Skip to main content

Multi-scale and Multi-physical/Biochemical Modeling in Bio-MEMS

  • Chapter
  • First Online:
Microsystems for Enhanced Control of Cell Behavior

Part of the book series: Studies in Mechanobiology, Tissue Engineering and Biomaterials ((SMTEB,volume 18))

  • 989 Accesses

Abstract

Multidisciplinarity is intrinsic to Biomedical Engineering, as the products, processes and systems of the biomedical industry, aimed at continuously improving the diagnosis, treatment and prevention of pathologies, are normally developed by large teams of physicians, biologists, materials scientists and engineers. In the field of biomedical microsystems (bio-MEMS) for interacting at a cellular and even molecular level, several physical, chemical and biological phenomena are present and an adequate comprehension of the behaviour of such microdevices also requires studying interactions between the microdevices and the surrounding environments at different scale levels. In such complex systems, the use of modeling resources may be a key aspect towards a straightforward and successful development process. As modern (bio)engineering systems usually exploit phenomena at different scales for improving functionalities of traditional systems, linking the different scales and using multi-scale modeling approaches can increase the predictive capability and applicability of modeling to a wide range of applications. In addition, as modern (bio)engineering systems typically involve different areas of Physics and Chemistry, understanding and modeling their behavior requires the use of multi-physical/chemical modeling approaches. Only by being able to describe the behavior of such (bio)engineering systems at different scale levels and taking account of the physical and chemical phenomena involved in their operation, can we benefit from the advantages of (computer-aided) modeling regarding cost saving, reduction of time-to-market and overall understanding of the products, processes and systems under development. This chapter details methods and examples and provides some cases of study linked to the use of multi-scale and multi-physical/chemical modeling approaches in the field of biomedical microsystems for interacting at a cellular and even molecular level, as introduction to procedures used thoroughly along the Handbook.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Brown SA, Loew LM (2012) Computational analysis of calcium signaling and membrane electrophysiology in cerebellar Purkinje neurons associated with ataxia. BMC Syst Biol 6–70

    Google Scholar 

  • Brown SA, Moraru II, Schaff JC, Loew LM (2011) Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling. J Comput Neurosci 31(2):385–400

    Article  Google Scholar 

  • D’Onofrio A (2009) Fractal growth of tumors and other cellular populations: linking the mechanistic to the phenomenological modeling and vice versa. Chaos Solitons Fractals 41(2):875–880

    Google Scholar 

  • Drexler E (1990) Enginers of creation: the coming era of nanotechnology. Anchor Books, 1986 (Oxford University Press, 1990)

    Google Scholar 

  • Drexler E (1991) Molecular machinery and manufacture with applications to computation. PhD Thesis, MIT

    Google Scholar 

  • Ho SY, Yu MH, Chung CA (2008) Simulation of cell growth and diffusion in tissue engineering scaffolds. IFMBE Proc 23:1745–1748

    Article  Google Scholar 

  • Kier LB, Seybold PG, Cheng C-K (2005) Modeling chemical systems using cellular automata. Springer

    Google Scholar 

  • Leach G (1996) Advances in molecular CAD. Nanotechnology 7(3)

    Google Scholar 

  • Lemon G, King JR (2006) Multiphase modeling of cell behaviour on artificial scaffolds: effects of nutrient depletion and spatially nonuniform porosity. Math Med Biol 24(1):57–83

    Article  MATH  Google Scholar 

  • Lorenzo Yustos H, Lafont Morgado P, Díaz Lantada A, Fernández-Flórez Navidad A, Muñoz Sanz JL, Munoz-Guijosa JM, Muñoz García J, Echávarri Otero J (2010) Towards complete product development teaching employing combined CAD-CAE-CAM technologies. Comput Appl Eng Educ 18(4):661–668

    Google Scholar 

  • Luskin C (2010) Molecular machines in the cell. Discovery Institute

    Google Scholar 

  • Maier B, Radler JO (1999) Conformation and self-diffusion of single DNA molecules confined to two dimensions. Phys Rev Lett 82:1911–1914

    Google Scholar 

  • Maton A (1997) Cells building blocks of life. Prentice Hall, New Yersey

    Google Scholar 

  • Mavroidis C, Dubey A, Yarmush ML (2004) Molecular machines. Annu Rev Biomed Eng 6:363–395

    Article  Google Scholar 

  • Meier-Schellersheim M, Fraser IDC, Klauschen F (2009) Multi-scale modeling in cell biology. Wiley Interdisc Rev Syst Biol Med 1(1):4–14

    Google Scholar 

  • Nelson P (2004) Biological physics. Energy, information, life. W.H. Freemand and Company, New York

    Google Scholar 

  • Piccolino M (2000) Biological machines: from mills to molecules. Nat Rev Mol Cell Biol 1:149–153

    Google Scholar 

  • Rosso L, De Baas A (2012) What makes a material function? Let me compute the ways. In: Modeling in FP7 NMP Programme, European Commission

    Google Scholar 

  • Sengers BG, Please CP, Oreffo ROC (2007) Experimental characterization and computational modeling of two-dimensional cell spreading for skeletal regeneration. R Soc: Interface 4(17):1107–1117

    Google Scholar 

  • Smith DE, Perkins TT, Chu S (1995) Self-diffusion of an entangled DNA molecule by reptation. Phys Rev Lett 75(22):4146–4149

    Article  Google Scholar 

  • Song J, Kinney KA (2002) A model to predict long-term performance of vapor-phase bioreactors: a cellular automaton approach. Environ Sci Technol 36(11):2498–2507

    Article  Google Scholar 

  • Swat M, Thomas GL, Belmonte JM, Shirinifard A, Hmeljak D, Glazier JA (2012) Computational methods in cell biology. Methods Cell Biol 110:325–366

    Article  Google Scholar 

  • Tsyganov MA, Kresteva IB, Aslanidi GV, Aslanidi KB, Deev AR, Ivanitsky GR (2007) The mechanism of fractal-like structure formation by bacterial populations. J Biol Phys 25:165–176

    Article  Google Scholar 

  • Zienkiewicz OC, Taylor RL, Zhu JZ (2005) The finite element method: its basis and fundamentals, 6th edn. Butterworth-Heinemann

    Google Scholar 

Some Interesting Related Websites

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrés Díaz Lantada .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Díaz Lantada, A. (2016). Multi-scale and Multi-physical/Biochemical Modeling in Bio-MEMS. In: Díaz Lantada, A. (eds) Microsystems for Enhanced Control of Cell Behavior. Studies in Mechanobiology, Tissue Engineering and Biomaterials, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-29328-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29328-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29326-4

  • Online ISBN: 978-3-319-29328-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics