Simulator Development for Neuron-based Molecular Communication

1. Simulator Development forNeuron-basedMolecular Communication • Jun Suzuki • UMass Boston

2. Background

3. Nanoscale Communication • An emerging research paradigm that aims to provide communication capabilities between nanoscale machines (nanomachines). • The first publication at 2005 • Nanomachine • The most basic functional unit in nanoscale systems. • Scale: one to a few hundred nanometers. • Consists of biological materials (e.g., molecules) and perform very simple computation, sensing and/or actuation tasks.

4. Nanomachines

5. Two Major Approaches toNanoscale Communication • Electromagnetic communication • Top-down miniaturization approach • Molecular communication • Bottom-up approach • Inspired by the communication mechanisms that naturally occur among living cells. • Utilizes molecules as a communication medium. • Several advantages over electromagnetic communication • Inherent nanometer scale, biocompatibility and energy efficiency • A major application domain: in-body nanonetworks • Nanomachines are networked through molecular communication to perform sensing and actuation tasks in the body for biomedical and prosthetic purposes. • e.g., vital information sensing, targeted drug release and neural signal augmentation.

6. Short- and Long-rangeMolecular Communication • Short-range communication • nanometers to millimeters • Uses molecular motors, calcium signaling and bacteria communication • Long-range communication • millimeters to meters • Uses neurons.

7. Neurons • Fundamental components of the nervous system. • The central nervous system • the brain, spinal cord and retina • The peripheral nervous system • sensory neurons, clusters of neurons called ganglia • Electrically excitable cells • Process and transmit information by electrical and chemical signaling.

8. The Structure of an Neuron • Cell body (soma) • 4 to 100 um in diameter • Dendrites • Up to a few hundred um in length • Thin structures that arise from the soma • Form a complex “dendritic tree.” • the majority of inputs to a neuron • Generate the majority of inputs to a neuron. Dendrite Axon terminal Node of ranvier Soma Axon Shwann cell Nucleus Myelin sheath

9. An axon • Up to 1 m in humans • A special cellular extension that arises from the soma. • Branches hundreds, or even thousands, of times before it terminates. • Travels thru the body in bundles called nerves. Dendrite Axon terminal Node of ranvier Soma Axon Shwann cell Nucleus Myelin sheath

10. Neuronal Network • Neurons self-organize to connect each other and form a network.

11. Neuron-to-Neuron Communication • Neurons communicate with others via synapses. • A synapse • A membrane-to-membrane junction • Contains molecular machinery that allows a (presynaptic) neuron to transmit a chemical signal to another (postsynaptic) neuron. • An axon makes thousands of synaptic contacts. Signal Synapse Presynaptic Neuron Postsynaptic Neuron

12. Neurotransmitters • When an axon terminal is stimulated with an electric signal, it initiates a chemical process in a synapse. • Generates neurotransmitter molecules. • e.g., Acetylcholine (ACh) • Neurotransmitters electrically excites a postsynaptic neuron.

13. Action Potential • When a neuron is electrically excited, it generates an electrochemical pulse called action potential. • The refractory period follows a signal transmission. • Signal transmission speed: 90 m (295 fts, 98 yds)/second

14. Neuron-based IBSANs

15. The Proposed Communication Framework for Neuron-based IBSANs • Neuronal TDMA • TDMA (Time Division Multiple Access) protocol framework

16. TDMA Optimization • Which neuron should start signaling in a given time slot? • Assumption: Each neuron transmits at least one signal in the signaling cycle. • Optimization objectives • Signaling yield • How many signals the sink receives from all sensors during the scheduling cycle. • Signaling fairness among sensors • How fairly/equally sensors access the shared neuronal network. • Signaling delay • A multiobjective optimization problem

17. Current Status • A multiobjective genetic algorithm (GA) has been developed and tested. • Can optimize TDMA scheduling by balancing the trade-offs among 3 objectives.

18. What’s the Future of MolCom? • Nanomachine development has been actively carried out in the academia and industry. • Molecular communication protocols/mechanisms have been studied through simulations in the academia since mid ’00. • Practical nanomachines will be developed in 10 (?) years. • Molecular communication will be integrated with nanomachines. • Applications • Metical monitoring • Prosthetic and rehabilitation devices

19. Cellular System Development and TDMA • TDMA • is/was used in 2G cellular systems such as GSM. • is still widely used in satellite systems. • was extensively studied in the academia for wireless communication in 70s and early 80s • through simulations. • GSM • The standardization of GSM started in ’82 and completed in ’90. • The first GSM call in 1991. • Practically usable cell phones became available on the market around 1993 - 1995. • GSM subscribers worldwide passed 10M in 1995, 100M in 1998, 500M in 2001, 1B in 2004 and 2B in 2008. • Has been fading out as 3G and 4G systems emerge.

20. CS682/3 Project:A Generic Simulation Environment forNeuronal TDMA

21. Architectural Overview • Neuronal Network Visualizer/Editor • Aids the user to manually draw neuronal networks in 2D/3D • Performs several visualization effects • e.g., zoom in, zoom out, etc. • Generates/serializes a text file (in XML?) according to a drawn neuronal network and stores it in the local disk or a remote cloud (e.g., Dropbox and Google Drive). • Reads a text file and visualize a neuronal network. Topology Data Generator Optimization Code Opt. Algo. (NSGA-II) Opt. Engine (jMetal) Topology Data (XML?) GUI Frontend Topology Data Reader Optimization Code Opt. Algo. (Simplex) Opt. Engine (GLPK) Manual 2D/3D topology editor Automatic Topology Generator

22. Automatic Topology Generator • Artificially grow neuronal networks based on a topology construction algorithm(s) and serializes their topology information in text files (XML?). • Optimization Code • I/F code to run optimization algorithms with a given neuronal network topology. Topology Data Generator Optimization Code Opt. Algo. (NSGA-II) Opt. Engine (jMetal) Topology Data (XML?) GUI Frontend Topology Data Reader Optimization Code Opt. Algo. (Simplex) Opt. Engine (GLPK) Manual 2D/3D topology editor Automatic Topology Generator

23. What You can Gain from this Project. • Extensive Java programming • GUI, 2D/3D visualization, text data handling, optimization, self-organizing topology formation • Very futuristic research experience • Potential co-authored publications (if you are interested)