HARDWARE ARCHITECTURE FOR NANOROBOT APPLICATION IN CEREBRAL ANEURYSM

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HARDWARE ARCHITECTURE FOR NANOROBOT
APPLICATION IN CEREBRAL ANEURYSM
Adriano Cavalcanti, Bijan Shirinzadeh, Toshio
Fukuda, Seiichi Ikeda
CAN Center for Automation in Nanobiotech Robotics
Mechatronics Research Lab., Monash
University Dept of Micro-Nano Systems Eng.,
Nagoya University
IEEE NANO 2007 Intl Conf. on Nanotechnology Hong
Kong, China August 2-5, 2007
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The new era of Nanotechnology is coming
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Medical Nanorobot Research Nanorobot Research
Challenge Research Objectives Research
Methodology Computational Analysis Nanorobot
Design Sensing Methodology Control
Model Verification Methodologies Nanorobot IC
Layout Conclusion
P r e s e n t a t i o n o u t l i n e
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1. MEDICAL NANOROBOT RESEARCH
New research subject - Interdisciplinary
focus Natural result from
- Microelectronics miniaturization ?
nanoelectronics
- Quantum Dot new materials
- Genome analysis
- Biomedical Problems
Motivation - Establish Methodologies on
System and Device Prototyping - Control and
Architecture of Nanorobots for Medicine
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2. NANOROBOT RESEARCH CHALLENGE

1. Architecture, sensing and actuation at
   nanoscale - development of molecular
   nanomachine systems ? Possible applications
   - Nanoassembly automation - Health
   care

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3. RESEARCH OBJECTIVES
a. Methodology
- Establish the necessary tools for the
study of nanorobots
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4. RESEARCH METHODOLOGY
a. Characterization of sensor-based events -
Define protein anti-body based signals - Control
modelling - nanorobot behaviours
b. Biomedical Flow Signal

