Modelling cells reaction kinetics with artificial neural networks: A comparison of three network architectures

• Type: Article in International Conference Proceedings Book
• Date: 2003

Authors

J. Peres (Author) FEUP; Oliveira, R (Author) Other; S. Feyo de Azevedo (Author) FEUP

Conference proceedings International

Title: EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING - 13

Pages: 839-844

13th European Symposium on Computer Aided Process Engineering (ESCAPE-13)

LAPPEENRANTA, FINLAND, JUN 01-04, 2003

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-000-JM9

DOI: 10.1016/s1570-7946(03)80221-3

Abstract (EN): The present work compares three neural network architectures for modelling reaction kinetics in biological systems: the Mixture of Experts (ME) network, the Backpropagation (BP) network and the Radial Basis Function (RBF) network. The methods are outlined for the case of the growth kinetics of the Saccharomyces cerevisae yeast. The S. cerevisae yeast is able to grow through 3 different pathways. The main results show that a ME network with 3 linear expert modules was able to discriminate between the 3 pathways. The network was trained with the Expectation Maximisation method. A Gaussian gating system produced three input space partitions, one for each of the pathways. The 3 expert modules developed expertise in describing the kinetics of each of the pathways.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6