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Tad Hogg, Ph.D.

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act, e.g., move, release chemicals, communicate. plausible extrapolation of current nanotechnology ... Nanomedicine: Nanotechnology, Biology, and Medicine 2: ... – PowerPoint PPT presentation

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Title: Tad Hogg, Ph.D.


1
Tad Hogg, Ph.D.
  • Member of the Research Staff
  • Hewlett-Packard Laboratories

2
Coordinating Microscopic Robots for Nanomedicine
  • Tad Hogg
  • HP Labs

with Phil Kuekes (HP) Arancha Casal (Stanford
Medical School)
David Sretavan (UCSF)
3
topics
  • microscopic robots
  • physics
  • example task

4
microscopic robots
  • robots with sizes similar to bacteria
  • a micron
  • capabilities
  • sense, e.g., chemicals
  • compute, e.g., pattern recognition
  • act, e.g., move, release chemicals, communicate
  • plausible extrapolation of current nanotechnology

5
size
  • atom (0.1nm)
  • large molecule (1-10nm)
  • virus (102nm)
  • bacteria (103nm)
  • complex cell (104nm)

component, e.g., switch
machine, e.g., computer
conventional semiconductor switches CPUs
100-1000 times larger
6
swarm of microscopic devices
104 1012 devices novel applications from activi
ty of group
not any single device
each device size about 1 micron, mass about
10-12 gram
with molecular electronic components
system design challenge reliable, useful group b
ehavior
in microscopic environments
low Reynolds number fluid flow
chemical diffusion
Brownian motion
7
microscopic robots Why?
  • access space and time scales relevant for
    biology
  • medical research, diagnosis, treatment
  • continuous high-resolution monitoring
  • instead of
  • occasional sampling
  • averaging over many cells behavior
  • study large populations of cells in vivo

8
Why for industry?
  • e.g., molecular electronics at HP
  • crossbar architecture
  • self-assembled molecular devices
  • for memory and logic
  • identify early niche applications
  • limited computation in tiny volume
  • e.g., for biology
  • NOT more computation than Pentium

9
microscopic robots How?
  • caveat not yet possible to fabricate
  • approaches
  • engineer biology
  • analogous to domesticating plants animals
  • de novo fabrication
  • analogous to semiconductor fabs
  • must be cheap to make large numbers
  • cf. transistors

10
How to control?
  • compared to conventional robots
  • different dominant physics
  • much larger numbers of robots
  • wide variety of micro-environments
  • not well-characterized
  • reactive, local control
  • reliability from many simple interactions
  • avoid undesirable emergent behaviors

11
topics
  • microscopic robots
  • physics
  • example task

12
physics of microscopic robots
  • surface dominates volume
  • thermal noise noticeable
  • quantum effects not significant

E. M. Purcell, Life at Low Reynolds Number,
American J. of Physics, 453-11 (1977)
13
physics surface forces
  • high surface to volume ratio
  • high strength, fast dynamics
  • viscous fluid drag, friction
  • force velocity, not F m a
  • inertial forces negligible
  • Newtons F m a

14
physics thermal noise
  • Brownian motion
  • randomly changes location orientation
  • noticeable for microscopic devices
  • limits sensor accuracy

15
physics quantum effects
  • superposition
  • uncertainty
  • interference
  • entanglement
  • not significant for cell-size machines
  • unless specialized hardware
  • robots with quantum computers
  • quantum smart matter

16
power a key constraint
  • to move, communicate, compute,
  • typical task requirements 1-1000pW
  • some sources for this power
  • on-board storage (short-term tasks)
  • glucose (typical blood concentrations)
  • ultrasound

17
power for 1-micron device
  • 1 picowatt (pW) allows
  • 105 logic operations/sec
  • communicating 104 bits/sec over 100mm
  • with ultrasound
  • moving 1mm/sec through water
  • 1000pW from glucoseoxygen in blood
  • compare 10-1000pW use by cells
  • cells are larger 10mm
  • person at rest uses 100 watts

18
topics
  • microscopic robots
  • physics
  • example task

19
evaluate control methods
  • examine various scenarios
  • performance vs. capability tradeoffs
  • e.g., time to finish vs. power use
  • with
  • medical relevance
  • quantified micro-environment
  • low computational cost to simulate

20
task scenarios
  • enhance immune response to injury
  • find source of chemical signal
  • repair damaged nerves
  • identify axons to connect via graft

start with simple parts of overall task
21
task respond to injury
  • monitor for chemical signal
  • follow gradient to source
  • coordinate avoid too many responders!
  • identify infectious microbe
  • pass info to attending physician
  • which immune cells cant do

