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. CONCLUSION: 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.
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Claims 3
Claim · model-written summary
The harmonic causal method was successfully applied to Arabidopsis flowering gene microarray data.
- Taxon
- Arabidopsis Thaliana
Arabidopsis thalianae0 - Outcome
- successfully appliede0
- Qualifier
- microarray data of the flowering genese0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana”
Claim · model-written summary
Environmental inputs were crucial for correct inference of the studied network structures.
- Taxon
- Arabidopsis ThalianaArabidopsis thalianae1
- Outcome
- correctly inferring network structurese0
- Comparator
- environmental inputse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“environmental inputs are crucial for correctly inferring network structures”
e1“flowering genes of the model plant Arabidopsis Thaliana”
Claim · model-written summary
The harmonic causal method detected environmental inputs and uncovered network structures, especially for periodically oscillating biological data.
- Taxon
- Arabidopsis ThalianaArabidopsis thalianae1
- Outcome
- detect environment inputs and uncover network structurese0
- Qualifier
- especially when the biological data exhibit periodic oscillationse0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
e1“flowering genes of the model plant Arabidopsis Thaliana”