Prediction of debacle parts for robustness in a cell by using recurrent neural networks
大島商船高等専門学校紀要 Volume 37
Page 1-7
published_at 2004-12
Title
リカレントニューラルネットワークを用いた 細胞内反応システムにおけるロバストネス瓦解部位予測
Prediction of debacle parts for robustness in a cell by using recurrent neural networks
Source Identifiers
Creator Keywords
Petri Net
Genomic Object Net
Recurrent Neural Networks
BPTT
Living organisms have sophisticated control mecha,nism to keep biological system robust against abnormalities from inside/outside of them. However, at the same time, the control mechanism has a critical point at which the stability can be broken easily. This paper proposes a method to find critical points of the control mechanism in a biological pathway described by hybrid functional Petri nets (HFPN). In this method. HFPNs are converted to a recurrent neural networks (RNNs), checking robustness of the biological pathway with the RNN, a^nd finding some crucial points for the robustness. An example to apply this method to an apoptosis pathway is also presented.
Languages
jpn
Resource Type
departmental bulletin paper
Publishers
大島商船高等専門学校
Date Issued
2004-12
File Version
Version of Record
Access Rights
open access
Relations
[ISSN]0387-9232
[NCID]AN00031668