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Items: 1 to 20 of 141

1.

Dynamic patterns of information flow in complex networks.

Harush U, Barzel B.

Nat Commun. 2017 Dec 19;8(1):2181. doi: 10.1038/s41467-017-01916-3.

2.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks.

Laniau J, Frioux C, Nicolas J, Baroukh C, Cortes MP, Got J, Trottier C, Eveillard D, Siegel A.

PeerJ. 2017 Oct 12;5:e3860. doi: 10.7717/peerj.3860. eCollection 2017.

3.

Digging into the low molecular weight peptidome with the OligoNet web server.

Liu Y, Forcisi S, Lucio M, Harir M, Bahut F, Deleris-Bou M, Krieger-Weber S, Gougeon RD, Alexandre H, Schmitt-Kopplin P.

Sci Rep. 2017 Sep 15;7(1):11692. doi: 10.1038/s41598-017-11786-w.

4.

Bioactive Potential of 3D-Printed Oleo-Gum-Resin Disks: B. papyrifera, C. myrrha, and S. benzoin Loading Nanooxides-TiO2, P25, Cu2O, and MoO3.

Horst DJ, Tebcherani SM, Kubaski ET, de Almeida Vieira R.

Bioinorg Chem Appl. 2017;2017:6398167. doi: 10.1155/2017/6398167. Epub 2017 Jul 25.

5.

SteadyCom: Predicting microbial abundances while ensuring community stability.

Chan SHJ, Simons MN, Maranas CD.

PLoS Comput Biol. 2017 May 15;13(5):e1005539. doi: 10.1371/journal.pcbi.1005539. eCollection 2017 May.

6.

Microbial consortia at steady supply.

Taillefumier T, Posfai A, Meir Y, Wingreen NS.

Elife. 2017 May 5;6. pii: e22644. doi: 10.7554/eLife.22644.

7.

Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways.

MacGillivray M, Ko A, Gruber E, Sawyer M, Almaas E, Holder A.

Sci Rep. 2017 Mar 21;7(1):268. doi: 10.1038/s41598-017-00170-3.

8.

In silico profiling of cell growth and succinate production in Escherichia coli NZN111.

Jian X, Li N, Zhang C, Hua Q.

Bioresour Bioprocess. 2016;3(1):48. Epub 2016 Nov 15.

9.

Stoichiometric Representation of Gene-Protein-Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction.

Machado D, Herrgård MJ, Rocha I.

PLoS Comput Biol. 2016 Oct 6;12(10):e1005140. doi: 10.1371/journal.pcbi.1005140. eCollection 2016 Oct.

10.

Exploring complex cellular phenotypes and model-guided strain design with a novel genome-scale metabolic model of Clostridium thermocellum DSM 1313 implementing an adjustable cellulosome.

Thompson RA, Dahal S, Garcia S, Nookaew I, Trinh CT.

Biotechnol Biofuels. 2016 Sep 6;9(1):194. doi: 10.1186/s13068-016-0607-x. eCollection 2016.

11.

Reconstruction of the Fatty Acid Biosynthetic Pathway of Exiguobacterium antarcticum B7 Based on Genomic and Bibliomic Data.

Kawasaki R, Baraúna RA, Silva A, Carepo MS, Oliveira R, Marques R, Ramos RT, Schneider MP.

Biomed Res Int. 2016;2016:7863706. doi: 10.1155/2016/7863706. Epub 2016 Aug 9.

12.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

Mih N, Brunk E, Bordbar A, Palsson BO.

PLoS Comput Biol. 2016 Jul 28;12(7):e1005039. doi: 10.1371/journal.pcbi.1005039. eCollection 2016 Jul.

13.

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow.

Brunk E, George KW, Alonso-Gutierrez J, Thompson M, Baidoo E, Wang G, Petzold CJ, McCloskey D, Monk J, Yang L, O'Brien EJ, Batth TS, Martin HG, Feist A, Adams PD, Keasling JD, Palsson BO, Lee TS.

Cell Syst. 2016 May 25;2(5):335-46. doi: 10.1016/j.cels.2016.04.004. Epub 2016 May 19.

14.

The Swine Plasma Metabolome Chronicles "Many Days" Biological Timing and Functions Linked to Growth.

Bromage TG, Idaghdour Y, Lacruz RS, Crenshaw TD, Ovsiy O, Rotter B, Hoffmeier K, Schrenk F.

PLoS One. 2016 Jan 6;11(1):e0145919. doi: 10.1371/journal.pone.0145919. eCollection 2016.

15.

The propagation of perturbations in rewired bacterial gene networks.

Baumstark R, Hänzelmann S, Tsuru S, Schaerli Y, Francesconi M, Mancuso FM, Castelo R, Isalan M.

Nat Commun. 2015 Dec 16;6:10105. doi: 10.1038/ncomms10105.

16.

Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.

Chen X, Zhao M, Qu H.

Biomed Res Int. 2015;2015:328568. doi: 10.1155/2015/328568. Epub 2015 Oct 1.

17.

A metabolomics-based strategy for identification of gene targets for phenotype improvement and its application to 1-butanol tolerance in Saccharomyces cerevisiae.

Teoh ST, Putri S, Mukai Y, Bamba T, Fukusaki E.

Biotechnol Biofuels. 2015 Sep 15;8:144. doi: 10.1186/s13068-015-0330-z. eCollection 2015.

18.

Mapping high-growth phenotypes in the flux space of microbial metabolism.

Güell O, Massucci FA, Font-Clos F, Sagués F, Serrano MÁ.

J R Soc Interface. 2015 Sep 6;12(110):0543. doi: 10.1098/rsif.2015.0543.

19.

Obstructions to Sampling Qualitative Properties.

Reimers AC.

PLoS One. 2015 Aug 19;10(8):e0135636. doi: 10.1371/journal.pone.0135636. eCollection 2015.

20.

Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle.

Capuani F, De Martino D, Marinari E, De Martino A.

Sci Rep. 2015 Jul 7;5:11880. doi: 10.1038/srep11880.

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