22
go in, look around, get out, tell me what you fou
nd
and then Ill determine what it means
23
microcirculation
vessels 95 of 500m2 surface area 99 of 5x104 km
length
small vessels exchange chemicals with tissue a
bout 10mm diameter
comparable to size of cells
24
devices within small blood vessels
schematic of one device in 20mm blood vessel
operate in moving fluid crowded with cells vario
us chemicals
fractal branching geometry
cf. artist conceptions often show much more open
space
a simulation environment A. Cavalcanti, www.nanor
obotdesign.com
25
scenario find chemical source
  • 1012 robots in 5-liter blood volume
  • use about 10-5 of blood volume
  • compared to 40 used by red cells
  • total mass of all robots 0.2 g
  • power to move 10-12 watt
  • so if all move at once 1 watt
  • vs. a person at rest using 100 watts

26
benefit of communication
  • detect source somewhat downstream
  • much power to swim back upstream
  • vs. communicate to upstream devices

color indicates chemical concentration
flow, 1mm/s
10 mm
30 mm
source on pipe wall, fluid flow (parabolic
profile), diffusion coef. 300mm2/s
27
comparing control methods
simulation
  • time to reach signal source
  • typical chemical diffusion, fluid flow speed,
    vessel size

random motion
time (seconds)
measure follow chemical gradient
number finding source
Adriano Cavalcanti (Unicamp Univ of Campinas,
Brazil)
28
lessons immune response
  • simple control rules effective
  • redundancy from huge numbers
  • even for source size of just one cell
  • possibly much faster response
  • than immune system
  • devices could act or alert physician

T. Hogg and P. Kuekes, Mobile Microscopic Sensors
for High-Resolution in vivo Diagnostics,
Nanomedicine Nanotechnology, Biology, and
Medicine 2239 2006
29
task nerve repair
  • approaches
  • regeneration via appropriate chemicals
  • repair via replacement with graft tissue

30
go in, find damaged axons, tell me what you find
then Ill think about the situation and tell
you what to fix, then well test your repairs, f
inally get out
31
nervous system
  • cells with long axons
  • up to 1m in length

32
axon injury
synapses lost (Wallerian degeneration)
33
scenario nerve repair
D. Sretavan et al., Neurosurgery 57635 (2005)
junction with exposed axons (only a few shown) 1
0s of microns long and wide
MEMS device
undamaged host
graft, 1cm
undamaged host
in vitro repair demonstrated for single axons
with MEMS in vivo must measure and manipulate
1000 axons in nerve
34
MEMS microsurgery device
D. Sretavan et al., Neurosurgery 57635 (2005)
1mm3 volume view from below axon cutter at cente
r
35
repair process
100mm
1mm
  • remove damaged section
  • replace with graft
  • expose axons in host graft
  • enzymes digest connective tissue
  • place two axons together, electrofuse
  • voltage pulse causes membranes to fuse
  • often gives functional axon

36
coordinate MEMS nano
104 nanorobots
  • nano identify axon type
  • motor, sensory
  • MEMS nano signal through graft
  • to determine matching axon ends
  • big computer determine axons to fuse
  • nano fuse axons
  • MEMS nano test repairs

physician remains in the loop
37
performance tradeoffs
simulation
  • 1000 axons, 104 robots/junction

to repair more uses more movement hence more po
wer
fraction repaired
distance axons moved to fuse (mm)
38
lessons nerve repair
human micro device nano swarm
  • general strategy
  • use devices for detailed look around
  • then compute what to do
  • incorporate relevant clinical constraints
  • use devices as tiny hands
  • MEMS for tissue-scale manipulation
  • fast accurate treatments
  • physician can monitor and control progress

T. Hogg and D. Sretavan, Controlling Tiny
Multi-Scale Robots for Nerve Repair,
Proc. of AAAI-2005
39
validation?
  • difficult
  • cant yet build devices to test
  • many unknown biophysical parameters
  • partial answer robustness
  • achieve task with multiple plausible
  • device capabilities
  • control methods
  • range of task parameters

40
safety
R. Freitas Jr, Nanomedicine IIA
Biocompatibility, 2003
  • biocompatibility
  • time minutes, hours, days, .
  • depending on task
  • reliable controls
  • allow for errors
  • sensor noise, broken devices,

41
further info
T. Hogg, Designing Microscopic Robots for Medical
Diagnosis and Treatment, Nanotechnology
Perceptions 363-73 (2007) T. Hogg and D. Sretav
an, Controlling Tiny Multi-Scale Robots for Nerve
Repair, Proc of AAAI05, 2005 www.hpl.hp.com/res
earch/idl/people/tad R. Freitas Jr., www.nanomedi
cine.com
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