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Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Impact of environmental inputs on reverse-engineering approach to network structures.

Jianhua Wu1, James L Sinfield, Vicky Buchanan-Wollaston

  • 1Department of Neuroscience, Columbia University, New York, NY, 10032, USA. jw2663@columbia.edu

BMC Systems Biology
|December 8, 2009
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Summary

Environmental inputs significantly impact biological network structures. Our new harmonic causal method effectively identifies these inputs and reveals network dynamics, especially for oscillating biological data.

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Area of Science:

  • System biology
  • Computational biology
  • Bioinformatics

Background:

  • Inferring biological network structures is key in systems biology.
  • Existing methods like Bayesian networks and Granger causality often neglect environmental influences.
  • Understanding environmental impacts is crucial for accurate biological network modeling.

Purpose of the Study:

  • To develop a novel systems biology approach incorporating environmental inputs for network inference.
  • To identify how external factors, such as sunlight, influence biological networks.
  • To uncover causal network structures affected by environmental dynamics.

Main Methods:

  • Representing environmental inputs using harmonic oscillators.
  • Integrating harmonic oscillators with Granger causality for input identification.
  • Applying the harmonic causal method to model biological networks, including plant gene expression data.

Main Results:

  • The proposed method successfully identifies environmental inputs and infers causal network structures.
  • Demonstrated effectiveness on simulated data and real-world microarray data from Arabidopsis thaliana.
  • Identified genes directly influenced by sunlight and their associated flowering metabolism networks.

Conclusions:

  • Environmental inputs are critical for accurate biological network inference.
  • The harmonic causal method is a powerful tool for detecting environmental influences and network structures.
  • This approach is particularly effective for biological data exhibiting periodic oscillations.