ROCKING THE WORLD NANO-SCALE:
FUTURE INTERNET RESEARCH FORCOMMUNICATION NETWORKS INMEDICINE, HEALTH AND BEYOND
I. F. AKYILDIZ
Ken Byers Chair Professor in TelecommunicationsGeorgia Institute of Technology
School of Electrical and Computer EngineeringBWN (Broadband Wireless Networking) Lab
Atlanta, GA, USA
IFA’2013 TRONDHEIM 2
I.F. Akyildiz, F. Brunetti, and C. Blázquez,"Nanonetworks: A New Communication Paradigm",Computer Networks Journal, (Elsevier), June 2008.
I.F. Akyildiz, J.M. Jornet and M. Pierobon,"Nanonetworks: A New Frontier in Communications”,Communications of the ACM, 2011.
REFERENCES
IFA’2013 TRONDHEIM
NANOTECHNOLOGY
Enables the control of matter at an atomic and molecular scale:
– At this scale, nanomaterials shownew properties not observedat the microscopic level
– Objective:Exploit these properties & developnew devices and applications
3
1 nm
IFA’2013 TRONDHEIM
Nanomaterial Based Design
Nano-Processor
Nano-AntennaNano-EM
Transceiver
Nanosensors
Nano-Memory
Nano-Power Unit
6 μm
2 μm
1 μm
Bio-inspired Design
Nano-Processor
Nanosensors
Nano-Memory
6 μm
2 μm
100 nm
Nano-Battery
Nano-Antennas
Nano-Transceiver
5
DESIGN OF NANOMACHINES
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–SILICON TECHNOLOGY ERAIS COMING TO AN END (~ 2020-2030)
-MOLECULAR TECHNOLOGY ERAIS STARTING AND WILL BE DOMINATING OURLIVES FOR THE NEXT 80 YEARS ~(2010-onwards)
FUTURE LOOK
6
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BIOLOGY: A RADICALLY DIFFERENTAPPROACH TO NANOMACHINES
Cells are nanoscale-precise biological machines
They communicate and interact/cooperate
7
Eukaryotic Cell Tissue
Eukaryotic Cell
Bacteria Population
Prokaryotic Cell
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CELLS AS BIOLOGICAL NANOMACHINES
Nucleus and Ribosomes= Biological MemoryAnd Processor
Mitochondria= Biological Battery
Gap Junctions= MolecularTransmitters
Chemical receptors= BiologicalSensors/MolecularReceivers
8
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BACTERIA-BASED NANOMACHINES
9
Reuse of entire biological cells
– Through genetic programming ofbacteria plasmids(Synthetic Biology)
DNA in Nucleoidand Plasmids= Nano-Memory/Processor
Cell Membrane= Nano-Power Unit
Flagellum= Nano-Actuator
Protein receptor= Nano-Receivers
Ribosomes= Nano-TransmittersPili
= Nano-Sensors/Actuators
SensingFunction
CommunicationFunctions
ActuationFunction
IFA’2013 TRONDHEIM
BIOLOGICAL NANOMACHINES:BIOLOGICAL BATTERY
Mitochondria obtainenergy by combining:
and synthesizing:
AdenosineTriPhosphate or ATP
–Glucose
–Amino Acids
–Fatty Acids
–Oxygen
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IFA’2013 TRONDHEIM
DNA in the nucleus contains the information
of protein structure (memory)
Ribosomes read and process the DNA
information (processor), synthesize the proteins
Proteins control functionalities of the cell
(e.g., cell signaling (Tx), ligand-binding (Rx), etc.)
BIOLOGICAL NANOMACHINES:BIOLOGICAL MEMORY AND PROCESSOR
Protein
11
Ribosome
Amino Acids
DNA
IFA’2013 TRONDHEIM
Eukaryotic Cells
Prokaryotic Cells
Molecules (Proteins, Ions, Hormones)
Molecules (e.g., Autoinducerexchange for Quorum Sensing)
TxRx
Tx/Rx
Rx/TxRx
Tx
Conjugation
Conjugation
Chemotaxis
12
BIOLOGICAL NANOMACHINES:COMMUNICATION THROUGH MOLECULES
Molecules (DNA plasmids)
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BIOLOGICAL NANOMACHINES:EXAMPLE OF TRANSMITTER
A cell (the transmitter) synthesizes and releases molecules (proteins) inthe medium, as a result of the expression of a DNA sequence.
Outerconcentration Spherical
boundary
Gap junction model
Emission of a particleInnerconcentration
Diffusing particles
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BIOLOGICAL NANOMACHINE:EXAMPLE OF A RECEIVER
Another cell (the receiver) captures those molecules and creates an internalchemical pathway that triggers the expression of other DNA sequences.
Particlerelease
Particlebinding
Chemicalreceptor
Incomingparticles
LIGAND-RECEPTORBINDING
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BIOLOGICAL NANOMACHINES
Can we create man-madebiological nanomachines?
YES!!!
Cells can be “reprogrammed”via DNA manipulation
(genetic engineering)
15
BioBricks Foundation(MIT)
http://biobricks.org/
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BIOLOGICAL NANOMACHINES:SHORTER-TERM GOAL
Genetically program biological cells to perform specific functions
16
Biological Circuits toperform specific
functions
Genetic codewith specific
functions(Plasmid)
GeneticallyEngineeredBacterium
GeneticallyEngineered
Bacteria Population
Observation ofbacteria behavior
Extraction of geneticcode that controls
behavior
Extraction ofcomponents for
specific functions
Buildingblocks ofbiologicalcircuits
IFA’2013 TRONDHEIM
To gen. eng. an eukaryotic cell insert a piece of new DNA code
in the lentivirus cell (special type of a virus used in gen. eng.)
