rocking the world nano-scale: future internet research for communication networks in medicine,...

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


DESIGN OF NANO-MACHINES

4


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


–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


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


CELLS AS BIOLOGICAL NANOMACHINES

Nucleus and Ribosomes= Biological MemoryAnd Processor

Mitochondria= Biological Battery

Gap Junctions= MolecularTransmitters

Chemical receptors= BiologicalSensors/MolecularReceivers

8


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


BIOLOGICAL NANOMACHINES:BIOLOGICAL BATTERY

Mitochondria obtainenergy by combining:

and synthesizing:

AdenosineTriPhosphate or ATP

–Glucose

–Amino Acids

–Fatty Acids

–Oxygen

10


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


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)


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

13


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

14


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/


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


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.

17

BIOLOGICAL NANOMACHINES:LONG-TERM GOAL (HUMAN CELL ENGINEERING)

Lentivirus


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


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


InjectionLocationInjectionLocation

Targetlocation

Injection Propagation

InjectedParticlesInjectedParticles

Spread ofDrugParticles

Spread ofDrugParticles

Delivery

CardiacInput

CardiacInput

TargetLocationTargetLocation

DrugParticles atTargetedSite

DrugParticles atTargetedSite

ParticleInjection Rate


Drug ParticlesPresence inOther Locations

Drug ParticlesPresence inOther Locations


ParticleInjection RateTransmitterTransmitter ChannelChannel ReceiverReceiver


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


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 Sensor Networks

– Sensor motes based on genetically

engineered bacteria

– Natural interconnection through

molecular communication

22


New genetic code


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)

23



BIOLOGICAL NANOMACHINES:LONG-TERM APPLICATION

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Bacteria-based Sensor Network in the Gastrointestinal Tract

Nanosensor/Actuator/TransceiverEngineeredBacteria

BacteriaPopulation

BacteriaMotion byChemotaxis

Propagation ofMolecularSignal


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.



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



Short Range(nm to µm)

Medium Range(µm to mm)

Long Range(mm to m)

MolecularMotors

CalciumIons

Bacteria Pheromonesand Pollen

27


SHORT-RANGE COMMUNICATIONUSING MOLECULAR MOTORS

28


SHORT-RANGE COMMUNICATIONUSING MOLECULAR MOTORS

Node1

Node2

Node4

Node5

Node6

Molecular Rail

Molecular Motor


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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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


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



MOLECULAR COMMUNICATIONBLOCKS USING MOLECULE DIFFUSION


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

33


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

34


… 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


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




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.

37


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


LONG-RANGE COMMUNICATION USINGPHEROMONES




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.

40


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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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


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


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


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?


* 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


Found analytical closed-form expression of the theoreticalmaximum achievable rate (capacity) in [bits/sec]

Focus on the Diffusion Process propagation

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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


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


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


Fluid FlowFluid Flow

Chamberscontainbacteria


Microfluidic ChannelMicrofluidic Channel

Input MoleculesInput MoleculesOutput MoleculesOutput Molecules

Fluid Flow


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


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


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


A bacteria-based molecularcommunication link

53

ReceiverBacteriaPopulation

TransmitterBacteriaPopulation

Molecule diffusion

ChemicalMessage

Transm.Signal

Recv.Signal

FluorescenceMessage

VALIDATION PLATFORM


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)


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


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)


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


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)


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, ...

59


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


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)


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


Easy to reach the diseased parts

Do not affect healthy cells and body fluids

63

PDDS: WHY NANO-PARTICLES?

1 nm – 100 nm


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.


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


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)


DIAGNOSIS INJECTION PROPAGATION DELIVERY

Diagnosis: Type of the diseases and its location is identified

MODELING THE PDDS



Injection: Appropriate nanoparticles for the disease are injected in the body

MODELING THE PDDS



Propagation: Nanoparticles get dispersed throughout the human body

MODELING THE PDDS



Delivery: Nanoparticles deliver their content to the disease while avoiding healthy parts

MODELING THE PDDS


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


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


ACTION POTENTIAL-TRIGGERED PARTICULATE DRUG

DELIVERY (Year 3)

• Electrical StimuliOptimization

• PNS Interference Study

• Joint Action-Potential and DrugPropagation Optimization


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


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.





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 RateTransmitterTransmitter ChannelChannel ReceiverReceiver

RESULTS:Transfer function, delay and attenuation of Drug Concentration fromInjection to Delivery


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


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


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


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)].


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



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


JunctionJunctionDrug Delivery

RateDrug Delivery

RateDrug Injection

RateDrug Injection

RateBifurcationBifurcationDrug Delivery

RateDrug Delivery

RateDrug Injection

RateDrug Injection

Rate


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



– 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 ?


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



Objective:

To establish the molecular communication fundamentals of intra-bodyparticle propagation in order to design PDDS

85



• E2E PDDS model





• Neuron Model




DELIVERY (Year 3)





– 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.



Objective:


87



• E2E PDDS model





• Neuron Model




DELIVERY (Year 3)





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(%?)


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


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


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



Objective:


92



• E2E PDDS model





• Neuron Model




DELIVERY (Year 3)





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


– 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


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


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


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)


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)


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


1st ACM Annual International Conference onNanoscale Computing and Communication(ACM NANOCOM 2014)

– Atlanta, USA, May 2014

100


A NEW “ELSEVIER” JOURNAL

http://www.elsevier.com/locate/nanocomnet


* 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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