An Effective Method for the Inference of Reduced S-system Models of Genetic Networks

• Kimura Shuhei

  Graduate School of Engineering, Tottori University

• Sato Masanao

  National Institute for Basic Biology, Okazaki Institute for Integrative Bioscience, National Institute for Natural Sciences

• Okada-Hatakeyama Mariko

  RIKEN Center for Integrative Medical Sciences

抄録

The inference of genetic networks is a problem to obtain mathematical models that can explain observed time-series of gene expression levels. A number of models have been proposed to describe genetic networks. The S-system model is one of the most studied models among them. Due to its advantageous features, numerous inference algorithms based on the S-system model have been proposed. The number of the parameters in the S-system model is however larger than those of the other well-studied models. Therefore, when trying to infer S-system models of genetic networks, we need to provide a larger amount of gene expression data to the inference method. In order to reduce the amount of gene expression data required for an inference of genetic networks, this study simplifies the S-system model by fixing some of its parameters to 0. In this study, we call this simplified S-system model a reduced S-system model. We then propose a new inference method that estimates the parameters of the reduced S-system model by minimizing two-dimensional functions. Finally, we check the effectiveness of the proposed method through numerical experiments on artificial and actual genetic network inference problems.

収録刊行物

• Information and Media Technologies

  Information and Media Technologies 10 (1), 166-174, 2015

  Information and Media Technologies 編集運営会議

キーワード

• reduced S-system model
• genetic network
• linear programming problem
• decoupling

詳細情報

• CRID: 1390282680241947776
• NII論文ID: 130004946341
• DOI: 10.11185/imt.10.166
• ISSN: 18810896
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可