Lentivirus is able to infect the eukaryotic cell, and inject the
new DNA code in the cell DNA
This technique, upon cell replication, would enable the cell reprog.
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BIOLOGICAL NANOMACHINES:LONG-TERM GOAL (HUMAN CELL ENGINEERING)
Lentivirus
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BIOLOGICAL NANOMACHINES APPLICATIONS:ADVANCED HEALTH SYSTEMS
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Cancer MonitoringNetwork
Interconnected IntrabodyNanonetworks
Heart MonitoringNetwork
Blood Sugar MonitoringNetwork
Alzheimer, Epilepsy,Depression Monitoring
Networks
Glucose MonitoringNano-sensors
Interface withExternal Networks
Brain CellsMonitoringNetworks
Brain CellsMonitoringNetworks
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PARTICULATE DRUG DELIVERY
19
Y. Chahibi, M. Pierobon, S. O. Song, and I. F. Akyildiz“A Molecular Communication System Model for Particulate Drug Delivery Systems”IEEE Transaction of Biomedical Engineering (2013)
ParticleDelivery Rate
ParticleDelivery Rate
InjectionLocationInjectionLocation
Targetlocation
Injection Propagation
InjectedParticlesInjectedParticles
Spread ofDrugParticles
Spread ofDrugParticles
Delivery
CardiacInput
CardiacInput
TargetLocationTargetLocation
DrugParticles atTargetedSite
DrugParticles atTargetedSite
ParticleInjection Rate
ParticleInjection Rate
Drug ParticlesPresence inOther Locations
Drug ParticlesPresence inOther Locations
ParticleInjection Rate
ParticleInjection RateTransmitterTransmitter ChannelChannel ReceiverReceiver
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Biological Computing
– Interacting genetically-modified organisms with biological logic gates
– Boolean logic operations within the biochemical environment
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BIOLOGICAL NANOMACHINES:LONG-TERM APPLICATIONS
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Autonomous Control and Decision Networks in Biochemical Environments
– Control of chemical reactions in industrial processes
(e.g., drug synthesis, harmful agents removal)
– Purification of proteins in biology labs
(e.g., quorum sensing-mediated protein copy)
21
BIOLOGICAL NANOMACHINES:LONG-TERM APPLICATIONS
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Biological Sensor Networks
– Sensor motes based on genetically
engineered bacteria
– Natural interconnection through
molecular communication
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BIOLOGICAL NANOMACHINES:LONG-TERM APPLICATIONS
New genetic code
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Natural Bacteria Environments
– Food toxicity monitoring (e.g., yogurt, chicken, etc.)
– Environmental monitoring and cleaning (e.g. water pipes, copper
mines, radioactivity remediation, etc.)
– Intrabody diagnosis and drug delivery networks (e.g., intestine)
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BIOLOGICAL NANOMACHINES:LONG-TERM APPLICATIONS
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BIOLOGICAL NANOMACHINES:LONG-TERM APPLICATION
24
Bacteria-based Sensor Network in the Gastrointestinal Tract
Nanosensor/Actuator/TransceiverEngineeredBacteria
BacteriaPopulation
BacteriaMotion byChemotaxis
Propagation ofMolecularSignal
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COMMUNICATION AMONG BIOLOGICAL NANOMACHINES
All these applications require nanomachines to communicate with each other for
– Relaying and spreading sensory information
– Coordinating to perform complex tasks which go beyond the capability ofa single nanomachine
For this, we need to better investigate their natural communication paradigm:
MOLECULAR COMMUNICATION
25
I. F. Akyildiz, F. Brunetti, and C. Blázquez,
"NanoNetworking: A New Communication Paradigm",
Computer Networks Journal, (Elsevier), June 2008.
IFA’2013 TRONDHEIM
MOLECULAR COMMUNICATION
26
Defined as the transmission and reception of
information encoded in molecules
Defined as the transmission and reception of
information encoded in molecules
An interdisciplinaryfield that spans
Nano, ECE, CS, Bio,Physics, Chemistry,
Medicine, andInformationTechnologies
IFA’2013 TRONDHEIM
MOLECULAR COMMUNICATION
Short Range(nm to µm)
Medium Range(µm to mm)
Long Range(mm to m)
MolecularMotors
CalciumIons
Bacteria Pheromonesand Pollen
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SHORT-RANGE COMMUNICATIONUSING MOLECULAR MOTORS
Node1
Node2
Node4
Node5
Node6
Molecular Rail
Molecular Motor
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Encoding Transmission Propagation Reception Decoding
MOLECULAR COMMUNICATIONBLOCKS USING MOLECULAR MOTORS
Classical Blocks of Communication Theory
Molecular Communication Blocks – Molecular Motors
SelectMolecules torepresentinformation
Encapsulatemolecules into vesicles
attach them tomolecular motors
Molecular motors
travel along
molecular rails
Detach vesiclesfrom
Molecular motors
Extract moleculesfrom vesicles
Interpretreceived
information
frommolecules
characteristics
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IFA’2013 TRONDHEIM 31
Molecular signals (e.g., CA2+ ions) travel through cells gap junctions
SHORT-RANGE COMMUNICATIONUSING MOLECULE DIFFUSION
Cells:Prokaryotic cells-> BacteriaEukaryotic cells-> Muscular tissue
Molecules:Auto-inducersIons (calcium,sodium, potassium)
Tx
Rx
~10 um
Gap junctions
CalciumSignaling
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Molecular signals (e.g., CA2+ ions) travel through cells gap junctions
SHORT-RANGE COMMUNICATIONUSING MOLECULE DIFFUSION
Cells:Prokaryotic cells-> BacteriaEukaryotic cells-> Muscular tissue
Molecules:Auto-inducersIons (calcium,sodium, potassium)
Tx
Rx~50 um
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Encoding Transmission Propagation Reception Decoding
MOLECULAR COMMUNICATIONBLOCKS USING MOLECULE DIFFUSION
Classical Blocks of Communication Theory
Molecular Communication Blocks – Molecule Diffusion-based Communication
Modulatemolecule
concentrationaccording to
theinformation
Emitmodulated
concentration
from gap junctionson the nanomachine
Modulatedconcentration
propagates viamoleculediffusion
Absorb incomingmolecules in the
nanomachine
Sense theirconcentration
through chemicalreceptors
Interpretreceived
information
fromvariations inconcentration
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IFA’2013 TRONDHEIM
MEDIUM RANGE MOLECULAR COMMUNICATIONTHROUGH BACTERIAL CHEMOTAXISM. Gregori and I. F. Akyildiz,"A New NanoNetwork Architecture using Flagellated Bacteria and Catalytic Nanomotors” ,
IEEE JSAC (Journal of Selected Areas in Communications), May 2010.
