Network Reconstitution for Quantitative Subnetwork Interaction Analysis

  • Fumiaki KatagiriEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1578)


A fundamental task in systems biology is to quantify the contributions of the systems’ parts and their interactions. Here I describe a powerful concept and tool for this purpose: network reconstitution. Genotypes of an organism that represent all possible combinations of the subnetworks in question will be quantitatively phenotyped. The quantitative phenotype data is analyzed using an R script to obtain estimates for single subnetwork contributions and their interactions.

Key words

Subnetwork interactions Network reconstitution Signaling allocation Combinatorial genotypes Quantitative phenotype 



I thank Rachel Hillmer for critical reading of the manuscript. This work was supported by grants from National Science Foundation, MCB-0918908, IOS-1121425, and MCB-1518058.


  1. 1.
    Tsuda K, Sato M, Stoddard T, Glazebrook J, Katagiri F (2009) Network properties of robust immunity in plants. PLoS Genet 5(12):e1000772CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Sato M, Tsuda K, Wang L, Coller J, Watanabe Y, Glazebrook J, Katagiri F (2010) Network modeling reveals prevalent negative regulatory relationships between signaling sectors in Arabidopsis immune signaling. PLoS Pathog 6(7):e1001011CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Kim Y, Tsuda K, Igarashi D, Hillmer RA, Sakakibara H, Myers CL, Katagiri F (2014) Mechanisms underlying robustness and tunability in a plant immune signaling network. Cell Host Microbe 15(1):84–94CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Katagiri F, Tsuda K (2010) Understanding the plant immune system. Mol Plant-Microbe Interact 23(12):1531–1536CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Plant and Microbial Biology, Microbial and Plant Genomics InstituteUniversity of MinnesotaSt. PaulUSA

Personalised recommendations