title: Prediction of debacle points for robustness of biological pathways by using recurrent neural networks.
abstract: Living organisms have ingenious control mechanisms in which many molecular interactions work for keeping their normal activities against disturbances inside and outside of them. However, at the same time, the control mechanism has debacle points at which the stability can be broken easily. This paper proposes a new method which uses recurrent neural network for predicting debacle points in an hybrid functional Petri net model of a biological pathway. Evaluation on an apoptosis signaling pathway indicates that the rates of 96.5 % of debacle points and 65.5 % of non-debacle points can be predicted by the proposed method.
authors.affiliation: Oshima College of Maritime Technology, 1091-1 Oshima-cho, Oshima-gun, Yamaguchi 742-2193, Japan. kitakaze@oshima-k.ac.jp
authors.forename: Hironori
authors.initials: H
authors.lastname: Kitakaze
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Matsuno
authors.forename: Nobuhiko
authors.initials: N
authors.lastname: Ikeda
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D017209:Apoptosis; D020169:Caspases / Q000378:metabolism; D019295:Computational Biology; D003198:Computer Simulation; D005188:False Negative Reactions; D005189:False Positive Reactions; D053222:Fas Ligand Protein; D005246:Feedback; D007700:Kinetics; D008562:Membrane Glycoproteins / Q000378:metabolism; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015398:Signal Transduction; D012984:Software; D049490:Systems Biology / Q000379:methods*; D048069:Tumor Necrosis Factors / Q000378:metabolism
chemical_list: D053222:Fas Ligand Protein; D008562:Membrane Glycoproteins; D048069:Tumor Necrosis Factors; D020169:Caspases