Agent-Oriented Stem Cell Computational Modeling
Resumo
Devido à capacidade de diferenciação em diferentes tipos de células, as células-tronco têm tido papel cada vez mais relevante como ferramenta terapêutica. São potencialmente úteis em terapias de combate a diversas doenças cardiovasculares, neurodegenerativas dentre outras. A possibilidade de utilização do poder computacional e da aplicabilidade das recentes técnicas de modelagem computacionais e matemáticas para entender e predizer aspectos importantes do comportamento de sistemas biológicos surgem como um novo ferramental para a pesquisa medica e biológica. Neste contexto, células-tronco podem ser caracterizadas como sistemas naturais, abertos, distribuídos, e complexos compostos de múltiplos elementos autonômicos interagindo entre si, e que exibem comportamento emergente. O projeto da modelagem computacional destes sistemas é uma tarefa não-trivial que, por definição, requer abordagens de engenharia de software específicas, incluindo técnicas de modelagem específicas. Este artigo apresenta os primeiros experimentos obtidos na modelagem computacional do comportamento de células-tronco embrionárias utilizando tal abordagem (orientada agentes), seus desafios e contribuições alvo esperadas a longo prazo. Este trabalho se situa no Segundo Grande Desafio da construção de modelos computacionais de sistemas naturais complexos.Referências
Gatti, M., de Vasconcellos, J.E., Lucena, C.J.P.; An Agent Oriented Software Engineering Approach for the Adult Stem-Cell Modeling, Simulation and Visualization. Workshop SEAS, João Pessoa, 2007.
Gatti, M., von Staa, A., Lucena, C.; AUML-BP: A Basic Agent Oriented Software Development Process Model Using AUML; Monografias em Ciência da Computação, Departamento de Informática, PUC-Rio, No. 21/07, 25 pg., 2007.
Luke, S., Cioffi-Revilla, C., Panait, L. and Sullivan, K. (2004), 'MASON: A New Multi-Agent Simulation Toolkit', SwarmFest 2004, Eighth Annual Swarm Users/Researchers Conference, University of Michigan, Ann Arbor, Michigan USA.
Gamma, E., Helm, R., Johnson, R., Vlissides, J. "Design Patterns: Elements of Reusable Object-Oriented Software." Reading, MA: Addison Wesley, 1995.
Lei, J., Mackey, M.C. (2007). "Stochastic differential delay equation, moment stability, and application to hematopoietic stem cell regulation system," SIAM J. Appl. Math. (2007), 67(2), 387–407.
Agur, Z., Daniel, Y. and Ginosar, Y. (2002). The universal properties of stem cells as pinpointed by a simple discrete model. Mathematical Biology, 44:79–86, 2002.
Axelrod, R. (1997) The Complexity of Cooperation (Princeton Univ. Press, Princeton).
Epstein, E. M. (2002) Proc. Natl. Acad. Sci. USA 99, 7243–7250
Helbing, D., Farkas, I. & Vicsek, T. (2000) Nature 407, 487–490.
Jennings, N., R. (2000). On Agent-Based Software Engineering. Artificial Intelligence Journal, 117 (2) 277-296, 2000.
d’Inverno, M. and Prophet, J. (2004). Modeling, simulation and visualisation of adult stem cells. In P. Gonzalez, E. Merelli, and A. Omicini, editors, Fourth International Workshop on Network Tools and Applications, NETTAB, pages 105–116, 2004.
d’Inverno, M. and Saunders, R. (2005). Agent-based modeling of stem cell organisation in a niche. In Engineering Self-Organising Systems, volume 3464 of LNAI. Springer, 2005.
d’Inverno, M., Theise, N. D. and Prophet, J. (2005). Mathematical modeling of stem cells: a complexity primer for the stem cell biologist. In C. Potten, J. Watson, R. Clarke, and A. Renehan, ed., Tissue Stem Cells: Biology and Applications. Marcel Dekker.
Scadden, D. T. (2006). The stem-cell niche as an entity of action. Nature 441, 1075-1079 (29 June 2006) | DOI: 10.1038/nature04957, Published online 28 June 2006.
Vogel, H., Niewisch, H., and Matioli, G.; J. Theor. Biol., 22(2):249-270, 1969.
Loeffler, M. and Grossmann, B.; J. Theor. Biol., 150(2):175-191, 1991.
Loeffler, M. and Roeder, I. Cells Tissues Organs, 171(1):8-26, 2002.
Lord, B.I.; in Stem cells, Cambridge Academic Press, 1997, pp 401-422.
Wichmann, H. E. and Loeffler, M.; Mathematical modeling of cell proliferation: Stem cell regulation in hemopoiesis, CRC Press, Boca Raton, 1985.
Metropolis, N. and Ulam, S. (1949). The Monte Carlo Method. J. Amer. Stat. Assoc. 44, 335-341, 1949.
Theise, N. D. and d’Inverno, M. (2003). Understanding cell lineages as complex adaptive systems. Blood, Cells, Molecules and Diseases, 32:17–20, 2003.
Weiss, G.; Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence. (ed). MIT Press, 2001. Chapter 4, pp. 165-199.
Ken Shoemake. Arcball rotation control, pages 175{192. Academic Press Professional, Inc., 1994.
