Robustness versus redundancy in biological systems

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Genetic networks offer a wealth of data; this is mainly due to the genomic dimensionality rather than the samples, as the latter usually come from measurements obtained under a few experimental conditions or time points. It is therefore a challenging task to design suitable statistical models and to develop effective reverse engineering algorithms. The signature of noise is pervasive in genetic networks. For instance, in perturbation experiments only a few genes change expression value, while most genes are either noisy or constant. Consequently, a genetic regulatory network is a redundant system, due to the high-dimensionality and the dependence between genes, and also a sparse system through the gene-gene interaction matrix only partially active. In order to explore these two aspects, redundancy and sparsity, independent component analysis (ICA) is proposed as a flexible approximation model targeted to dimensionality reduction and gene feature selection.

Original languageEnglish (US)
Pages (from-to)L375-L385
JournalFluctuation and Noise Letters
Volume5
Issue number3
DOIs
StatePublished - Sep 2005
Externally publishedYes

Keywords

  • Dimensionality reduction
  • Feature selection
  • Genetic networks
  • Independent component analysis
  • Redundancy and robustness

ASJC Scopus subject areas

  • Mathematics(all)
  • Physics and Astronomy(all)

Fingerprint

Dive into the research topics of 'Robustness versus redundancy in biological systems'. Together they form a unique fingerprint.

Cite this