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Temporal dependency, this also extends to the extracted networks. Since transcriptomics data do not necessarily reflect enzyme activities (due to post-transcriptional modification and regulatory effects), we use the results from the analysis of the expression data only as indicator for the activity of the respective reaction rather that definite values. Furthermore, the approach does not rely on a condition-specific objective function, e.g., biomass yield. Thus, it overcomes one of the drawbacks of FBA, associated with the selection of a suitable objective function, not only for different but also varying conditions. Finally, comparative analysis with MADE, a state-of-the-art method, demonstrates high overlap between the extracted networks. However, in comparison to MADE, our approach?Topfer et al. BMC Systems Biology 2012, 6:148 http://www.biomedcentral.com/1752-0509/6/Page 9 ofresults in consistently smaller networks amenable to EFM analysis.6. 7. 8.ConclusionWe applied our approach to time-resolved transcriptomics data from heat and cold shock experiments in E. coli. The predictions from the integration of the largescale metabolic networks with time-series data are in line with observations and conclusions from existing experimental studies. Moreover, analysis of the fractional appearance profiles for heat and Capmatinib cold stress adaptation in E. coli have generated interesting hypothesis to be validated in future experiments. Finally, the proposed method and the presence of the two types of profiles, resulting from its application on a well-investigated model organism, indicate the "tug-of-war" between the systemic properties of robustness and adaptability necessary for maintenance of major processes while settling in a new metabolic state.9. 10.11.12. 13.14.15.16.Additional fileAdditional file 1: Supplementary Information. 17. Competing interests The authors declare that they have no competing interests.18.Authors' contributions NT and ZN designed the study and wrote the manuscript. NT performed the study. SJ, NT and ZN interpreted the results. All authors read and approved the final manuscript.19.20.Acknowledgements The authors thank the Max-Planck society for financial support. 21. Author details1 Systems Biology and Mathematical Modeling Group, Max-Planck-Institute ofMolecular Plant Physiology, 14476 Potsdam, Germany. 2 ETH Zurich, Institute of Molecular Systems Biology, 8093 Zurich, Switzerland.22.Received: 1 May 2012 Accepted: 7 November 2012 Published: 30 November 2012 References 1. Varma A, Palsson BO: Stoichiometric flux balance models quantitatively predict growth and metabolic PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/16989806 by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 1994, 60(10):3724?731. 2. Leroi AM, Bennett AF, Lenski RE: Temperature acclimation and competitive fitness: an experimental test of the beneficial acclimation assumption. Proc Nat Acad Sci USA 1994, 91(5):1917?921. 3. Kitano H: Biological robustness. Nat Rev Gene 2004, 5(11):826?37. 4. Larhlimi A, Blachon S, Selbig J, Nikoloski Z: Robustness of metabolic networks: A review of existing definitions. Bio Syst 2011, 106:1?. 5. Alon U, Surette MG, Barkai N, Leibler S: Robustness in bacterial chemotaxis. Nature 1999, 397(6715):168?71.23. 24.25. 26. 27.28.29.Barkai N, Leibler S: Robustness in simple biochemical networks to transfer and process information. Nature 1997, 387(6636):913?17. Kitano H: Towards a theory of biological robustness. Mol Syst Biol 2007, 3:137. Ideker T, Ozier O,.