A simulation environment for polymeric nanoparticles based on multi-agent systems

  • Alexandre de O. ZamberlanEmail author
  • Guilherme C. Kurtz
  • Tomas L. Gomes
  • Rafael H. Bordini
  • Solange B. Fagan
Software Report


Production and characterization of polymeric nanoparticles, as colloidal dispersions, are processes that require time and technical skills to make the results accurate. Computational simulations in nanoscience have been used to help in these processes and provide agility and support to reach results: stability and quality in dispersions. Multi-Agent System for Polymeric Nanoparticles (MASPN) is an innovative and original simulation environment with features to demonstrate interactions of particles from physical-chemical parameters, ensuring Brownian motion of particles and attractive and repulsive behaviour. The MASPN environment has been designed and has been built according to the feature-driven development (FDD), as software methodology, and a multi-agent systems approach. In addition, we have used the event-driven simulation package algs4, the JASON agent building environment, all integrated by Java language. This paper aims to present the relation of the algs4 package and the JASON tool, both integrated into the MASPN environment to generate Brownian motion with elastic and inelastic collisions. The MASPN environment as a simulation tool emerges as a result, including the following features: graphical interface; integrated physical-chemical parameters; Brownian motion; JASON and algs4 integration; and distribution charts (size, zeta potential, and pH).


Simulation software Artificial intelligence Nanotechnology Particle collisions Cooperative systems 



