Transcript Document

Tad Hogg, Ph.D.
Member of the Research Staff
Hewlett-Packard Laboratories
Coordinating Microscopic
Robots for Nanomedicine
Tad Hogg
HP Labs
with
Phil Kuekes (HP)
Arancha Casal (Stanford Medical School)
David Sretavan (UCSF)
topics
• microscopic robots
• physics
• example task
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
swarm of microscopic
devices
104 – 1012 devices
novel applications
from activity of group
not any single device
each device: size about 1 micron, mass about 10-12 gram
with molecular electronic components
system design challenge:
• low Reynolds number fluid flow
reliable, useful group behavior • chemical diffusion
in microscopic environments • Brownian motion
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
topics
• microscopic robots
• physics
• example task
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, 45:3-11 (1977)
topics
• microscopic robots
• physics
• example task
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
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 can’t do
go in, look around, get out,
tell me what you found
and then I’ll determine what it means
microcirculation
vessels <0.1mm diameter:
~10% total blood volume
~95% of ~500m2 surface area
>99% of ~5x104 km length
small vessels
- exchange chemicals with tissue
- about 10mm diameter
- comparable to size of cells
devices within small blood vessels
schematic of one device in ~20mm blood vessel
operate in moving fluid
crowded with cells
various chemicals
fractal branching geometry
cf. artist conceptions often
show much more open space
a simulation environment
A. Cavalcanti, www.nanorobotdesign.com
benefit of communication
• detect source somewhat downstream
– much power to swim back upstream
– vs. communicate to upstream devices
10 mm
color indicates chemical concentration
flow, ~1mm/s
30 mm
source on pipe wall, fluid flow (parabolic profile), diffusion coef. = 300mm2/s
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 2:239 2006
task: nerve repair
• approaches
– regeneration via appropriate chemicals
– repair via replacement with graft tissue
go in, find damaged axons,
tell me what you find
then I’ll think about the situation
and tell you what to fix,
then we’ll test your repairs,
finally get out
nervous system
• cells with long axons
– up to 1m in length
~1mm
~100mm
axon injury
synapses lost
(Wallerian
degeneration)
cell death
D. Sretavan et al., Neurosurgery 57:635 (2005)
scenario: nerve repair
junction with exposed axons
(only a few shown)
10s of microns long and wide
MEMS device
in vitro: repair demonstrated for single axons with MEMS
in vivo: must measure and manipulate ~1000 axons in nerve
D. Sretavan et al., Neurosurgery 57:635 (2005)
MEMS microsurgery device
1mm3 volume
view from below
axon cutter at center
repair process
• remove damaged section
~1mm
~100mm
– 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
~104 nanorobots
coordinate MEMS & nano
• 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”
human + micro device + nano swarm
lessons: nerve repair
• 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
validation?
• difficult
– can’t 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
R. Freitas Jr, Nanomedicine IIA: Biocompatibility, 2003
safety
• biocompatibility
– time: minutes, hours, days, ….
• depending on task
• reliable controls
– allow for errors
• sensor noise, broken devices,…
further info
T. Hogg, Designing Microscopic Robots for Medical
Diagnosis and Treatment, Nanotechnology
Perceptions 3:63-73 (2007)
T. Hogg and D. Sretavan, Controlling Tiny Multi-Scale
Robots for Nerve Repair, Proc of AAAI05, 2005
www.hpl.hp.com/research/idl/people/tad
R. Freitas Jr., www.nanomedicine.com