Tad Hogg, Ph.D.

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

2. CoordinatingMicroscopicRobotsforNanomedicine 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. 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: reliable, useful group behavior in microscopic environments • low Reynolds number fluid flow • chemical diffusion • Brownian motion

6. 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

7. topics • microscopic robots • physics • example task

8. 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)

9. topics • microscopic robots • physics • example task

10. 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

11. 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

12. go in, look around, get out, tell me what you found and then I’ll determine what it means

13. 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

14. 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

15. 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

16. 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 Medicine2:239 2006

17. task: nerve repair • approaches • regeneration via appropriate chemicals • repair via replacement with graft tissue

18. 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

19. ~100mm ~1mm nervous system • cells with long axons • up to 1m in length

20. cell death axon injury synapses lost (Wallerian degeneration)

21. D. Sretavan et al., Neurosurgery57:635 (2005) scenario: nerve repair junction with exposed axons (only a few shown) 10s 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

22. D. Sretavan et al., Neurosurgery57:635 (2005) MEMS microsurgery device 1mm3 volume view from below axon cutter at center

23. 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

24. ~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”

25. 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

26. 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

27. 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,…

28. further info • T. Hogg, Designing Microscopic Robots for Medical Diagnosis and Treatment, Nanotechnology Perceptions3:63-73 (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