  1. 1.
    Dowling A, Clif R, Grobert N (2004) Nanoscience and nanotechnologies: opportunities and uncertainties. Tech. rep., The Royal Academic of Engineering, LondonGoogle Scholar
  2. 2.
    Sedgewick R, Wayne K (2011) Algorithms, 4th edn. Addison-Wesley Professional, BostonGoogle Scholar
  3. 3.
    Ntika M, Kefalas P, Stamatopoulou I (2013) In: 2013 17th International Conference System Theory, Control and Computing (ICSTCC). IEEE, Sinaia, pp 777–782Google Scholar
  4. 4.
    Jo DH, Kim JH, Lee TG, Kim JH (2015) Size, surface charge, and shape determine therapeutic effects of nanoparticles on brain and retinal diseases. Nanomed Nanotechnol Biol Med 11(7):1603CrossRefGoogle Scholar
  5. 5.
    Wooldridge M (2001) Introduction to multiagent systems. Wiley, New YorkGoogle Scholar
  6. 6.
    Jain K, Mehra NK, Jain NK (2014) Potentials and emerging trends in nanopharmacology. Curr Opin Pharmacol 15:97. Cardiovascular and renalCrossRefGoogle Scholar
  7. 7.
    Bagul R, Mahajan V, Dhake A (2012) New approaches in nanoparticulate drug delivery system: a review. Int J Curr Pharm Res 4(2):29Google Scholar
  8. 8.
    Mohanraj V, Chen Y (2006) Nanoparticles—a review. Trop J Pharm Res 5(1):561. Google Scholar
  9. 9.
    Pohlmann AR, Mezzalira G, de Garcia Venturini C, Cruz L, Bernardi A, Jäger E., Battastini AM, da Silveira NP, Guterres SS (2008) Determining the simultaneous presence of drug nanocrystals in drug-loaded polymeric nanocapsule aqueous suspensions: a relation between light scattering and drug content. Int J Pharm 359 (1):288CrossRefGoogle Scholar
  10. 10.
    Neto OPV (2014) Intelligent computational nanotechnology: The role of computational intelligence in the development of nanoscience and nanotechnology. J Comput Theor Nanosci 11:1CrossRefGoogle Scholar
  11. 11.
    de C. Barbosa R, Krott LB, Barbosa MC (2016) Structural behavior of an anomalous fluid under hydrophobic, hydrophilic and heterogeneous confinement, Journal of Physics - VIII Brazilian Meeting on Simulational Physics 686.
  12. 12.
    Halliday D, Walker J, Resnick R (2010) Fundamentals of physics. Wiley, New YorkGoogle Scholar
  13. 13.
    Ho-Kim Q, Kumar N, Lam CS (2004) Invitation to contemporary physics. World Scientific, New YorkCrossRefGoogle Scholar
  14. 14.
    Uhrmacher AM, Weyns D (2009) Multi-agent systems: Simulation and application. Computational analysis, synthesis, and design of dynamic models series. CRC Press, Boca RatonGoogle Scholar
  15. 15.
    Bordini R, Hübner JF, Wooldridge M (2007) Programming multi-agent systems in AgentSpeak using Jason. Wiley, LiverpolGoogle Scholar
  16. 16.
    Zamberlan A (2002) Em direção a uma técnica para programação orientada a agentes BDI. Master’s thesis, PUCRSGoogle Scholar
  17. 17.
    Zamberlan A, Dalcin AJ, Kurtz G, Bordini R, Raffin R, Fagan S (2016) Simulation environment for polymeric nanoparticle: experiment database. Disciplinarum Sci 17(3):429Google Scholar
  18. 18.
    d’Inverno M, Howells P, Montagna S, Roeder I, Saunders R (2009) Multi-Agent Systems: Simulation and application. In: Uhrmacher AM, Weyns D (eds) Computational Analysis, Synthesis, and Design of Dynamic Models Series. chap. 13, pp 389–418. CRC Press, Boca RatonGoogle Scholar
  19. 19.
    Hogg T (2007) Coordinating microscopic robots in viscous fluids. Auton Agent Multi-Agent Syst 14(3):271. CrossRefGoogle Scholar
  20. 20.
    Dan N (2014) Nanostructured lipid carriers: Effect of solid phase fraction and distribution on the release of encapsulated materials. Langmuir 30(46):13809CrossRefGoogle Scholar
  21. 21.
    Pressman RS (2010) Software Engineering: a Practitioners Approach, 7th edn. McGraw-Hill Education, New YorkGoogle Scholar
  22. 22.
    Rao A (1996) Agents Breaking Away. Springer, Eindhoven, pp 42–55CrossRefGoogle Scholar
  23. 23.
    Petreska I, Stamatopoulou I (2013) .. In: Proceedings of the 6th Balkan Conference in Informatics, BCI ’13. ACM, New York, pp 53–60
  24. 24.
    Rafiee A, Alimohammadian MH, Gazori T, Riazi-rad F, Fatemi SMR, Parizadeh A, Haririan I, Havaskary M (2014) Comparison of chitosan, alginate and chitosan/alginate nanoparticles with respect to their size, stability, toxicity and transfection. Asian Pac J Trop Dis 4(5):372CrossRefGoogle Scholar
  25. 25.
    Li Z, Cheng E, Huang W, Zhang T, Yang Z, Liu D, Tang Z (2011) Improving the yield of mono-DNA-functionalized gold nanoparticles through dual steric hindrance. J Amer Chem Soc 133(39):15284. PMID: 21894982CrossRefGoogle Scholar
  26. 26.
    dos Santos Chaves P, Ourique AF, Frank LA, Pohlmann AR, Guterres SS, Beck RCR (2017) Carvedilol-loaded nanocapsules: Mucoadhesive properties and permeability across the sublingual mucosa. Eur J Pharm Biopharm 114:88. CrossRefGoogle Scholar
  27. 27.
    Lopes LQS, Santos CG, de Almeida Vaucher R, Raffin RP, Santos RCV (2016) Nanocapsules with glycerol monolaurate: Effects on Candida albicans biofilms. Microb Pathog 97:119. CrossRefGoogle Scholar
  28. 28.
    Lollo G, Gonzalez-Paredes A, Garcia-Fuentes M, Calvo P, Torres D, Alonso MJ (2017) Polyarginine nanocapsules as a potential oral peptide delivery carrier. J Pharm Sci 106(2):611. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidade FranciscanaSanta MariaBrazil
  2. 2.PUCRSPorto AlegreBrazil

Personalised recommendations