303 related articles for article (PubMed ID: 28114351)
21. Modeling signal transduction networks: a comparison of two stochastic kinetic simulation algorithms.
Pettigrew MF; Resat H
J Chem Phys; 2005 Sep; 123(11):114707. PubMed ID: 16392583
[TBL] [Abstract][Full Text] [Related]
22. Stochastic model simulation using Kronecker product analysis and Zassenhaus formula approximation.
Caglar MU; Pal R
IEEE/ACM Trans Comput Biol Bioinform; 2013; 10(5):1125-36. PubMed ID: 24384703
[TBL] [Abstract][Full Text] [Related]
23. Adjoint systems for models of cell signaling pathways and their application to parameter fitting.
Fujarewicz K; Kimmel M; Lipniacki T; Swierniak A
IEEE/ACM Trans Comput Biol Bioinform; 2007; 4(3):322-335. PubMed ID: 17666754
[TBL] [Abstract][Full Text] [Related]
24. BioBayes: a software package for Bayesian inference in systems biology.
Vyshemirsky V; Girolami M
Bioinformatics; 2008 Sep; 24(17):1933-4. PubMed ID: 18632751
[TBL] [Abstract][Full Text] [Related]
25. A scalable moment-closure approximation for large-scale biochemical reaction networks.
Kazeroonian A; Theis FJ; Hasenauer J
Bioinformatics; 2017 Jul; 33(14):i293-i300. PubMed ID: 28881983
[TBL] [Abstract][Full Text] [Related]
26. Recipes for Analysis of Molecular Networks Using the Data2Dynamics Modeling Environment.
Steiert B; Kreutz C; Raue A; Timmer J
Methods Mol Biol; 2019; 1945():341-362. PubMed ID: 30945255
[TBL] [Abstract][Full Text] [Related]
27. The Beta Workbench: a computational tool to study the dynamics of biological systems.
Dematté L; Priami C; Romanel A
Brief Bioinform; 2008 Sep; 9(5):437-49. PubMed ID: 18463130
[TBL] [Abstract][Full Text] [Related]
28. Connecting quantitative regulatory-network models to the genome.
Pan Y; Durfee T; Bockhorst J; Craven M
Bioinformatics; 2007 Jul; 23(13):i367-76. PubMed ID: 17646319
[TBL] [Abstract][Full Text] [Related]
29. Parameter estimation of dynamic biological network models using integrated fluxes.
Liu Y; Gunawan R
BMC Syst Biol; 2014 Nov; 8():127. PubMed ID: 25403239
[TBL] [Abstract][Full Text] [Related]
30. BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network.
Chen X; Chen M; Ning K
Bioinformatics; 2006 Dec; 22(23):2952-4. PubMed ID: 17005537
[TBL] [Abstract][Full Text] [Related]
31. Hybrid stochastic simplifications for multiscale gene networks.
Crudu A; Debussche A; Radulescu O
BMC Syst Biol; 2009 Sep; 3():89. PubMed ID: 19735554
[TBL] [Abstract][Full Text] [Related]
32. Detailed comparison between StochSim and SSA.
Liu Z; Cao Y
IET Syst Biol; 2008 Sep; 2(5):334-41. PubMed ID: 19045828
[TBL] [Abstract][Full Text] [Related]
33. Inference of biochemical network models in S-system using multiobjective optimization approach.
Liu PK; Wang FS
Bioinformatics; 2008 Apr; 24(8):1085-92. PubMed ID: 18321886
[TBL] [Abstract][Full Text] [Related]
34. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models.
Romers J; Thieme S; Münzner U; Krantz M
NPJ Syst Biol Appl; 2020; 6():2. PubMed ID: 31934349
[TBL] [Abstract][Full Text] [Related]
35. Gene regulatory network inference: data integration in dynamic models-a review.
Hecker M; Lambeck S; Toepfer S; van Someren E; Guthke R
Biosystems; 2009 Apr; 96(1):86-103. PubMed ID: 19150482
[TBL] [Abstract][Full Text] [Related]
36. Benchmarking of dynamic Bayesian networks inferred from stochastic time-series data.
David LA; Wiggins CH
Ann N Y Acad Sci; 2007 Dec; 1115():90-101. PubMed ID: 17925346
[TBL] [Abstract][Full Text] [Related]
37. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference.
Quach M; Brunel N; d'Alché-Buc F
Bioinformatics; 2007 Dec; 23(23):3209-16. PubMed ID: 18042557
[TBL] [Abstract][Full Text] [Related]
38. An enzyme mechanism language for the mathematical modeling of metabolic pathways.
Yang CR; Shapiro BE; Mjolsness ED; Hatfield GW
Bioinformatics; 2005 Mar; 21(6):774-80. PubMed ID: 15509612
[TBL] [Abstract][Full Text] [Related]
39. Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method.
Chatterjee A; Mayawala K; Edwards JS; Vlachos DG
Bioinformatics; 2005 May; 21(9):2136-7. PubMed ID: 15699024
[TBL] [Abstract][Full Text] [Related]
40. Parameter estimation for cell cycle ordinary differential equation (ODE) models using a grid approach.
Alfieri R; Mosca E; Merelli I; Milanesi L
Stud Health Technol Inform; 2007; 126():93-102. PubMed ID: 17476052
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