M. Spivey. The Z Notation (second edition). Prentice Hall International: Hemel Hempstead, England, 1992.
T. De Wolf, T. Holvoet; Towards a Methodology for Engineering Self-Organising Emergent Systems; Self-Organization and Autonomic Informatics (I), Volume 135 of Frontiers in Artificial Intelligence and Applications; H. Czap et al. (Eds.); IOS Press, 2005.
Workshop Report on Grand Challenges in Computer Science Research in Brazil: 2006-2016. Brazilian Computer Society (SBC), 2006.
UK Computing Research Committee and the British Computer Society. iViS - in Vivo - in Silico: The Virtual Worm, Weed and Bug Breathing Life into the Biological DataMountain. A Grand Challenge For Computational Systems Biology., 2004.
Gatti, M., von Staa, A., Lucena, C.; AUML-BP: A Basic Agent Oriented Software Development Process Model Using AUML; Monografias em Ciência da Computação, Departamento de Informática, PUC-Rio, No. 21/07, 25 pg., 2007.
Luke, S., Cioffi-Revilla, C., Panait, L. and Sullivan, K. (2004), 'MASON: A New Multi-Agent Simulation Toolkit', SwarmFest 2004, Eighth Annual Swarm Users/Researchers Conference, University of Michigan, Ann Arbor, Michigan USA.
Gamma, E., Helm, R., Johnson, R., Vlissides, J. "Design Patterns: Elements of Reusable Object-Oriented Software." Reading, MA: Addison Wesley, 1995.
Lei, J., Mackey, M.C. (2007). "Stochastic differential delay equation, moment stability, and application to hematopoietic stem cell regulation system," SIAM J. Appl. Math. (2007), 67(2), 387–407.
Agur, Z., Daniel, Y. and Ginosar, Y. (2002). The universal properties of stem cells as pinpointed by a simple discrete model. Mathematical Biology, 44:79–86, 2002.
Axelrod, R. (1997) The Complexity of Cooperation (Princeton Univ. Press, Princeton).
Epstein, E. M. (2002) Proc. Natl. Acad. Sci. USA 99, 7243–7250
Helbing, D., Farkas, I. & Vicsek, T. (2000) Nature 407, 487–490.
Jennings, N., R. (2000). On Agent-Based Software Engineering. Artificial Intelligence Journal, 117 (2) 277-296, 2000.
d’Inverno, M. and Prophet, J. (2004). Modeling, simulation and visualisation of adult stem cells. In P. Gonzalez, E. Merelli, and A. Omicini, editors, Fourth International Workshop on Network Tools and Applications, NETTAB, pages 105–116, 2004.
d’Inverno, M. and Saunders, R. (2005). Agent-based modeling of stem cell organisation in a niche. In Engineering Self-Organising Systems, volume 3464 of LNAI. Springer, 2005.
d’Inverno, M., Theise, N. D. and Prophet, J. (2005). Mathematical modeling of stem cells: a complexity primer for the stem cell biologist. In C. Potten, J. Watson, R. Clarke, and A. Renehan, ed., Tissue Stem Cells: Biology and Applications. Marcel Dekker.
Scadden, D. T. (2006). The stem-cell niche as an entity of action. Nature 441, 1075-1079 (29 June 2006) | DOI: 10.1038/nature04957, Published online 28 June 2006.
Vogel, H., Niewisch, H., and Matioli, G.; J. Theor. Biol., 22(2):249-270, 1969.
Loeffler, M. and Grossmann, B.; J. Theor. Biol., 150(2):175-191, 1991.
Loeffler, M. and Roeder, I. Cells Tissues Organs, 171(1):8-26, 2002.
Lord, B.I.; in Stem cells, Cambridge Academic Press, 1997, pp 401-422.
Wichmann, H. E. and Loeffler, M.; Mathematical modeling of cell proliferation: Stem cell regulation in hemopoiesis, CRC Press, Boca Raton, 1985.
Metropolis, N. and Ulam, S. (1949). The Monte Carlo Method. J. Amer. Stat. Assoc. 44, 335-341, 1949.
Theise, N. D. and d’Inverno, M. (2003). Understanding cell lineages as complex adaptive systems. Blood, Cells, Molecules and Diseases, 32:17–20, 2003.
Weiss, G.; Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence. (ed). MIT Press, 2001. Chapter 4, pp. 165-199.
Ken Shoemake. Arcball rotation control, pages 175{192. Academic Press Professional, Inc., 1994.
M. Spivey. The Z Notation (second edition). Prentice Hall International: Hemel Hempstead, England, 1992.
T. De Wolf, T. Holvoet; Towards a Methodology for Engineering Self-Organising Emergent Systems; Self-Organization and Autonomic Informatics (I), Volume 135 of Frontiers in Artificial Intelligence and Applications; H. Czap et al. (Eds.); IOS Press, 2005.
Workshop Report on Grand Challenges in Computer Science Research in Brazil: 2006-2016. Brazilian Computer Society (SBC), 2006.
UK Computing Research Committee and the British Computer Society. iViS - in Vivo - in Silico: The Virtual Worm, Weed and Bug Breathing Life into the Biological DataMountain. A Grand Challenge For Computational Systems Biology., 2004.
Publicado
12/07/2008
Como Citar
GATTI, Maíra A. de C.; FAUSTINO, Geisa M.; BISPO, Diego; VASCONCELLOS, José Eurico; LUCENA, Carlos J. P. de.
Agent-Oriented Stem Cell Computational Modeling. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 35. , 2008, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2008
.
p. 31-45.
ISSN 2595-6205.
