Inferência de Redes de Regulação Gênica Usando Programação Genética Cartesiana Paralela
Resumo
A inferência de Redes de Regulação Gênica (GRNs) é importante em Biologia Sistêmica, pois permite o entendimento de padrões de interações entre genes. Essas descobertas sãoúteis para fornecer compreensão sobre doenças e ajudar no desenvolvimento de fármacos. Técnicas de computação evolutiva, como a Programação Genética Cartesiana (CGP), têm sido utilizadas para inferir GRNs com resultados promissores. Entretanto, a CGP tem problemas de escalabilidade. Aqui, GRNs são inferidas de forma eficiente usando abordagens de computação de alto desempenho. Experimentos computacionais mostram que o método desenvolvido nesta iniciação científica é capaz de inferir GRNs mais rapidamente do que outros da literatura com soluções simbólicas. O ganho em tempo de processamento da técnica paralela apresentada em relação ao formato sequencial é de até 104%.Referências
da Silva, J. E. H. et al. (2020). Inferring gene regulatory network models from time-series data using metaheuristics. In 2020 IEEE (CEC), pages 1–8. IEEE.
Delahaye-Duriez, A. et al. (2016). Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery. Genome biology, 17(1):1–18.
Dumitrescu, D., Lazzerini, B., Jain, L. C., and Dumitrescu, A. (2000). Evolutionary computation. CRC press.
Emmert-Streib, F. et al. (2014a). The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks. Frontiers in genetics, 5:15.
Emmert-Streib, F. et al. (2014b). Grns and their applications: understanding biological and medical problems in terms of networks. Front. in cell and devel. biology, 2:38.
Khan, M. M., Khan, G. M., and Miller, J. F. (2010). Evolution of neural networks using cartesian genetic programming. In Cong. on Evol. Comput. (CEC), pages 1–8. IEEE.
Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Complex adaptive systems. MIT Press.
Ma, B. et al. (2019). Identification of gene regulatory networks by integrating genetic programming with particle filtering. IEEE Access, 7:113760–113770.
McCall, M. N. (2013). Estimation of gene regulatory networks. Postdoc journal: a journal of postdoctoral research and postdoctoral affairs, 1(1):60.
Medeiros, F. et al. (2019). Gene regulatory network inference and analysis of multidrugresistant pseudomonas aeruginosa. Memórias do Instituto Oswaldo Cruz, 114.
Miller, J. F. (2011). Cartesian genetic programming. In Cartesian Genetic Programming, pages 17–34. Springer.
Miller, J. F. et al. (1999). An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In Proceedings of the genetic and evolutionary computation conference, volume 2, pages 1135–1142.
Noman, N. and Iba, H. (2007). Inferring grns using differential evolution with local search heuristics. IEEE/ACM Trans. on comp. biology and bioinfo., 4(4):634–647.
Palafox, L., Noman, N., and Iba, H. (2013). Reverse engineering of grns using dissipative particle swarm optimization. IEEE Trans. on Evolutionary Comp., 17(4):577–587.
Pratapa, A. et al. (2020). Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature methods, 17(2):147–154.
Qian, L., Wang, H., and Dougherty, E. R. (2008). Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and kalman filtering. IEEE Transactions on Signal Processing, 56(7):3327–3339.
Silva, B. M., Bernardino, H. S., and Barbosa, H. J. (2021). Human activity recognition using parallel cartesian genetic programming. In CEC, pages 474–481. IEEE.
Streichert, F., Planatscher, H., Spieth, C., Ulmer, H., and Zell, A. (2004). Comparing genetic programming and evolution strategies on inferring gene regulatory networks. In Genetic and Evolutionary Computation Conference, pages 471–480. Springer.
Van Der Wijst, M. G., de Vries, D. H., Brugge, H., Westra, H.-J., and Franke, L. (2018). An integrative approach for building personalized gene regulatory networks for precision medicine. Genome medicine, 10(1):1–15.
Delahaye-Duriez, A. et al. (2016). Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery. Genome biology, 17(1):1–18.
Dumitrescu, D., Lazzerini, B., Jain, L. C., and Dumitrescu, A. (2000). Evolutionary computation. CRC press.
Emmert-Streib, F. et al. (2014a). The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks. Frontiers in genetics, 5:15.
Emmert-Streib, F. et al. (2014b). Grns and their applications: understanding biological and medical problems in terms of networks. Front. in cell and devel. biology, 2:38.
Khan, M. M., Khan, G. M., and Miller, J. F. (2010). Evolution of neural networks using cartesian genetic programming. In Cong. on Evol. Comput. (CEC), pages 1–8. IEEE.
Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Complex adaptive systems. MIT Press.
Ma, B. et al. (2019). Identification of gene regulatory networks by integrating genetic programming with particle filtering. IEEE Access, 7:113760–113770.
McCall, M. N. (2013). Estimation of gene regulatory networks. Postdoc journal: a journal of postdoctoral research and postdoctoral affairs, 1(1):60.
Medeiros, F. et al. (2019). Gene regulatory network inference and analysis of multidrugresistant pseudomonas aeruginosa. Memórias do Instituto Oswaldo Cruz, 114.
Miller, J. F. (2011). Cartesian genetic programming. In Cartesian Genetic Programming, pages 17–34. Springer.
Miller, J. F. et al. (1999). An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In Proceedings of the genetic and evolutionary computation conference, volume 2, pages 1135–1142.
Noman, N. and Iba, H. (2007). Inferring grns using differential evolution with local search heuristics. IEEE/ACM Trans. on comp. biology and bioinfo., 4(4):634–647.
Palafox, L., Noman, N., and Iba, H. (2013). Reverse engineering of grns using dissipative particle swarm optimization. IEEE Trans. on Evolutionary Comp., 17(4):577–587.
Pratapa, A. et al. (2020). Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature methods, 17(2):147–154.
Qian, L., Wang, H., and Dougherty, E. R. (2008). Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and kalman filtering. IEEE Transactions on Signal Processing, 56(7):3327–3339.
Silva, B. M., Bernardino, H. S., and Barbosa, H. J. (2021). Human activity recognition using parallel cartesian genetic programming. In CEC, pages 474–481. IEEE.
Streichert, F., Planatscher, H., Spieth, C., Ulmer, H., and Zell, A. (2004). Comparing genetic programming and evolution strategies on inferring gene regulatory networks. In Genetic and Evolutionary Computation Conference, pages 471–480. Springer.
Van Der Wijst, M. G., de Vries, D. H., Brugge, H., Westra, H.-J., and Franke, L. (2018). An integrative approach for building personalized gene regulatory networks for precision medicine. Genome medicine, 10(1):1–15.
Publicado
07/06/2022
Como Citar
PRACHEDES, Luciana N. S.; SILVA, José Eduardo Henriques da; BERNARDINO, Heder Soares; OLIVEIRA, Itamar Leite de.
Inferência de Redes de Regulação Gênica Usando Programação Genética Cartesiana Paralela. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 74-79.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2022.222566.