– Bacteria are microorganisms composed only by one prokaryotic cell
– Flagellum allows them to convert chemical energy into motion
– 4 and 10 flagella (moved by rotary motors, fuelled by chemical compounds)
– Approximately 2 µm long and 1 µm in diameter.
Rx
Conjugation
Conjugation
Chemotaxis
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IFA’2013 TRONDHEIM
… in plasmids or chains of DNA, which contain:– Message to transmit Approx. 600 KB per plasmid
– Active area + Transfer region Regulate bacteria behavior
35
INFORMATION ENCAPSULATION
Transfer Region
Active areaMessage
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MEDIUM RANGE MOLECULAR COMMUNICATIONTHROUGH BACTERIAL CHEMOTAXIS
36
Tx Rx
RX releases chemicalattractant to “guide”the bacterium until itobtains the information
TX inserts the information(plasmid) in the bacterium(conjugation)
ChemicalAttractant
Plasmid
Bacterium moves in aseries of runs and tumbles
< 1 mm
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Encoding Transmission Propagation Reception Decoding
Classical Blocks of Communication Theory
Molecular Communication Blocks – Bacteria Chemotaxis and Conjugation
IntroduceDNA plasmidinside thebacteria’scytoplasm
(conjugation)
Receiver releases attractantsso the bacteria can reach it
Bacteria sense the gradient ofattractant particles
They move towardsthe gradient direction (chemotaxis)
DNA plasmidsextracted from
incoming bacteria(conjugation)
Plasmidsare read
and informationis
interpreted
MEDIUM RANGE MOLECULAR COMMUNICATIONTHROUGH BACTERIAL CHEMOTAXISL.C. Cobo-Rus, and I.F. Akyildiz,“Bacteria-based Communication Networks”,Nano Communication Networks, (Elsevier), December 2010.
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LONG-RANGE COMMUNICATION USING PHEROMONESL. PARCERISA AND I.F. AKYILDIZ,"MOLECULAR COMMUNICATION OPTIONS FOR LONG RANGE NANONETWORKS,"COMPUTER NETWORKS JOURNAL (ELSEVIER), NOV. 2009.
Pheromones are larger molecules which can be propagatedover longer distances through wind (advection)
~ 1-10 m
Pheromones
Tx Rx
Pheromone Antenna Pheromone Antenna
Wind
IFA’2013 TRONDHEIM
Encoding Transmission Propagation Reception Decoding
Classical Blocks of Communication Theory
Molecular Communication Blocks – Molecule Advection and Diffusion
Modulateproduction of
moleculeswith certainChemical
character.
Release thesemoleculesIn the air
Moleculespropagate thanks
to theadvection ofair turbulence
(wind) anddiffusion
Sense incomingmolecules
withchemicalreceptors
InterpretReceived
information
fromchemical charact.
of sensedmolecules
MOLECULAR COMMUNICATION THROUGH PHEROMONESL. Parcerisa and I.F. Akyildiz,"Molecular Communication Options for Long Range Nanonetworks“,Computer Networks (Elsevier) Journal, November 2009.
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IFA’2013 TRONDHEIM
NSF MONACO PROJECTI. F. Akyildiz, F. Fekri, C. R. Forest, B. K. Hammer, and R. Sivakumar,“MONACO: Fundamentals of Molecular Nano-Communication Networks,’’IEEE Wireless Communications Magazine Special Issue on WirelessCommunications at the Nano-Scale, October 2012.
NSF Funding:
– $3M in 4 years (2011-2015)
– 5 PIs in wireless communication and networks, biologyand microfluidic engineering
Project webpage:http://www.ece.gatech.edu/research/labs/bwn/monaco/index.html
This material isbased upon worksupported by theNational ScienceFoundation underGrant No. 1110947
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IFA’2013 TRONDHEIM
NSF MONACO TEAM
42
R. SivakumarI. F. Akyildiz F. Fekri C. R. Forest B. K. Hammer
Principal Investigators
Ph.D. Students and Post Docs
M. Pierobon O. Bicen A. Einolghozati P. Bardill B. KrisnaswamyC. HenegarB. Unluturk
IFA’2013 TRONDHEIM
NSF MONACO PROJECT:SPECIFIC OUTCOMES
Establish the theoreticalfoundations
Design networkarchitectures, modulationschemes and protocols
Develop a molecularcommunication networkbased on geneticallymodified/engineeredprokaryotic cells (bacteria)in a microfluidic device
43
CommunicationModels
Simulation(NS-3)
NetworkArchitectureand Protocols
PhysicalExperiments
Generic
BacteriaCase
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END TO END MODEL (2 NODES)M. Pierobon, and I. F. Akyildiz,“A Physical End to End Model for Molecular Communication in Nanonetworks,’’
IEEE JSAC (Journal of Selected Areas in Communications), May 2010.
