivers and other components. The use of molecules, instead of electromagnetic or acoustic waves, to encode and transmit the information represents a new communication paradigm that demands novel solutions such as molecular transceivers, channel models or protocols for nanonetworks.

While there is a large number of applications that nanonetworks could apply to, we briefly present three categories of applications below that are capture the significance of nanonetworks:

Biomedical applications. The nanoscale is the natural domain of molecules, proteins, DNA, organelles and the major components of cells. As a result, a large number of applications of nanonetworks is in the biomedical field. Nanomachines can be deployed over (e.g., tattoo-like) or inside the human body (e.g., a pill or intramuscular injection) to monitor glucose, sodium, and cholesterol, to detect the presence of different infectious agents, or to identify specific types of cancer. Nanonetworks will also enable new smart drug delivery systems which combine the sensing capabilities of nanomachines with the abilities of nano-actuators to release specific drugs inside the body with great accuracy and in a timely manner.

Industrial applications The tools provided by nanotechnologies can be used to monitor and control the formation of biofilms in several industrial applications. A biofilm is an aggregate of nano and micro-organisms in which cells adhere to each other and usually onto a surface. Biofilms can have both beneficial and detrimental effects, depending on the application. For example, they can be used to clean residual waters coming from different manufacturing processes or organic waste. However, they can also be the tool for infectious diseases to spread through pipes and other liquid conducting mechanisms. In our vision, nanonetworks can be used to first detect the formation of biofilms and then to release specific chemical compounds to locally enhance or terminate their formation

Security/Safety applications. Nanotechnology is enabling the development of biological and chemical nanosensors which have an unprecedented sensing accuracy. Nanonetworks composed by several of these nanosensors will serve as a countermeasure for surveillance against Nuclear, Biological and Chemical attacks at the nanoscale. For example, nanosensors can be used to detect chemical particles faster and in lower concentrations than conventional microsensors. Upon the detection of a toxic chemical compound, several nanomachines will transmit the information related to this event in a multi-hop way to a sink or command center. In addition, it will also be possible for the nanomachines to receive commands from the macroscale in order to, for example, change their behavior.

IV. ISSUES / CHALLENGES

To better understand the challenges involved in nano communication, it might be useful to first look at insights gained from classical wireless sensor networks. We are study the list of security issues presented therein, taking a look at the novel problems, limitations, and opportunities in the nano networking domain.

The following security challenges have to especially be considered in sensor networks:

Performance and scalability – Focusing on ultra-low resource nano networks, the performance of secure communication protocols and cryptographic algorithms needs to be reconsidered for developing practical applications.

Secure localization – Localization techniques for location depended applications such as drug delivery will have to rely on some basic nano communication capabilities.

Performance and scalability

Nano communication security will create huge performance and scalability challenges. Severe resource limitations in single nano machines on the one hand and an uncountable number of those machines on the other hand makes nano communication incomparable to any existing communication system. The performance of cryptographic algorithms has been evaluated in the sensor networking domain, but these results cannot be directly transferred to nano devices because of the different form of information processing. Examples include indirect techniques using specific RNA sequences (communication using shelfs of flagellated bacteria).

Energy consumption is another critical aspect. Some communication schemes like nanotube based radios have rather high energy consumption, and extending communication due to cryptographic payload or security protocols might be prohibitive. A specific encoding information in DNA/RNA and molecular processing based on specific enzymes might be faster and more energy efficient but prevent usage of existing security schemes. Using classical cryptography might also be very inefficient if only limited information is transmitted (like sending a small specific molecule to transmit one bit of information). Then adding a digital signature or long cryptographic message authentication code is not appropriate.

Another interesting aspect is whether authentication can be scaled to such a large number of entities. For example, can those systems be individually named and addressed which would be a requirements for most classical authentication schemes. Finally, one needs to note that there will be a huge asymmetry between the computational performance of a single nanomachine compared to a regular desktop computer. This might affect the achievable security level, as one might have to work with short key lengths due to resource constraints, which would allow attackers easier brute-force attacks using highperformance computing, e.g., available through graphic cards.

