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Cooperative Control Design for Nanorobots in Drug Delivery

Chapter

Abstract

In this chapter, we present cooperative control strategies for multi-robots to deliver drugs in tumor environments. We first discuss a nanorobot architecture, including chemical sensors, actuators, power supply, and data transmission, which is supported by the state of the art of nanotechnology. We then review tumor microenvironment modeling and pH measurement, where a tumor pH diffusion model is introduced and the pH value profile is established in the tumor environment. Based on the mathematical modeling, we propose a cooperative control strategy for pH sensitive nanorobots to deliver drugs in such environments. The control law is composed of gradient estimation and cooperative control, where the robots cooperatively estimate the gradient of the center of the group based on individual pH measurement, and then move towards the tumor center in a formation. We conduct rigorous convergence analysis and prove that the designed control steers the group of the robots reaching the cancer cells with the lowest pH value in the presence of estimation errors. Numerical simulations have shown effectiveness of the algorithm.

Keywords

Formation Center Complementary Metal Oxide Semiconductor Necrotic Core Chemical Exchange Saturation Transfer Cooperative Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Partial support for this work was provided by the National Science Foundation’s Course, Curriculum, and Laboratory Improvement (CCLI) program under Award No. 0837584. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References

  1. 1.
    Lamprecht A, Ubrich N, Yamamoto H, Schafer U, Takeuchi H, Maincent P, Kawashima Y, Lehr CM (2001) Biodegradable nanoparticles for targeted drug delivery in treatment of inflammatory bowel disease. J Pharm Exp Ther 299(2):775–781Google Scholar
  2. 2.
    Ummat A, Sharma G, Mavroidis C, Dubey A (2005) Biomedical engineering handbook, chapter Bio-nanorobotics: state of the art and future challenges. CRC Press, LondonGoogle Scholar
  3. 3.
    Cavalcanti A, Shirinzadeh B, Fukuda T, Ikeda S (2009) Nanorobot for brain aneurysm. Int J Robot Res 28(4):558–570Google Scholar
  4. 4.
    Cavalcanti A, Shirinzadeh B, Kretly L (2008) Medical nanorobotics for diabetes control. Nanomedicine 4(2):127–138Google Scholar
  5. 5.
    Cavalcanti A, Shirinzadeh B, Freitas R, Hogg T (2008) Nanorobot architecture for medical target identification. Nanotechnol 19(1):1–15Google Scholar
  6. 6.
    Cavalcanti A, Freitas R (2005) Nanorobotics control design: a collective behavior approach for medicine. IEEE Trans Nanobiosci 4(2):133–140Google Scholar
  7. 7.
    Cavalcanti A (2003) Assembly automation with evolutionary nanorobots and sensor-based control applied to nanomedicine. IEEE Trans Nanotechnol 2(2):82–87Google Scholar
  8. 8.
    Freitas R (2005) What is nanomedicine? Nanomed 1(1):2–9Google Scholar
  9. 9.
    Cavalcanti A, Freitas R (2002) Autonomous multi-robot sensor-based cooperation for nanomedicine. Int J Nonlinear Sci Numer Simul 3(4):743–746Google Scholar
  10. 10.
    Lewis M, Bekey G (1992) The behavioral self-organization of nanorobots using local rules. In: Proceedings of IEEE international conference on intelligent robots and systems, pp 1333–1338Google Scholar
  11. 11.
    Chandrasekaran S, Hougen D (2006) Swarm intelligence for cooperation of bio-nano robots using quorum sensing. In Bio micro and nanosystems conference, p 104, San Francisco, June 2006Google Scholar
  12. 12.
    Pawel P, Bermdez I Badia S, Bernardet U, Knsel P, Carlsson M, Gu J, Chanie E, Hansson BS, Tim C, Pearce, Verschure PFMJ (2006) An artificial moth: chemical source localization using a robot based neuronal model of moth optomotor anemotactic search. Auton Robots 20:197–213Google Scholar
  13. 13.
    Macnab RM, Koshland DE (1972) The gradient-sensing mechanism in bacterial chemotaxis. In: Proc National Academy of Sciences of the USA 69(9):2509–2512Google Scholar
  14. 14.
    Wadhams George H, Armitage Judith P (2004) Making sense of it all: bacterial chemotaxis. Nat Rev Mol Cell Biol 5(12):1024–1037Google Scholar
  15. 15.
    Freitas R (1999) Nanomedicine, volume I: basic capabilities. Landes Bioscience, GeorgetownGoogle Scholar
  16. 16.
    Patolsky F, Lieber C (2005) Nanowire nanosensors. Mater Today 8(4):20–28Google Scholar
  17. 17.
    Cui Y, Wei Q, Park H, Lieber C (2001) Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species. Science 293(5533):1289–1292Google Scholar
  18. 18.
    Hahm J, Lieber C (2004) Direct ultrasensitive electrical detection of dna and dna sequence variations using nanowire nanosensors. Nano Lett 4(1):51–54Google Scholar
  19. 19.
    Grosios K, Traxler P (2003) Tyrosine kinase targets in drug discovery. Drugs Futur 28(7):679Google Scholar
  20. 20.
    Kishimoto J, Spurr N, Liao M, Lizhi L, Emson P, Xu W (1992) Localization of brain nitric oxide synthase (nos) to human chromosome 12. Genomics 14(3):802–804Google Scholar
  21. 21.
    Wright E, Sampedro A, Hirayama B, Koepsell H, Gorboulev V, Osswald C (2005) Novel glucose sensor. US patent, 0267154Google Scholar
  22. 22.
    Che J, Cagin T, Goddard W (2000) Thermal conductivity of carbon nanotubes. Nanotechnology 11(7):65–69Google Scholar
  23. 23.
    Thostenson E, Ren Z, Chou T (2001) Advances in the science and technology of carbon nanotubes and their composites: a view. Compos Sci Technol 61(13):1899–1912Google Scholar
  24. 24.
    Thostenson E, Li C, Chou T (2005) Nanocomposites in context. Compos Sci Technol 65(3–4):491–516Google Scholar
  25. 25.
    Tombler T, Zhou C, Alexseyev L (2000) Reversible electromechanical characteristics of carbon nanotubes under local-probe manipulation. Nature 405:769–672Google Scholar
  26. 26.
    Yoon H, Xie JN, Abraham JK, Varadan VK, Ruffin PB (2006) Passive wireless sensors using electrical transition of carbon nanotube junctions in polymer matrix. Smart Mater Struct 15(1):s14–s20Google Scholar
  27. 27.
    Levitsky IA, Kanelos P, Euler WB (2004) Electromechanical actuation of composite material from carbon nanotubes and ionomeric polymer. J Chem Phys 121(2):1058–1065Google Scholar
  28. 28.
    Kim P, Lieber CM (1999) Nanotube nanotweezers. Science 286(5447):2148–2150Google Scholar
  29. 29.
    Wei BQ, Vajtai R, Ajayan PM (2001) Reliability and current carrying capacity of carbon nanotubes. Appl Phys Lett 79(8):1172–1174Google Scholar
  30. 30.
    Jenkner M, Tartagni M, Hierlemann A, Thewes R (2004) Cell-based cmos sensor and actuator arrays. IEEE J Solid-State Circuits 39(12):2431–2437Google Scholar
  31. 31.
    Astier Y, Bayley H, Howorka S (2005) Protein components for nanodevices. Curr Opin Chem Biol 9(4):576–584Google Scholar
  32. 32.
    Seeman N (2005) From genes to machines: Dna nanomechanical devices. Trends Biochem Sci 30(3):119–125Google Scholar
  33. 33.
    Sauer A, Stanacevic M, Cauwenberghs G, Thakor N (2005) Power harvesting and telemetry in cmos for implanted devices. IEEE Trans Circuits Syst 52(12):2605–2613Google Scholar
  34. 34.
    Mohseni P, Najafi K, Eliades S, Wang X (2005) Wireless multichnnel biopotential recording using an integrated fm telemetry circuit. IEEE Tans Neural Syst Rehabil Eng 13(3):263–271Google Scholar
  35. 35.
    Norris T (2006) Nanoacoustics: towards imaging nanostructures using picosecond ultrasonics. J Acoust Soc Am 119(5):3284–3285Google Scholar