44
ChemicalReceptor
DiffusingMolecules
Emission rate attransmitter
Concentrationat receiver
Inputsignal
Outputsignal
EmissionProcess
ReceptionProcess
DiffusionProcess
Modulates emission rate bycontrolling the molecule outputaccording to the input signal
Propagates the modulatedemission rate from thetransmitter to the receiver
Reads the moleculeconcentration and returnsthe output signal
IFA’2013 TRONDHEIM
Molecular communication system components– Molecule emission (modulates rate of molecule release)
– Molecule propagation (diffusion most general)
– Molecule reception (based on chemical reactions)
Attenuation– As high as 150dB in 50µm distance
Delay– In the range of seconds per 50µm
45
WHAT DID WE LEARN?
IFA’2013 TRONDHEIM
* M. Pierobon, and I. F. Akyildiz,
“Diffusion-based Noise Analysis for Molecular Communication in Nanonetworks,’’
IEEE Tr. on Signal Processing, June 2011.
* M. Pierobon, and I. F. Akyildiz,
“Noise Analysis in Ligand-binding Reception for Molecular Communication in
Nanonetworks,’’
IEEE Tr. on Signal Processing, Sept. 2011.
End-to-End Molecular Communication
Transmitter Propagation Receiver
46
NOISE MODELS
Inputsignal
Outputsignal
EmissionProcess
ReceptionProcess
DiffusionProcess
CountingNoise
SamplingNoise
Ligand-receptorKinetic Noise
IFA’2013 TRONDHEIM
Found analytical closed-form expression of the theoreticalmaximum achievable rate (capacity) in [bits/sec]
Focus on the Diffusion Process propagation
47
INFORMATION CAPACITYM. Pierobon and I. F. Akyildiz,“Capacity of a Diffusion-based Molecular Communication System withChannel Memory and Molecular Noise,”
IEEE Tr. on Information Theory, Feb. 2013.(Shorter version appeared in Proc. of IEEE INFOCOM 2011).
Inputsignal
Outputsignal
EmissionProcess
ReceptionProcess
ChannelMemory
CountingNoise
Diffusion Process
Persistency of emitted molecules in thespace
Generated by Molecules subject toBrownian motion
IFA’2013 TRONDHEIM
Theoretical upper bound of the communication performance of a diffusion-based molecular communication
48
Fick’s Diffusion
Molecule LocationDisplacement
Variables
Diffusivity
Temperature
Transmission range
Bandwidth
Transmission power
Receiver radius
INFORMATION CAPACITY
DT
dW
RVR
KB = Boltzmann's constant
G(.) = Gamma function
IFA’2013 TRONDHEIM
MC AMONG BACTERIA POPULATIONS
Information capacity limits of
– Intra-node collective sensing (Quorum Sensing)How bacteria in a population perform sensing and coordinate their actions
– Inter-node communicationHow bacteria transfer information from one population to another
49
A.Einolghozati, M.Sardari, A.Beirami and F.Fekri,“Data Gathering in Networks of Bacteria Colonies: Collective Sensing and Relaying Using MolecularCommunication,”Proc. of 1st NetSciCom Workshop at INFOCOM 2012, Orlando, FL, USA, March 2012
Bacteria PopulationNetwork of Bacteria Populations
IFA’2013 TRONDHEIM 5050
Fluid FlowFluid Flow
Chamberscontainbacteria
Chamberscontainbacteria
Microfluidic ChannelMicrofluidic Channel
Input MoleculesInput MoleculesOutput MoleculesOutput Molecules
Fluid Flow
Chamberscontainbacteria
Microfluidic Channel
Input MoleculesOutput Molecules
Mathematical modeling of microfluidic channelshapes (Building blocks of microfluidic devices)- Frequency Response and Delay
Framework to design microfluidic devices- Optimize microfluidic channel shapes for a
desired spectral output
Mathematical modeling of microfluidic channelshapes (Building blocks of microfluidic devices)- Frequency Response and Delay
Framework to design microfluidic devices- Optimize microfluidic channel shapes for a
desired spectral output
A. O. Bicen, and I. F. Akyildiz,“System-Theoretic Analysis and Least-Squares Design of Microfluidic Channels forFlow-Induced Molecular Communication,”IEEE Tr. on Signal Processing, October 2013.
A. O. Bicen, and I. F. Akyildiz,“System-Theoretic Analysis and Least-Squares Design of Microfluidic Channels forFlow-Induced Molecular Communication,”IEEE Tr. on Signal Processing, October 2013.
