Advertisement

Informatics in Solid-state Fermentation

  • Wilerson Sturm
  • Carlos R Soccol
  • Dario Eduardo Amaral Dergint
  • Jose A Rodríguez-Leön
  • Deiva Canali Navarro Vieira Magalháes

Abstract

The technological progress in informatics and computation sciences has been increasing faster than most could imagine or even understand, not only because of its velocity but essentially because of its large scope of applications. With constant improvements of performance features and relative drop of prices, these equipments are more and more cost effective. One of the areas where the demand for more computing power is most notorious is in biotechnology, especially in DNA researches, where it could turn into the driving force of development because of the need to store and analyze the enormous amount of genomic and proteomic data. However, almost the same has been taken place in all other biotechnology areas. Actually the advances are not only in hardware specifications. Indeed there are new programming techniques, new and powerful languages, and even new entire programming frameworks. Together with the hardware performance these software developments allow more complex problems solution. This chapter will discuss some of the new techniques, solutions and challenges concerning the computing uses in biotechnology and show some examples of implementation like Fersol2 software, for instance.

Keywords

Fuzzy Logic Specific Growth Rate Multiagent System Synaptic Weight Biomass Synthesis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anthony, Martin, Bartlett & Peter L, 1999, Neural Network Learning: Theoretical Foundations. 403p. p 1,5. Cambridge University Press: Cambridge.Google Scholar
  2. Carrizalez V, Rodríguez H & Sardina I, 1981, Determination of the specific growth rate of molds on semi-solid cultures. Biotechnology and Bioengineering, 23, 321.Google Scholar
  3. Dergint Dario EA, 1999, Apprentissage Collectif et Milieux Innovateurs: Étude de Cas α Grenoble et Simulations Multi-Agents — Thesis, p 567. UTC, Compiègne.Google Scholar
  4. delstein-Keshet L, 2004, Mathematical Models in Biology, p 586. Siam: Philadelphia.Google Scholar
  5. Gupta MM, 1977, Fuzzy Automata and Decision Processes. New York: Elsevier.Google Scholar
  6. Harrington H. James, & lumay Kerim, 2000, Simulation Modeling Methods: To Reduce Risks and Increase Performance, p 1–3. p 379. McGraw-Hill: New York.Google Scholar
  7. Holland John H, Keith J Nisbett, Richard E Thagard & Paul R, 1986, Induction: Processes of Inference, Learning and Discovery. Cambridge, Mass: MIT Press.Google Scholar
  8. Jennings Nicholas R, Sycara Katia & Wooldridge Michael, 1998, A Roadmap of Agent Research and Development — Autonomous Agents and Multi-Agent Systems, pp. 7–38. Boston: Kluwer Academic Publishers.Google Scholar
  9. Kelly Laurie & Bai Ying, 2005, The Windows Serial Port Programming Handbook, p 4, 5. CRC Press LLC: Charlotte.Google Scholar
  10. Koba Y, Feroza B, Fujio Y & Ueda S, 1986, Journal of Fermentation Technology, 62, 2, p. 175.Google Scholar
  11. Matchman SE, Jordan BR & Wood DA, 1985, Estimation of fungal biomass by three different methods. Applied Microbiology and Biotechnology, 21, p. 108.Google Scholar
  12. Nishio N, Tai Tai & K Nagai S, 1979, Hydrolase production by Aspergillus niger in solid state cultivation. European Jounal of Applied Microbiology and Biotechnology, 8, p. 263.Google Scholar
  13. Norgaard M, Ravn O, Poulsen NK & Hansen LK, 2000, Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner‐s Handbook. 246 p. Springer-Verlag London Limited: Great Britain.Google Scholar
  14. Okasaki N, Sugama S & Tanaka T, 1980, Mathematical model of surface culture of koji mold, Journal of Fermentation Technology, 58,5, p. 471.Google Scholar
  15. Pandey A, Soccol CR, Rodríguez-leön JA & Nigam P, 2001; Solid State Fermentation in Biotechnology: Fundamentals and Applications. Asiatech Publishers, Inc. New Dehli. India.Google Scholar
  16. Raimbault M, 1981, Travaux et Documents de l’ORSTOM, No. 27, Paris.Google Scholar
  17. Rodríguez León J A, Sastre L, Echevarria J, Delgado G & Bechstedt W, 1988, A Mathematical Approach for the estimation of biomass production rate in solid-state fermentation, Ada Biotechnologica, 8(4), 299–302Google Scholar
  18. Sturm Wilerson, 2005, Avaliaçao do Potencial de Uso da Lögica Fuzzy para a Identificação de Indicadores de Competências no Curriculo Lattes. Dissertation, p 104. April-25-2005. Centro Federal de Educação Tecnolögica do Paraná. Curitiba.Google Scholar
  19. Sycara Katia P, 1998, Multiagent Systems, AI Magazine, 79–92.Google Scholar
  20. Wang Lingfeng, Tan & Kay Chen, 2006, Industrial Automation Software Design, p 158,159 John Wiley and Sons: New Jersey.Google Scholar
  21. Zadeh LA, 1965, Fuzzy Sets. Information and Control, 8, 338–353.Google Scholar
  22. Zadeh LA, 1990, The birth and evolution of fuzzy logic. In: Turksen, I.B. Proceedings of NAFIP’90.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Wilerson Sturm
    • 1
  • Carlos R Soccol
    • 1
  • Dario Eduardo Amaral Dergint
    • 1
  • Jose A Rodríguez-Leön
    • 1
  • Deiva Canali Navarro Vieira Magalháes
    • 2
  1. 1.Bioprocess Engineering and Biotechnology DivisionFederal University of ParanáCuritiba-PRBrazil
  2. 2.Departamento de EletrônicaUniversidade Tecnológica Federal do ParanCuritiba-PRBrazil

Personalised recommendations