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Objective criteria that can be used to evaluate dynamical changes in data are therefore important to filter experimental noise and to enable extraction of unexpected, biologically important information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here we demonstrate the effectiveness of a Markov model, named the Linear Dynamical System, to simulate the dynamics of a transcript or metabolite time series, and propose a probabilistic index that enables detection of time-sensitive changes. This method was applied to time series datasets from<jats:italic>Bacillus subtilis<\/jats:italic>and<jats:italic>Arabidopsis thaliana<\/jats:italic>grown under stress conditions; in the former, only gene expression was studied, whereas in the latter, both gene expression and metabolite accumulation. Our method not only identified well-known changes in gene expression and metabolite accumulation, but also detected novel changes that are likely to be responsible for each stress response condition.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This general approach can be applied to any time-series data profile from which one wishes to identify elements responsible for state transitions, such as rapid environmental adaptation by an organism.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-8-343","type":"journal-article","created":{"date-parts":[[2007,9,18]],"date-time":"2007-09-18T18:13:22Z","timestamp":1190139202000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Predicting state transitions in the transcriptome and metabolome using a linear dynamical system model"],"prefix":"10.1186","volume":"8","author":[{"given":"Ryoko","family":"Morioka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigehiko","family":"Kanaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masami Y","family":"Hirai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitsuru","family":"Yano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naotake","family":"Ogasawara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuki","family":"Saito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2007,9,18]]},"reference":[{"key":"1715_CR1","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1089\/106652700750050961","volume":"7","author":"N Friedman","year":"2000","unstructured":"Friedman N, Linial M, Nachman I, Pe'er D: Using Bayesian network to analyze expression data. 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