MOLECULAR PROPAGATION OVER MICROFLUIDIC CHANNELSMOLECULAR PROPAGATION OVER MICROFLUIDIC CHANNELS
IFA’2013 TRONDHEIM 5151
ttInputInput
ttOutputOutput
SourceSource0,1,1,0,0,1…0,1,1,0,0,1…
DestinationDestinationTRANSMITTERTRANSMITTER
CHANNELCHANNEL
RECEIVERRECEIVER
0,1,1,0,0,1…0,1,1,0,0,1…
tInput
tOutput
Source0,1,1,0,0,1…
DestinationTRANSMITTER
CHANNEL
RECEIVER
0,1,1,0,0,1…
MOLECULAR COMMUNICATIONTHROUGH MICROFLUIDIC CHANNELSMOLECULAR COMMUNICATIONTHROUGH MICROFLUIDIC CHANNELS
Establish the fundamentals of practical molecular nanonetworks
Analyze the channel and information capacity
Design information encoding/decoding techniques, and modulation schemes
Establish the fundamentals of practical molecular nanonetworks
Analyze the channel and information capacity
Design information encoding/decoding techniques, and modulation schemes
IFA’2013 TRONDHEIM
VALIDATION PLATFORM
Bacteria (E. coli) are genetically modified inorder to produce:
– Transmitter bacterium (Tx)
Can only release molecules(autoinducers)
– Receiver bacterium (Rx)
Glows upon the detection ofautoinducers
52
EngineeredDNA plasmid
IFA’2013 TRONDHEIM
A bacteria-based molecularcommunication link
53
ReceiverBacteriaPopulation
TransmitterBacteriaPopulation
Molecule diffusion
ChemicalMessage
Transm.Signal
Recv.Signal
FluorescenceMessage
VALIDATION PLATFORM
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VALIDATION PLATFORMWHAT CAN WE MEASURE?
54
Growth of bacteria in thechamber after seeding (left)and after 11 hours (right)
Fluorescence microscopeimages after seeding (left)and after 11 hours (right)
IFA’2013 TRONDHEIM
NSF MONACO PROJECT:SPECIFIC OUTCOMES
Establish the theoreticalfoundations
Design networkarchitectures, modulationschemes and protocols
Develop a molecularcommunication networkbased on geneticallymodified/ engineeredprokaryotic cells (bacteria)in a microfluidic device
55
CommunicationModels
Simulation(NS-3)
NetworkArchitectureand Protocols
PhysicalExperiments
Generic
BacteriaCase
IFA’2013 TRONDHEIM
For each MC option
(e.g., molecular motors, pheromones, calcium ions)
– End-to-end system design and modelingTransmitter Design (e.g., how pheromones are released)
Receiver Design
(e.g., pheromone antenna with MIMO technology)
Channel Modeling
(e.g., model of meteorological conditions for
pheromone air transport)
and study of attenuation and delay.
56
FUTURE WORK (I)
IFA’2013 TRONDHEIM
FUTURE WORK (II)
For each MC option(e.g., molecular motors, pheromones, calcium ions)
– Noise Analysis(e.g., misrecognition of pheromone types)
– Capacity Analysis(e.g., maximum rate of delivery of pheromone messages)
– Interference Analysis(e.g., how different pheromone sources interfere)
57
IFA’2013 TRONDHEIM
For each MC Option(e.g., molecular motors, pheromones, calcium ions)
– Energy Modeling(e.g., energy for pheromone emission/recognition)
– Modulation/demodulation (e.g., pheromone pulses)
– Addressing (e.g., different pheromone types)
– Packetizing (e.g., information in bursts of pheromones)
58
FUTURE WORK (III)
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NSF MONACO PROJECT:BROADER IMPACT
Significant impact on research in
– Nanotechnology
– Biology
– Information and communication technologies
Define the first steps towards real implementable solutions
Great impact in almost every field of our society
– E.g., healthcare, homeland security and environmental protection, ...
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ANOTHER APPLICATION OF MOLECULARCOMMUNICATION RESEARCH
Particulate Drug Delivery System (PDDS)
– Drug particles are released in the body
– They propagate in the cardiovascular system
– Upon reception at the targeted site, they perform their healing action
Utilize the tools and techniques learnt in the MoNaCo project
to study and enhance Particulate DDSs
60
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ORIGINS OF PDDS
61
Paul Ehrlich (1854-1915):
– He imagined the concept of“magic bullets” (Magische Kugeln)which are the ideal therapeuticagent killing the targeteddiseases without affecting theother healthy parts of the body
Paul EhrlichNobel prize in
Medicine (1908)
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Method to direct drug nanoparticles to the exact
location of the disease at the right concentration
while minimizing the effects on other healthy parts
of the body
62
DEFINITION OF THE PDDS
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Easy to reach the diseased parts
Do not affect healthy cells and body fluids
63
PDDS: WHY NANO-PARTICLES?
1 nm – 100 nm
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Microscopic view of clusters ofdrug-loaded nanoparticles
Biochemists can putpharmaceutical agentsinside nanoparticles
HOW ARE NANOPARTICLES FOR DDS FABRICATED?J. Kost and R. Langer,"Responsive Polymeric Delivery Systems"Advanced Drug Delivery Reviews, 2012.
IFA’2013 TRONDHEIM
Biochemists can engineer drug-loaded nanoparticles of anysize, shape, and chemical properties(reaction rates, absorption rates, affinity with disease parts)
PDDS are engineered in such a way that they– Target the diseases with cell precision
– Survive long enough in the human body
– Not be excreted from the body quickly
– Attach themselves to the diseased cells by ligand-targeting
65
DRUG LOADED NANO-PARTICLES
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Nanoparticles accumulate in diseased parts but not inhealthy parts
Nanoparticles are able to travel from the blood to thediseased parts through damaged vessel walls
Drug-loaded nanoparticles can go inside the diseased cellsand deliver the drug
66
Healthyparts
Diseased parts
Damaged vessel walls
Drug-loadednanoparticles
Normal vessel walls
HOW DOES PDDS WORK?I. Brigger, C. Dubernet P. Couvreur"Nanoparticles in Cancer Therapy and Diagnosis"Advanced Drug Delivery Reviews (2012)
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DIAGNOSIS INJECTION PROPAGATION DELIVERY
Diagnosis: Type of the diseases and its location is identified
MODELING THE PDDS
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DIAGNOSIS INJECTION PROPAGATION DELIVERY
Injection: Appropriate nanoparticles for the disease are injected in the body
MODELING THE PDDS
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DIAGNOSIS INJECTION PROPAGATION DELIVERY
Propagation: Nanoparticles get dispersed throughout the human body
MODELING THE PDDS
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DIAGNOSIS INJECTION PROPAGATION DELIVERY
Delivery: Nanoparticles deliver their content to the disease while avoiding healthy parts
MODELING THE PDDS
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CHALLENGES
Modeling must capture realistic phenomena:
– Time-variance of the blood flow
– Reaction and dispersion of drug particles in the blood
– Individual specificities
– Body clearance mechanisms
– Physiological abnormalities, etc.
