title: Impact of environmental inputs on reverse-engineering approach to network structures.
abstract: BACKGROUND
  Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs.
  
  
  RESULTS
  With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism.
  
  
  CONCLUSIONS
  We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.
authors: Department of Neuroscience, Columbia University, New York, NY, 10032, USA. jw2663@columbia.edu
authors: Jianhua
authors: J
authors: Wu
authors: James L
authors: JL
authors: Sinfield
authors: Vicky
authors: V
authors: Buchanan-Wollaston
authors: Jianfeng
authors: J
authors: Feng
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism*; D001683:Biological Clocks / Q000502:physiology*; D004777:Environment; D035264:Flowers / Q000235:genetics / Q000378:metabolism; D018506:Gene Expression Regulation, Plant / Q000235:genetics*; D053263:Gene Regulatory Networks / Q000235:genetics*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D049490:Systems Biology / Q000379:methods*