Prediction of debacle parts for robustness in a cell by using recurrent neural networks

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

Creators Kitakaze Hironori

Creators Matsuno Hiroshi

Creators Ikeda Nobuhiko

Creators Miyano Satoru

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.

[ISSN]0387-9232

[NCID]AN00031668