71
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MEDDS: MOLECULAR COMMUNICATION FORDRUG DELIVERY SYSTEMSSAMSUNG SAIT (2012-2015)
Objective:
To establish the molecular communication fundamentals of intra-bodyparticle propagation in order to design the PDDS
72
MOLECULAR COMMUNICATIONFOR NANOPARTICLES
PROPAGATION (Year 1 and 2)
• E2E PDDS Model
• Network Performance
• Validation (COMSOL/KMC)
• Use case:Pharmacokinetics
MOLECULAR COMMUNICATIONFOR ACTION-POTENTIALPROPAGATION (Year 2)
• Neuron Model
• Peripheral NervousSystem (PNS) Model
• Network Performance
ACTION POTENTIAL-TRIGGERED PARTICULATE DRUG
DELIVERY (Year 3)
• Electrical StimuliOptimization
• PNS Interference Study
• Joint Action-Potential and DrugPropagation Optimization
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SAMSUNG MEDDS TEAM
73
I. F. Akyildiz
Principal Investigator
Ph.D. Students
M. PierobonUniv. of Nebraska-Lincoln
O. BicenY. Chahibi
Collaborators
S. O. SongSamsung SAIT
IFA’2013 TRONDHEIM
END-TO-END PDDS MODEL
Interferers
Walls Absorption
Receiver
Diseased Part
Channel
Human Body
Interferers
Body Tissues
Packets
Drug-loadedNanoparticles
ReceivedSignal
Drug DeliveryPattern
Interferers
Liver and KidneyClearance
Transmittedsignal
Drug InjectionPattern
Y. Chahibi, M. Pierobon, S. O. Song, and I. F. Akyildiz“A Molecular Communication System Model for PDDS”,IEEE Trans. on Biomedical Engineering, 2013.
74
We abstract elements oftargeted drug delivery systemsas components of a molecularcommunication system.
IFA’2013 TRONDHEIM 75
END-TO-END PDDS MODEL
ParticleDelivery Rate
ParticleDelivery Rate
InjectionLocationInjectionLocation
TargetLocation
Injection Propagation
InjectedParticlesInjectedParticles
Spread ofthe DrugParticles
Spread ofthe DrugParticles
Delivery
CardiacInput
CardiacInput
TargetLocationTargetLocation
DrugParticles atthe TargetedSite
DrugParticles atthe TargetedSite
ParticleInjection
Rate
ParticleInjection
Rate
Drug ParticlesPresence inOtherLocations
Drug ParticlesPresence inOtherLocations
ParticleInjection Rate
ParticleInjection RateTransmitterTransmitter ChannelChannel ReceiverReceiver
RESULTS:Transfer function, delay and attenuation of Drug Concentration fromInjection to Delivery
IFA’2013 TRONDHEIM
Blood Velocity Network
– Models the blood velocity in bloodvessels
– Derived from the Navier-Stokesequations
Drug Propagation Network
– Models the spread of drug in bloodvessels
– Stems from the advection-diffusionequation
76
Injection
Delivery
Propagation
BloodVelocity
(BV)Network
Drug Propagation(DP) Network
Bloodvelocity
PROPAGATION (CHANNEL) MODEL
IFA’2013 TRONDHEIM 77
Drug propagates in the body thanks to the blood velocity
Blood velocity network models the time–varying blood velocity
– Everywhere in the body
– For any individual
(Different persons have different blood flows)
BLOOD VELOCITY NETWORK
IFA’2013 TRONDHEIM 78
BLOOD VELOCITY NETWORK
A complex network comprised of:
– Cardiac Input (Blood velocity at the output of the heart)
– Large and Small Arteries
– Veins
– Other Organs
Final expression of the blood velocity is derived using
tools from transmission line theory
IFA’2013 TRONDHEIM
Predicts the drug concentration in the cardiovascular system
79
Injection Rate
(Blood Velocities)(Injection Site)(Delivery Site)
(Drug Properties)
Drug PropagationNetwork
DrugDelivery Rate
DRUG PROPAGATION NETWORK
Concentration of particles flowing in the blood per second.Unit is [mol/(m^3)].
