Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Journal of Computer and System Sciences
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Discrete solutions to differential equations by metabolic P systems
Theoretical Computer Science
Structure and parameter estimation for cell systems biology models
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The metabolic algorithm for P systems: Principles and applications
Theoretical Computer Science
Evolution and oscillation in p systems: applications to biological phenomena
WMC'04 Proceedings of the 5th international conference on Membrane Computing
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
The quantification of pollutants in drinking water by use of artificial neural networks
Natural Computing: an international journal
Data analysis pipeline from laboratory to MP models
Natural Computing: an international journal
The evolution of higher-level biochemical reaction models
Genetic Programming and Evolvable Machines
Metabolic p system flux regulation by artificial neural networks
WMC'09 Proceedings of the 10th international conference on Membrane Computing
Tuning p systems for solving the broadcasting problem
WMC'09 Proceedings of the 10th international conference on Membrane Computing
Towards an evolutionary procedure for reverse-engineering biological networks
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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Metabolic P systems, also called MP systems, are discrete dynamical systems which proved to be effective for modeling biological systems. Their dynamics is generated by means of a metabolic algorithm based on "flux regulation functions". A significant problem related to the generation of MP models from experimental data concerns the synthesis of these functions. In this paper we introduce a new approach to the synthesis of MP fluxes relying on neural networks as universal function approximators, and on evolutionary algorithms as learning techniques. This methodology is successfully tested in the case study of mitotic oscillator in early amphibian embryos.