title: A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks.
abstract: BACKGROUND
  Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation.
  
  
  RESULTS
  We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model.
  
  
  CONCLUSIONS
  We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.
authors.affiliation: Institute for Advanced Biosciences, Keio University, Tsuruoka, 997-0035, Japan. chaos@sfc.keio.ac.jp
authors.forename: Katsuyuki
authors.initials: K
authors.lastname: Yugi
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Nakayama
authors.forename: Shigen
authors.initials: S
authors.lastname: Kojima
authors.forename: Tomoya
authors.initials: T
authors.lastname: Kitayama
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
mesh_terms: D020816:Amino Acid Motifs; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004926:Escherichia coli / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D023281:Genomics / Q000379:methods; D007700:Kinetics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D012270:Ribosomes / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D012984:Software; D049490:Systems Biology