IFA’2013 TRONDHEIM 80
Comprised of Network Links (Vessels) and Network Nodes (Bifurcations)
DrugDeliveryRate
DrugDeliveryRate
DrugInjection
Rate
DrugInjection
Rate
NetworkSourceNetworkSource
NetworkDestinationNetwork
Destination
Network Node= BifurcationNetwork Node= Bifurcation
NetworkLink
= BloodVessel
NetworkLink
= BloodVessel
Drug Propagation Network(Advection-Diffusion)
Drug Propagation Network(Advection-Diffusion)
DP depends on the:
– Topology of thecardiovascular system
– Body temperature
– Drug radius
– Drug shape
– Blood viscosity
DRUG PROPAGATION NETWORK
IFA’2013 TRONDHEIM
Derived the impulse responseof a blood vessel,bifurcation, and junction asexpressions of:
– Blood velocity
– Diffusion coefficient
– Temperature
– Viscosity
– Length
– Radius
81
Drug InjectionRate
Drug InjectionRate
LinkLink
Drug DeliveryRate
Drug DeliveryRate
DRUG PROPAGATION NETWORK
JunctionJunctionDrug Delivery
RateDrug Delivery
RateDrug Injection
RateDrug Injection
RateBifurcationBifurcationDrug Delivery
RateDrug Delivery
RateDrug Injection
RateDrug Injection
Rate
IFA’2013 TRONDHEIM
By combining the Drug Propagation
Network and Blood Velocity Network
we obtain the transfer function of
the overall network
Predict the Drug Delivery Rate
82
Injection
Delivery
Propagation
BloodVelocity
(BV)Network
Drug Propagation(DP) Network
BloodVelocity
END-TO-END PDDS MODEL
IFA’2013 TRONDHEIM
– Molecular Communication is a flexible, accurate, and efficientparadigm to model the PDDS
Flexible Impulse responses are easy to manipulate analytically Efficient Analytical solutions are much faster than Monte-Carlo or finite
element methodsAccurat Good agreement with finite element and Monte-Carlo results
Implications:- Drug delivery can be optimized– It provides better insight about intra-body communication
83
WHAT DID WE LEARN ?
IFA’2013 TRONDHEIM 84
Controlling the quantity and the timing of thedrug injection to optimize the healing
Developing personalized drug delivery systems
Providing an alternative to in-vivo testing ofdrugs using the drug propagation simulation tool
IMPACT ON HEALTH AND MEDICINE
IFA’2013 TRONDHEIM
MEDDS: MOLECULAR COMMUNICATION FORDRUG DELIVERY SYSTEMSSAMSUNG SAIT (2012-2015)
Objective:
To establish the molecular communication fundamentals of intra-bodyparticle propagation in order to design PDDS
85
MOLECULAR COMMUNICATIONFOR NANOPARTICLES
PROPAGATION (Year 1 and 2)
• E2E PDDS model
• Network Performance
• Validation (COMSOL/KMC)
• Use case:Pharmacokinetics
MOLECULAR COMMUNICATIONFOR ACTION-POTENTIALPROPAGATION (Year 2)
• Neuron Model
• Peripheral NervousSystem (PNS) Model
• Network Performance
ACTION POTENTIAL-TRIGGERED PARTICULATE DRUG
DELIVERY (Year 3)
• Electrical StimuliOptimization
• PNS Interference Study
• Joint Action-Potential and DrugPropagation Optimization
IFA’2013 TRONDHEIM 86
– Noise: Brownian Motion
– Information-Theoretical Capacity:
the maximum amount of drugs that can reliably be
delivered from injection point to the target point
– Delay:
Time required for a drug to attain the delivery location
COMMUNICATION ASPECTS OF PDDSY. Chahibi, I.F. Akyildiz and S. O. Song,“Molecular Communication Noise and Capacity Analysis for Drug Delivery Systems”
Submitted for journal publication.
IFA’2013 TRONDHEIM
MEDDS: MOLECULAR COMMUNICATION FORDRUG DELIVERY SYSTEMSSAMSUNG SAIT (2012-2015)
Objective:
To establish the molecular communication fundamentals of intra-bodyparticle propagation in order to design PDDS
87
MOLECULAR COMMUNICATIONFOR NANOPARTICLES
PROPAGATION (Year 1 and 2)
• E2E PDDS model
• Network Performance
• Validation (COMSOL/KMC)
• Use case:Pharmacokinetics
MOLECULAR COMMUNICATIONFOR ACTION-POTENTIALPROPAGATION (Year 2)
• Neuron Model
• Peripheral NervousSystem (PNS) Model
• Network Performance
ACTION POTENTIAL-TRIGGERED PARTICULATE DRUG
DELIVERY (Year 3)
• Electrical StimuliOptimization
• PNS Interference Study
• Joint Action-Potential and DrugPropagation Optimization
IFA’2013 TRONDHEIM
CASE: PHARMACOKINETICSY. Chahibi, I.F. Akyildiz and S. O. Song,“Pharmacokinetics use case of Molecular Communication for Drug Delivery Systems”Submitted for journal publication.
88
Allow to map our communicationmodel to pharmacologists
– Where does the drug go?
– How much of it reacts?
– How much is absorbedby healthy parts?
– How do cardiovascular diseasesaffect the drug distribution?
Healthy Vessel(%?)
Diseased Vessel(%?)
ReactedParticles
(%?)
Wall Absorption(%?)
Wall Adsorption(%?)
IFA’2013 TRONDHEIM 89
Blood vessel damage can severely affectthe drug delivery
How can we model the damaged vessels?
A healthy blood vessels can be modeled asan electrical circuit with: Resistance Blood Viscosity Inductance Blood Inertia Capaciance Vessel Elasticity
We propose equivalent electricalcircuits for damaged blood vessels andstudied their effect on drug propagation
Healthy(No leakage)
(No conductance)
Diseased part(With leakage)
Add a parfallel conductanceto the helthy model
Arteriosclerosis(No elasticity)
(No conductance)
CASE: PHARMACOKINETICS
Electrical equivalents forcardiovascular diseases: Tumorvessels small holes
Fluid leakage Conductance
Arteriosclerosis Hardenedvessel walls No (remove) capacitance
IFA’2013 TRONDHEIM 90
CASE: PHARMACOKINETICSWHAT BODY DOES TO THE DRUG?
We extend the drug propagationnetwork to account for realisticphenomena that occur in nanoparticles
– Reaction– Absorption– Adsorption
We derived expressions for thequantity of drugs that is absorbed bytissues, the quantity that reacts with theblood, etc.
