about
Multi-state modeling of biomoleculesModeling for (physical) biologists: an introduction to the rule-based approachModelling the Structure and Dynamics of Biological PathwaysQuantitative and logic modelling of molecular and gene networksStochastic Simulation Service: Bridging the Gap between the Computational Expert and the BiologistAutomated detection of discourse segment and experimental types from the text of cancer pathway results sectionsLASSIE: simulating large-scale models of biochemical systems on GPUs.Diagnostics for stochastic genome-scale modeling via model slicing and debuggingA high-level language for rule-based modelling.Fractional killing arises from cell-to-cell variability in overcoming a caspase activity thresholdPycellerator: an arrow-based reaction-like modelling language for biological simulations.Constant-complexity stochastic simulation algorithm with optimal binning.Competition and allostery govern substrate selectivity of cyclooxygenase-2.Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems.MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.GPU-powered model analysis with PySB/cupSODA.Modelling the molecular mechanisms of aging.Rule-based modeling with Virtual Cell.BioNetGen 2.2: advances in rule-based modeling.The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales.MESOSCOPIC MODELING OF STOCHASTIC REACTION-DIFFUSION KINETICS IN THE SUBDIFFUSIVE REGIME.PyDREAM: High-dimensional parameter inference for biological models in Python.BCL-2 family proteins: changing partners in the dance towards death.Automated visualization of rule-based models.Emerging whole-cell modeling principles and methods.MAMMOTh: A new database for curated mathematical models of biomolecular systems.From word models to executable models of signaling networks using automated assembly.Biosynthesis of Orthogonal Molecules Using Ferredoxin and Ferredoxin-NADP+ Reductase Systems Enables Genetically Encoded PhyB Optogenetics.A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens.An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems.Tellurium notebooks-An environment for reproducible dynamical modeling in systems biology.
P2860
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P2860
description
2013 nî lūn-bûn
@nan
2013 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Programming biological models in Python using PySB
@ast
Programming biological models in Python using PySB
@en
Programming biological models in Python using PySB
@nl
type
label
Programming biological models in Python using PySB
@ast
Programming biological models in Python using PySB
@en
Programming biological models in Python using PySB
@nl
prefLabel
Programming biological models in Python using PySB
@ast
Programming biological models in Python using PySB
@en
Programming biological models in Python using PySB
@nl
P2093
P2860
P356
P1476
Programming biological models in Python using PySB
@en
P2093
Carlos F Lopez
Jeremy L Muhlich
John A Bachman
Peter K Sorger
P2860
P356
10.1038/MSB.2013.1
P577
2013-01-01T00:00:00Z