Secure localization

Some applications using nano communication will require localization of nano machines to fulfill their tasks. Requirements might be very different from classical sensor networks, using other coordinate systems (e.g., position inside the body) and having nano scale accuracy requirements. Absolute positioning with nano scale resolution might be difficult to achieve, but relative positioning might be more relevant anyways. This links directly to security where physical proximity might be used

as part of authentication, e.g., allowing only close-by nano machines to communicate, preventing more distant attackers from interfering.

Approaches similar to existing secure distance bounding protocols that ensure that communicating entities are close-by could be investigated. Distance bounding protocols can thus be developed as an additional mean of authentication. However, as many existing schemes are based on time-of-flight measurements, these are not directly applicable as it would require sub-nano-second clock accuracy.

V. STANDARDS RELATED WITH NANO COMMUNICATION NETWORKS

Intracellular communication by a signaling pathway and a molecular motor

From molecular motors and ion channels, an image of nanomachines in general can be viewed. Nanobiomachines bridge the two fields of material science and molecular biology in which there are naturally existing nanobiomachines. The basic mechanism of nanobiomachines is self-assembly/self-organizing. Self-assembly is the major process for synthesizing nanosystems and self-organizing is the major factor for analyzing biological systems.

Formalizing a molecular motor as an automation

The movement process of molecular motors corresponds to the state transition of an automaton and can be formulated by a deterministic finite automaton. Here the condition is that a molecular motor and a kinase pathway are connected.Weconsider a scenario that a kinase pathway activates the movement of a molecular motor and then the molecular motor activates the related pathway after it arrives at the destination. Considering the process that a molecular motor transports a kinase from place x to place y in a cell, we can design the rules of state transition as follows.

Rule 1:

where S0 refers to the state of a pathway at place x and it is activated by a signaling molecule a and activates the kinase k; S1 refers to the state of activation of the kinase k.

Rule 2:

where S2 refers to the state that the kinase k is bound by the molecular motor m at place x.

Rule 3:

where S3 refers to the state that the kinase k is transported by the molecular motor m to place y on the microtubule u.

Rule 4:

where S4 refers to the state that the kinase k activates the pathway q by binding with a signaling molecule c, e.g., in the case that a signaling molecule c is located at the membrane of the cell.

Rule 5:

where S5 refers to the state that the molecular motor m is detached from kinase k at place y. The states are represented by the following strings

The graph of the state transition is given in Fig. 5.

Considering the relation between one kinase and multiple pathways, the linear order of transported kinases n can control as many as the number of pathways whose upper bound is 2n in theory.

Fig. 5. Formalization of a process of a molecular motor.

VI. CONCLUSION

In this paper, we know that molecular synthetic biology provides us with engineering ways to control the communication processes among nanomachines. Studies of the networks existing in cells where the biomolecular signaling processes in natural nanobiosystems are adaptive and robust will bridge the network informatics of cells and the network-based control for nano-communication. With the parallelism structure of network informatics in cells, it becomes possible to regulate signaling pathways and to control communications among molecular motors. Besides, nano technologies also promise new solutions for several applications in biomedical, industrial and military fields.

VII. REFERENCES

Jian-Qin Liu, Kobe Advanced ICT Research Center, National Institute of Information and Communication Technology, 588-2 Iwaoka, Nishi-ku, Kobe, Hyogo, 651-2492, Japan.

Ian F. Akyildiz, Fernando Brunetti, Cristina Blázquez, Nanonetworks: a new communication paradigm, Computer Networks 52 (2008) 2260-2279.

B. Atakan, O.B. Akan, On Channel Capacity and Error Compensation in Molecular Communication, in: Springer Transactions on ComputationalSystems Biology, vol. 10, 2008, pp. 59 - 80.

] I.F. Akyildiz, J.M. Jornet, M. Pierobon, Nanonetworks: a new frontier in communications, Communications of the ACM 54 (2011) 84–89.