1
2
4
5
3
6
7
Healthy
Tree of bloodvessels
Fractions ofdrugs
reacting,absorbed bytissues and,remaining inthe blood.
Injection
IFA’2013 TRONDHEIM 91
Predicting what happens to drug-loaded nanoparticleson their way to the diseased parts
Studying the effect of abnormal body conditions(damaged blood vessels, high blood pressure, etc.)on drug delivery
Evaluating how much of the drug is excreted fromthe body
IMPACTON HEALTH AND MEDICINE
IFA’2013 TRONDHEIM
MEDDS: MOLECULAR COMMUNICATION FORDRUG DELIVERY SYSTEMSSAMSUNG SAIT (2012-2015)
Objective:
To establish the molecular communication fundamentals of intra-bodyparticle propagation in order to design PDDS
92
MOLECULAR COMMUNICATIONFOR NANOPARTICLES
PROPAGATION (Year 1 and 2)
• E2E PDDS model
• Network Performance
• Validation (COMSOL/KMC)
• Use case:Pharmacokinetics
MOLECULAR COMMUNICATIONFOR ACTION-POTENTIALPROPAGATION (Year 2)
• Neuron Model
• Peripheral NervousSystem (PNS) Model
• Network Performance
ACTION POTENTIAL-TRIGGERED PARTICULATE DRUG
DELIVERY (Year 3)
• Electrical StimuliOptimization
• PNS Interference Study
• Joint Action-Potential and DrugPropagation Optimization
IFA’2013 TRONDHEIM 93
CHALLENGES OF MOLECULAR COMMUNICATIONMODELING FOR ACTION-POTENTIAL PROPAGATION
Mathematical modeling is complex because of thenonlinear effects in neural cells
Propagated action-potential should not affect thenormal biological functions of the body
Many neural mechanisms are not deeply understoodbecause action-potentials are difficult to measure onthe human body
IFA’2013 TRONDHEIM 94
– Transmitter:The signal is transmitted bypotential stimulation by an electricalexcitation
– Channel:First, the electrical propagation inion-channels along the neuron cellSecond, the diffusion ofneurotransmitters in the gapbetween two neuron cells
– Receiver:The signal is received by voltageinduction at the receiver neuron cell
MOLECULAR COMMUNICATIONFOR ACTION-POTENTIAL PROPAGATIONY. Chahibi, I.F. Akyildiz and S. O. Song,
“An Action-Potential Molecular Communication Model”, in preparation
IFA’2013 TRONDHEIM 95
Outcomes:– Transfer function,
attenuation, delay, betweentwo extremities of a neuralnetwork
– Optimization of the electricalstimuli, and the location ofthe source and the destination
– Triggering of the chemicalactivity of drugs through theaction potential
MOLECULAR COMMUNICATIONFOR ACTION-POTENTIAL PROPAGATION
IFA’2013 TRONDHEIM 96
Targeting neural diseases (Epilepsy, Depression,Alzheimer, …) using the action-potential
Combining particulate drug delivery with action-potential triggering
Analyzing human cells that produce electrical signals(action-potentials), such as heart and brain cells
IMPACT OF MC ACTION-POTENTIALPROPAGATION ON THE MEDICAL WORLD
IFA’2013 TRONDHEIM
RECOGNITION OF THE WORK ONINTERCELLULAR COMMUNICATION
97
2013 Nobel Prize in Medicine or Physiology
– J. E. Rothman, R. Schekman and T. C. Südhof:
– Distinguished for “their discoveries of
machinery regulating vesicle traffic,
a major transport system in our cells”
Laureates of theNobel prize inMedicine or
Physiology (2013)
IFA’2013 TRONDHEIM
RECOGNITION OF THE WORK ONINTERCELLULAR COMMUNICATION
98
2013 Nobel Prize in Chemistry
– Arieh Warshel, Martin Karplus and Michael Levitt:
– Distinguished for “the development of
multiscale models for complex chemical systems”
Laureates of theNobel prize in
Chemistry (2013)
IFA’2013 TRONDHEIM 99
IEEE Int. Workshop on Molecular and Nano-scale Communication(MoNaCom)
1. IEEE Infocom Conf., in Shanghai, China, April 2011.
2. IEEE ICC 2012 Conf., in Ottawa, Canada, June 2012.
3. IEEE ICC 2013 Conf., in Budapest, Hungary, June 2013.
IEEE Int. Workshop on Molecular and Nano-scale Communication(MoNaCom)
1. IEEE Infocom Conf., in Shanghai, China, April 2011.
2. IEEE ICC 2012 Conf., in Ottawa, Canada, June 2012.
3. IEEE ICC 2013 Conf., in Budapest, Hungary, June 2013.
CONFERENCE ACTIVITIES
IFA’2013 TRONDHEIM
1st ACM Annual International Conference onNanoscale Computing and Communication(ACM NANOCOM 2014)
– Atlanta, USA, May 2014
100
IFA’2013 TRONDHEIM 102
* N3Cat: NaNoNetworking Research Center, (since 2007)UPC, Barcelona, Spain.
* NANO-KAU, (since 2011)NanoCom Center at King Abdulaziz University, Jeddah, KSA.
* FiDiPro: Finnish Distinguished Professorship for NANOCOM,TUT, Tampere, Finland. (since Sept. 2012)
* N3Cat: NaNoNetworking Research Center, (since 2007)UPC, Barcelona, Spain.
* NANO-KAU, (since 2011)NanoCom Center at King Abdulaziz University, Jeddah, KSA.
* FiDiPro: Finnish Distinguished Professorship for NANOCOM,TUT, Tampere, Finland. (since Sept. 2012)
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