Towards a Surrogate-assisted PALLAS algorithm for Gene Regulatory Network Inference
Resumo
This paper analyzes the application of surrogate models to improve the efficiency of Gene Regulatory Network (GRN) inference from time-series data. A Radial Basis Function (RBF) surrogate model was integrated with the Penalized mAximum LikeLihood and pArticle Swarms (PALLAS) using a Mixed Fish School Search (MFSS) algorithm to reduce the computational cost associated with evaluating the penalized log-likelihood (PLL) fitness function. Experimental results on the p53-MDM2 negative-feedback loop GRN dataset demonstrate that the surrogate-assisted approach significantly reduced fitness function calls by 50% and 89% while maintaining the quality of the PLL metric, with this showing the potential of surrogate models to accelerate GRN inference.Referências
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Bastos Filho, C. J., Neto, F. L., Sousa, M. F. C., Pontes, M. R., and Madeiro, S. S. (2009). On the influence of the swimming operators in the fish school search algorithm. In 2009 IEEE International Conference on Systems, Man and Cybernetics, pages 5012–5017. IEEE.
Batchelor, E., Loewer, A., and Lahav, G. (2009). The ups and downs of p53: understanding protein dynamics in single cells. Nature Reviews Cancer, 9(5):371–377.
Braga-Neto, U. (2011). Optimal state estimation for boolean dynamical systems. In 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pages 1050–1054. IEEE.
Buhmann, M. D. (2000). Radial basis functions. Acta numerica, 9:1–38.
Eriksson, D., Bindel, D., and Shoemaker, C. A. (2019). pysot and poap: An event-driven asynchronous framework for surrogate optimization. arXiv preprint arXiv:1908.00420.
Eskinazi, I. and Fregly, B. J. (2016). An open-source toolbox for surrogate modeling of joint contact mechanics. IEEE Transactions on Biomedical Engineering, 63(2):269–277.
Ferreira, J., Pedemonte, M., and Torres, A. I. (2019). A genetic programming approach for construction of surrogate models. In Computer Aided Chemical Engineering, volume 47, pages 451–456. Elsevier.
Ibrahim, I., Silva, R., and Lowther, D. A. (2022). Application of surrogate models to the multiphysics sizing of permanent magnet synchronous motors. IEEE Transactions on Magnetics, 58(9):1–4.
Imani, M. and Braga-Neto, U. M. (2018). Particle filters for partially-observed boolean dynamical systems. Automatica, 87:238–250.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks, volume 4, pages 1942–1948. ieee.
Leal-Romo, F. d. J., Chávez-Hurtado, J. L., and Rayas-Sánchez, J. E. (2018). Selecting surrogate-based modeling techniques for power integrity analysis. In 2018 IEEE MTTS Latin America Microwave Conference (LAMC 2018), pages 1–3.
Marku, M. and Pancaldi, V. (2023). From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLOS Computational Biology, 19(8):e1011254.
Sastry, K., Goldberg, D., and Kendall, G. (2005). Genetic Algorithms, pages 97–125. Springer US, Boston, MA.
Tan, Y., Neto, F. B. L., and Neto, U. B. (2020). Pallas: Penalized maximum likelihood and particle swarms for inference of gene regulatory networks from time series data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(3):1807–1816.
Vousden, K. H. and Prives, C. (2009). Blinded by the light: the growing complexity of p53. Cell, 137(3):413–431.
Bastos Filho, C. J., Neto, F. L., Sousa, M. F. C., Pontes, M. R., and Madeiro, S. S. (2009). On the influence of the swimming operators in the fish school search algorithm. In 2009 IEEE International Conference on Systems, Man and Cybernetics, pages 5012–5017. IEEE.
Batchelor, E., Loewer, A., and Lahav, G. (2009). The ups and downs of p53: understanding protein dynamics in single cells. Nature Reviews Cancer, 9(5):371–377.
Braga-Neto, U. (2011). Optimal state estimation for boolean dynamical systems. In 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pages 1050–1054. IEEE.
Buhmann, M. D. (2000). Radial basis functions. Acta numerica, 9:1–38.
Eriksson, D., Bindel, D., and Shoemaker, C. A. (2019). pysot and poap: An event-driven asynchronous framework for surrogate optimization. arXiv preprint arXiv:1908.00420.
Eskinazi, I. and Fregly, B. J. (2016). An open-source toolbox for surrogate modeling of joint contact mechanics. IEEE Transactions on Biomedical Engineering, 63(2):269–277.
Ferreira, J., Pedemonte, M., and Torres, A. I. (2019). A genetic programming approach for construction of surrogate models. In Computer Aided Chemical Engineering, volume 47, pages 451–456. Elsevier.
Ibrahim, I., Silva, R., and Lowther, D. A. (2022). Application of surrogate models to the multiphysics sizing of permanent magnet synchronous motors. IEEE Transactions on Magnetics, 58(9):1–4.
Imani, M. and Braga-Neto, U. M. (2018). Particle filters for partially-observed boolean dynamical systems. Automatica, 87:238–250.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks, volume 4, pages 1942–1948. ieee.
Leal-Romo, F. d. J., Chávez-Hurtado, J. L., and Rayas-Sánchez, J. E. (2018). Selecting surrogate-based modeling techniques for power integrity analysis. In 2018 IEEE MTTS Latin America Microwave Conference (LAMC 2018), pages 1–3.
Marku, M. and Pancaldi, V. (2023). From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLOS Computational Biology, 19(8):e1011254.
Sastry, K., Goldberg, D., and Kendall, G. (2005). Genetic Algorithms, pages 97–125. Springer US, Boston, MA.
Tan, Y., Neto, F. B. L., and Neto, U. B. (2020). Pallas: Penalized maximum likelihood and particle swarms for inference of gene regulatory networks from time series data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(3):1807–1816.
Vousden, K. H. and Prives, C. (2009). Blinded by the light: the growing complexity of p53. Cell, 137(3):413–431.
Publicado
02/12/2024
Como Citar
AMORIM NETO, Hugo de A.; LOO, Luis; LACERDA, Marcelo G. P. de; BRAGA NETO, Ulisses; L. NETO, Fernando Buarque de.
Towards a Surrogate-assisted PALLAS algorithm for Gene Regulatory Network Inference. In: SIMPÓSIO BRASILEIRO DE BIOINFORMÁTICA (BSB), 17. , 2024, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 119-130.
ISSN 2316-1248.
DOI: https://doi.org/10.5753/bsb.2024.245586.