  36. 36.
    Solberg JR, Lynch KM, Maciver MA (2008) Active electrolocation for underwater target localization. Int J Robot Res 27(5):529–548Google Scholar
  37. 37.
    Horiuchi TK, Cummings RE (2004) A time-series novelty detection chip for sonar. Int J Robot Autom 19(4):171–177Google Scholar
  38. 38.
    Ieee standard for safety levels with respects to human exposure to rf electromagnetic fields 3kHz–300GHz, 1999Google Scholar
  39. 39.
    Vaillancourt P, Djemouai A, Harvey J, Sawan M (1997) Em radiation behavior upon biological tissues in a radio-frequency power transfer link for a cortical visual implant. In: Proceedings of IEEE engineering in medicine and biology society, pp 2499–2502, ChicagoGoogle Scholar
  40. 40.
    Engin K, Leeper D, Cater J, Thistlethwaite A, Tupchong L, Mcfarlane J (1995) Extracellular ph distribution in human tumors. Int J Hyperth 11(2):211–216Google Scholar
  41. 41.
    Oh K, Ohb Y, Ohc N, Lee KK, Lee E (2009) A smart flower-like polymeric micelle for ph-triggered anticancer drug release. Int J Pharm 375(1–2):163–169Google Scholar
  42. 42.
    Sethuraman V, Lee M, Bae Y (2008) A biodegradable ph-sensitive micelle system for targeting acidic solid tumors. Pharm Res 25(3):657–666Google Scholar
  43. 43.
    Wike-Hooley J, Haveman J, Reinhold H (1984) The relevance of tumor ph to the treatment of malignant disease. Radiother Oncol 2(4):343–366Google Scholar
  44. 44.
    Zhang X, Lin Y, Gillies R (2010) Tumor ph and its measurement. J Nucl Med 51(8):1167–1170Google Scholar
  45. 45.
    Liu G, Li Y, Pagel M (2007) A single paracest mri contrast agent for accurate in vivo ph measurements. In: International society for magnetic resonance in medicine, p 3406, Berlin, May 2007Google Scholar
  46. 46.
    Bhujwalla Z, Artemov D, Ballesteros P, Cerdan S, Gillies RJ, Solaiyappan M (2002) Combined vascular and extracellular ph imaging of solid tumors. NMR Biomed 15(2):114–119Google Scholar
  47. 47.
    Garcia-Martin ML, Herigault G, Remy C, Farion R, Ballesteros P, Coles JA, Cerdan S, Ziegler A (2001) Mapping extracellular ph in rat brain gliomas in vivo by 1h magnetic resonance spectroscopic imaging: comparison with maps of metabolites. Cancer Res 61(17):6524–6531Google Scholar
  48. 48.
    Gatenby R, Gawlinski E (1996) A reaction-diffusion model of cancer invasion. Cancer Res 56:5745–5753Google Scholar
  49. 49.
    Martin N, Gaffney E, Gatenby R, Maini P (2010) Tumour-stromal inter-actions in acid-mediated invasion: a mathematical model. J Theor Biol 267(3):461–470Google Scholar
  50. 50.
    Patel A, Gawlinski E, Lemieux S, Gatenby R (2001) A cellular automaton model of early tumor growth and invasion: the effects of native tissue vascularity and increased anaerobic tumor metabolism. J Theor Biol 213(3):315–331Google Scholar
  51. 51.
    Smallbone K, Gavaghanb D, Gatenbyc R, Mainia P (2005) The role of acidity in solid tumour growth and invasion. J Theor Biol 235(4):476–484Google Scholar
  52. 52.
    Smallbone K, Gatenby R, Maini P (2008) Mathematical modelling of tumour acidity. J Theor Biol 255(1):106–112Google Scholar
  53. 53.
    Li S, Guo Y (2012) Distributed source seeking by cooperative robots: all-to-all and limited communications. In: Proceedings of IEEE international conference on robotics and automation, pp 1107–1112, Saint Paul, May 2012Google Scholar
  54. 54.
    Slotine JJ,Li W (1990) Applied nonlinear control. Prentice Hall,Englewood CliffsGoogle Scholar
  55. 55.
    Hassan Khalil (2002) Nonlinear systems,3rd edn, Prentice HallGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringStevens Institute of TechnologyHobokenUSA

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