Desenvolvimento de um Framework para Modelagem e Simulação de Redes Regulatórias Genéticas usando Sistemas Multiagente
Resumo
Os sistemas biológicos são altamente complexos e a sua separação em partes individuais facilita o estudo. A representação de sistemas biológicos como Redes Regulatórias Genéticas que formam um mapa das interações entre as moléculas num organismo é uma maneira padrão de representar essa complexidade biológica. As Redes Regulatórias Genéticas são compostas de genes que são traduzidos em fatores de transcrição, que por sua vez regulam outros genes. Os cientistas trabalharam na inferência e representação de Redes Regulatórias Genéticas. Para fins de simulação e inferência, muitos modelos matemáticos e algorítmicos diferentes foram adotados para representar as Redes Regulatórias Genéticas nos últimos anos. Entre esses métodos, acreditamos que os Sistemas Multiagentes sejam um pouco negligenciados. Neste trabalho, apresentam-se os primeiros esforços para desenvolver um simulador usando o Sistemas Multiagente para modelar Redes Regulatórias Genéticas genéricos. Neste sentido, está sendo desenvolvido um Sistema Multiagente que é composto por agentes que imitam os processos bioquímicos de regulação de genes.Referências
Agostinho, N., Werhli, A., and Adamatti, D. (2018). Revisão sistemática para o desenvolvimento de um ambiente de simulação multiagente para vias regulatórias. RETEC. REVISTA DE TECNOLOGIAS (OURINHOS), 11.
Alon, U. (2007). Network motifs: theory and experimental approaches. Nature Reviews Genetics.
Ezer (2014). Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression. Comput Struct Biotechnol J.
Ghazikhani, A., Akbarzadeh, T., and Monsefi, R. (2011). Genetic regulatory network inference using recurrent neural networks trained by a multi agent system. International eConference on Computer and Knowledge Engineering (ICCKE).
Gibas, C. and Jambeck, P. (2002). Developing bioinformatics computer skills. Yale Journal of Biology and Medicine.
Haydarlou, R., Jacobsen, A., Bonzanni, N., Feenstra, K. A., Abeln, S., and Heringa, J. (2016). Bioasf: a framework for automatically generating executable pathway models specified in biopax. Bioinformatics,, 32:i60–i69.
Johnson, K. and Goody, R. (2011). The original michaelis constant: Translation of the 1913 michaelis-menten paper. Biochemistry.
Khan, S., Makkena, R., McGeary, F., Decker, K., Gillis, W., and Schmidt, C. (2003). A multi-agent system for the quantitative simulation of biological networks. AAMAS.
Liu, J., Chi, Y. Z. C., and Zhu, C. (2016). A dynamic multiagent genetic algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 24.
Lopes, F. M. (2011). Redes complexas de expressao genica: sintese, identificacao, analise e aplicacoes. Master’s thesis, Universidade de Sao Paulo.
Mariano, D., Leite, C., Santos, L., Rocha, R., and Melo-Minardi, R. (2017). A guide to performing systematic literature reviews in bioinformatics. Technical Report RT.DCC.002/2017, Universidade Federal de Minas Gerais.
Pham, D. (2008). Multi-agent based simulation of large random boolean network. MSCLES.
Pokhilko, A., Fernandez, A., Edwards, K., Southern, M., Halliday, K., and Millar, A. (2012). The clock gene circuit in arabidopsis includes a repressilator with additional feedback loops. molecular systems biology. Molecular System Biology, 8.
Sanfilippo, A., Haack, J., McDermott, J., Stevens, S., and Stenzel-Poore, M. (2012). Modeling emergence in neuroprotective regulatory networks. International Conference on Complex Sciences - springer, pages 291–302.
Schreiber, S. (2005). Small molecules: the missing link in the central dogma. Nature Chemical Biology.
Yang, T. and Sun, Y. (2011). The reconstruction of gene regulatory network based on multi-agent system by fusing multiple data sources. 2011 IEEE International Conference on Computer Science and Automation Engineering, 4.
Alon, U. (2007). Network motifs: theory and experimental approaches. Nature Reviews Genetics.
Ezer (2014). Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression. Comput Struct Biotechnol J.
Ghazikhani, A., Akbarzadeh, T., and Monsefi, R. (2011). Genetic regulatory network inference using recurrent neural networks trained by a multi agent system. International eConference on Computer and Knowledge Engineering (ICCKE).
Gibas, C. and Jambeck, P. (2002). Developing bioinformatics computer skills. Yale Journal of Biology and Medicine.
Haydarlou, R., Jacobsen, A., Bonzanni, N., Feenstra, K. A., Abeln, S., and Heringa, J. (2016). Bioasf: a framework for automatically generating executable pathway models specified in biopax. Bioinformatics,, 32:i60–i69.
Johnson, K. and Goody, R. (2011). The original michaelis constant: Translation of the 1913 michaelis-menten paper. Biochemistry.
Khan, S., Makkena, R., McGeary, F., Decker, K., Gillis, W., and Schmidt, C. (2003). A multi-agent system for the quantitative simulation of biological networks. AAMAS.
Liu, J., Chi, Y. Z. C., and Zhu, C. (2016). A dynamic multiagent genetic algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 24.
Lopes, F. M. (2011). Redes complexas de expressao genica: sintese, identificacao, analise e aplicacoes. Master’s thesis, Universidade de Sao Paulo.
Mariano, D., Leite, C., Santos, L., Rocha, R., and Melo-Minardi, R. (2017). A guide to performing systematic literature reviews in bioinformatics. Technical Report RT.DCC.002/2017, Universidade Federal de Minas Gerais.
Pham, D. (2008). Multi-agent based simulation of large random boolean network. MSCLES.
Pokhilko, A., Fernandez, A., Edwards, K., Southern, M., Halliday, K., and Millar, A. (2012). The clock gene circuit in arabidopsis includes a repressilator with additional feedback loops. molecular systems biology. Molecular System Biology, 8.
Sanfilippo, A., Haack, J., McDermott, J., Stevens, S., and Stenzel-Poore, M. (2012). Modeling emergence in neuroprotective regulatory networks. International Conference on Complex Sciences - springer, pages 291–302.
Schreiber, S. (2005). Small molecules: the missing link in the central dogma. Nature Chemical Biology.
Yang, T. and Sun, Y. (2011). The reconstruction of gene regulatory network based on multi-agent system by fusing multiple data sources. 2011 IEEE International Conference on Computer Science and Automation Engineering, 4.
Publicado
07/07/2020
Como Citar
AGOSTINHO, Nilzair B.; WERHLI, Adriano V.; ADAMATTI, Diana F..
Desenvolvimento de um Framework para Modelagem e Simulação de Redes Regulatórias Genéticas usando Sistemas Multiagente. In: WORKSHOP-ESCOLA DE SISTEMAS DE AGENTES, SEUS AMBIENTES E APLICAÇÕES (WESAAC), 14. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 238-243.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2020.33396.