• Finite Element Method (FEM) to study flow
  patterns

c. System Identification and Requirements -
System Modular approach to validate nanorobot
architecture analysis - Verification Hardware
Description Language (VHDL) to verify IC-Layout
Architecture simulation
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5. COMPUTATIONAL ANALYSIS
b. Mobile nanorobot interaction and tasks -
Perform molecular assembly manipulation -
Biomedical engineering applications
c. Storage simulation data - Later
analyses for nanodevices manufacturing - Serves
on sensing/actuation device design - Layout for
DNA based new ICs nanobioelectronics
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6. NANOROBOT DESIGN
a. For Molecular Manipulation nanorobot uses
actuators
nanorobot design
b. Nanorobot navigation - Uses plane
surfaces (three fins total) - Propulsion by
bi-directional propellers two
simultaneously counter-rotating screw drives -
navigational acoustic sensors
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7. SENSING METHODOLOGY
a. Decision planning
Medical target delivery
Motion random, chemical, thermochemical
Behaviour activation
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7. SENSING METHODOLOGY
b. Physical parameters in the simulator
Blood Flow Signal Analysis (FEM) - velocity -
temperature - shear stress - molecular
concentrations
Interactive simulation
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8. CONTROL MODEL
a. Nanorobots Collective Control Planning
determines the kind of behaviour for r.
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w chemical level of the medical target i at
time t.
y surplus/deficit to the desired protein/drug
amount.
Q total of protein captured by r in t.
d desired protein compound rate.
x substance amount injected in the medical
target i.
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8. CONTROL MODEL
b. Signal Sensor - Based Control Reaction
D diffusion coeficient
C molecules concentration per
v flow velocity
molecules per second
r distance from the center of the vessel
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9. VERIFICATION METHODOLOGIES - CASES STUDIES
a. Nanorobots for Cancer -
Surgery / Drug Delivery / Early Diagnosis
Nanorobots searching for malignant tissues
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E-cadherin / bcl-2 gradient changed by tumour
Genome Mapping - Chromosome 21
Constant signal diffusion from injury target
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E-cadherin / bcl-2 protein signals to detect
cancer
signal spreads further throughout vessel
This high constant diffusion could be used as
signals for robots.
Chemical temperature signals activate
nanorobots near target.
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20microns diameter vessel
Comparative behaviors
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b. Nanorobots for Cardiology Blood Pressure
Monitoring / Drug Delivery
Such control activation parameters could be used
for biomedical applications e.g. Coronary
Atherosclerosis
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Nanorobots and Red Blood Cells Near the vessel
occlusion
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c. Nanorobots for Diabetes - Glucose Monitoring
patients must take small blood samples many times
a day to control glucose levels.
Such procedures are uncomfortable and extremely
inconvenient
Nanorobots with nanobiochemosensors (hSGLT3) can
be used for pervasive diabetes monitoring.
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RF are proposed in our nanorobot architecture
for Upload control Data communication Tele-opera
tion
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d. Nanorobots for Brain Aneurysm Early
Diagnosis / Nanowire Delivery
Human Genome Mapping ?
Chromosome 12 DNA Analysis provides antibody
agent for CMOS Electro-Chemical biosensors.
New NanoCMOS IC Design ?
is progressively advancing through integration of
new materials for nanobiosensors and actuator for
biomedical application.
Brain Aneurysm Bulb
Medical Nanorobots ?
Can be applied for cerebral aneurysm with
detection of iNOS (inducible Nitric Oxide
Synthase)
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Nanorobots can enable precise delivery of
nanowires to fill the aneurysm
Diagnosis and detection of vessels dilatation
deformation in early stages is crucial
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iNOS (inducible Nitric Oxide Synthase)
Nanorobots can be used with biosensors to detect
iNOS Signals for diagnosis before a stroke happens
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10. Nanorobot IC Layout
CMOS
CMOS achieved 10nm sizes functionality
Can be used as embedded nanodevice to build
integrated sensors and actuator for nanorobots
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10. Nanorobot IC Layout
CMOS
Photonics Q.D. nanotubes
enable high performance to production of
nanodevices
RF-CMOS with wireless communication is a feasible
way to interface with nanorobots tracking,
operation, diagnosis
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10. Nanorobot IC Layout
CMOS
Nanorobot hardware integrated nanocircuit
architecture
Electromagnetic backpropagation waves are used
to define the nanorobot positions
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Selected Peer Reviewed Publications
Journal
Adriano Cavalcanti, Bijan Shirinzadeh, Robert A.
Freitas Jr., Luiz C. Kretly, Medical Nanorobot
Architecture Based on Nanobioelectronics,
Recent Patents on Nanotechnology, Bentham
Science, Vol. 1, no. 1, pp. 1-10, Feb. 2007.
Adriano Cavalcanti, Robert A. Freitas Jr.,
Nanorobotics Control Design A Collective
Behavior Approach for Medicine, IEEE
Transactions on NanoBioscience, Vol 4., no. 2,
pp. 133-140, Jun. 2005.
Adriano Cavalcanti, Assembly Automation with
Evolutionary Nanorobots and Sensor-Based Control
applied to Nanomedicine, IEEE Transactions on
Nanotechnology, Vol. 2, no. 2, pp. 82-87, Jun.
2003.
Conference
Adriano Cavalcanti, Bijan Shirinzadeh, Declan
Murphy, Julian A. Smith, Nanorobot
for Laparoscopic Cancer Surgery, IEEE-ICIS Intl
Conf. on Computer and Information Science,
Melbourne, Australia, pp. 738-743, Jul. 2007.
Adriano Cavalcanti, Lior Rosen, Bijan
Shirinzadeh, Moshe Rosenfeld, Nanorobot for
Treatment of Patients with Artery Occlusion,
Springer Proceedings of Virtual Concept, Cancun,
Mexico, Nov. 2006.
Adriano Cavalcanti, Warren W. Wood, Luiz C.
Kretly, Bijan Shirinzadeh, Computational
Nanomechatronics A Pathway for Control and
Manufacturing Nanorobots, IEEE CIMCA Intl
Conf. on Computational Intelligence for
Modelling, Control and Automation, IEEE Computer
Society, Sydney, Australia, pp. 185-190, Nov.
2006.
Adriano Cavalcanti, Tad Hogg, Bijan Shirinzadeh,
Nanorobotics System Simulation in 3D Workspaces
with Low Reynolds Number, IEEE-RAS MHS Intl
Symposium on Micro-Nanomechatronics and Human
Science, Nagoya, Japan, pp. 226-231, Nov. 2006.
Arancha Casal, Tad Hogg, Adriano Cavalcanti,
Nanorobots as Cellular Assistants in
Inflammatory Responses, IEEE BCATS Biomedical
Computation at Stanford 2003 Symposium, IEEE
Computer Society, Stanford CA, USA, Oct. 2003.
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Acknowledgments / Technical Collaboration
A. Casal (Stanford University US)
R. A. Freitas Jr. (Inst. for Molecular
Manufacturing US)
T. Hogg (HP US)
L. C. Kretly (Campinas University BR)
D. Murphy (Guys Hospital UK)
L. Rosen (Tel Aviv University IL)
W. W. Wood (Michigan State University US)
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11. CONCLUSION
b. Rapid Evaluation of Various Control Algorithms
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