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Items: 1 to 20 of 121

1.

Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.

Khan FM, Schmitz U, Nikolov S, Engelmann D, Pützer BM, Wolkenhauer O, Vera J.

Biochim Biophys Acta. 2014 Jan;1844(1 Pt B):289-98. doi: 10.1016/j.bbapap.2013.05.007.

PMID:
23692959
2.

Global analysis of phosphorylation networks in humans.

Hu J, Rho HS, Newman RH, Hwang W, Neiswinger J, Zhu H, Zhang J, Qian J.

Biochim Biophys Acta. 2014 Jan;1844(1 Pt B):224-31. doi: 10.1016/j.bbapap.2013.03.009.

3.

MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology.

Vera J, Lai X, Schmitz U, Wolkenhauer O.

Adv Exp Med Biol. 2013;774:55-76. doi: 10.1007/978-94-007-5590-1_4. Review.

PMID:
23377968
4.

Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

Samaga R, Klamt S.

Cell Commun Signal. 2013 Jun 26;11(1):43. doi: 10.1186/1478-811X-11-43.

5.

A computational framework for qualitative simulation of nonlinear dynamical models of gene-regulatory networks.

Ironi L, Panzeri L.

BMC Bioinformatics. 2009 Oct 15;10 Suppl 12:S14. doi: 10.1186/1471-2105-10-S12-S14.

6.

Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization.

Hasegawa T, Yamaguchi R, Nagasaki M, Miyano S, Imoto S.

PLoS One. 2014 Aug 27;9(8):e105942. doi: 10.1371/journal.pone.0105942.

7.

Modeling transcriptional control in gene networks--methods, recent results, and future directions.

Smolen P, Baxter DA, Byrne JH.

Bull Math Biol. 2000 Mar;62(2):247-92. Review.

PMID:
10824430
8.

Integrated module and gene-specific regulatory inference implicates upstream signaling networks.

Roy S, Lagree S, Hou Z, Thomson JA, Stewart R, Gasch AP.

PLoS Comput Biol. 2013;9(10):e1003252. doi: 10.1371/journal.pcbi.1003252.

9.

Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive.

Eberhardt M, Lai X, Tomar N, Gupta S, Schmeck B, Steinkasserer A, Schuler G, Vera J.

Methods Mol Biol. 2016;1386:135-79. doi: 10.1007/978-1-4939-3283-2_9. Review.

PMID:
26677184
10.

Mesoscopic modeling as a starting point for computational analyses of cystic fibrosis as a systemic disease.

Voit EO.

Biochim Biophys Acta. 2014 Jan;1844(1 Pt B):258-70. doi: 10.1016/j.bbapap.2013.03.023.

11.

Estimating dynamic models for gene regulation networks.

Cao J, Zhao H.

Bioinformatics. 2008 Jul 15;24(14):1619-24. doi: 10.1093/bioinformatics/btn246.

12.

Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

Berestovsky N, Zhou W, Nagrath D, Nakhleh L.

PLoS Comput Biol. 2013;9(11):e1003306. doi: 10.1371/journal.pcbi.1003306.

13.

MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model.

Le Béchec A, Portales-Casamar E, Vetter G, Moes M, Zindy PJ, Saumet A, Arenillas D, Theillet C, Wasserman WW, Lecellier CH, Friederich E.

BMC Bioinformatics. 2011 Mar 4;12:67. doi: 10.1186/1471-2105-12-67.

14.

Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks.

Hsieh WT, Tzeng KR, Ciou JS, Tsai JJ, Kurubanjerdjit N, Huang CH, Ng KL.

BMC Syst Biol. 2015;9 Suppl 1:S5. doi: 10.1186/1752-0509-9-S1-S5.

15.

Modeling gene regulatory network motifs using Statecharts.

Fioravanti F, Helmer-Citterich M, Nardelli E.

BMC Bioinformatics. 2012 Mar 28;13 Suppl 4:S20. doi: 10.1186/1471-2105-13-S4-S20.

16.

Incorporating motif analysis into gene co-expression networks reveals novel modular expression pattern and new signaling pathways.

Ma S, Shah S, Bohnert HJ, Snyder M, Dinesh-Kumar SP.

PLoS Genet. 2013;9(10):e1003840. doi: 10.1371/journal.pgen.1003840.

17.

Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling.

Mazur J, Ritter D, Reinelt G, Kaderali L.

BMC Bioinformatics. 2009 Dec 28;10:448. doi: 10.1186/1471-2105-10-448.

19.

Comparing different ODE modelling approaches for gene regulatory networks.

Polynikis A, Hogan SJ, di Bernardo M.

J Theor Biol. 2009 Dec 21;261(4):511-30. doi: 10.1016/j.jtbi.2009.07.040.

PMID:
19665034
20.

Characterizing regulatory path motifs in integrated networks using perturbational data.

Joshi A, Van Parys T, Peer YV, Michoel T.

Genome Biol. 2010;11(3):R32. doi: 10.1186/gb-2010-11-3-r32.

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