TY - GEN
T1 - Gene Regulatory Network for the Tryptophanase Operon Under the Threshold Boolean Network Model
AU - Encina-Chacana, Felipe
AU - Ruz, Gonzalo A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - This paper presents an evolutionary computation framework that uses genetic algorithm and particle swarm optimization to infer a threshold Boolean network of the Tryptophanase operon. A unique feature of this network is that it exhibits bistability, converging to two fixed points under certain conditions. We proposed a fitness function to achieve this, ensuring the network showed the desired dynamics, particularly the bistability property. Additionally, we explored and analyzed the results obtained from 500 simulations conducted by each algorithm. The genetic algorithm could infer 23 different networks with perfect scores, but particle swarm optimization could not infer any. The results showed that, in general, the genetic algorithm could explore the search space more effectively, obtaining networks with more edges than particle swarm optimization, thus allowing it to find networks satisfying the biological restriction of the model inferred.
AB - This paper presents an evolutionary computation framework that uses genetic algorithm and particle swarm optimization to infer a threshold Boolean network of the Tryptophanase operon. A unique feature of this network is that it exhibits bistability, converging to two fixed points under certain conditions. We proposed a fitness function to achieve this, ensuring the network showed the desired dynamics, particularly the bistability property. Additionally, we explored and analyzed the results obtained from 500 simulations conducted by each algorithm. The genetic algorithm could infer 23 different networks with perfect scores, but particle swarm optimization could not infer any. The results showed that, in general, the genetic algorithm could explore the search space more effectively, obtaining networks with more edges than particle swarm optimization, thus allowing it to find networks satisfying the biological restriction of the model inferred.
KW - Bistability
KW - Boolean Networks
KW - Genetic Algorithm
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=85210231166&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-76604-6_12
DO - 10.1007/978-3-031-76604-6_12
M3 - Conference contribution
AN - SCOPUS:85210231166
SN - 9783031766039
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 174
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 27th Iberoamerican Congress, CIARP 2024, Proceedings
A2 - Hernández-García, Ruber
A2 - Barrientos, Ricardo J.
A2 - Velastin, Sergio A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2024
Y2 - 26 November 2024 through 29 November 2024
ER -