title: Inference of gene networks--application to Bifidobacterium.
abstract: BACKGROUND
  The reliable and reproducible identification of gene interaction networks represents one of the grand challenges of both modern molecular biology and computational sciences. Approaches based on careful collection of literature data and network topological analysis, applied to unicellular organisms, have proven to offer results applicable to medical therapies. However, when little a priori knowledge is available, other approaches, not relying so strongly on previous literature, must be used. We propose here a novel algorithm (based on ordinary differential equations) able to infer the interactions occurring among genes, starting from gene expression steady state data.
  
  
  RESULTS
  The algorithm was first validated on synthetic and real benchmarks. It was then applied to the reconstruction of the core of the amino acids metabolism in Bifidobacterium longum, an essential, yet poorly known player in the human gut intestinal microbiome, known to be related to the onset of important diseases, such as metabolic syndromes. Our results show how computational approaches can offer effective tools for applications with the identification of potential new biological information.
  
  
  BACKGROUND
  The software is available at www.bioconductor.org and at www.picb.ac.cn/ClinicalGenomicNTW/temp2.html.
authors.affiliation: Key Laboratory of Computational Biology, Chinese Academy of Sciences Max Planck Institute Partner Institute for Computational Biology (CAS-MPG), Shanghai, People's Republic of China.
authors.forename: Darong
authors.initials: D
authors.lastname: Lai
authors.forename: Xinyi
authors.initials: X
authors.lastname: Yang
authors.forename: Gang
authors.initials: G
authors.lastname: Wu
authors.forename: Yuanhua
authors.initials: Y
authors.lastname: Liu
authors.forename: Christine
authors.initials: C
authors.lastname: Nardini
mesh_terms: D000465:Algorithms; D001644:Bifidobacterium / Q000235:genetics* / Q000378:metabolism; D053263:Gene Regulatory Networks; D012984:Software