PyDREAM: High-dimensional parameter inference for biological models in Python.

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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2017 nî lūn-bûn

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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PyDREAM: High-dimensional parameter inference for biological models in Python.

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P1476

PyDREAM: High-dimensional parameter inference for biological models in Python.

@en

P2093

Alfonso Valencia

http://www.w3.org/2001/XMLSchema#string

Erin M Shockley

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P2860

Modeling for (physical) biologists: an introduction to the rule-based approach

Models of signalling networks - what cell biologists can gain from them and give to them

An empirical Bayesian approach for model-based inference of cellular signaling networks

Programming biological models in Python using PySB

Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes.

Competition and allostery govern substrate selectivity of cyclooxygenase-2.

Integrated experimental and model-based analysis reveals the spatial aspects of EGFR activation dynamics.

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments.

Statistical mechanical approaches to models with many poorly known parameters.

Dynamics within the CD95 death-inducing signaling complex decide life and death of cells.

P304

695-697

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P31

scholarly article

P356

10.1093/BIOINFORMATICS/BTX626

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P407

English

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Carlos F. Lopez

Jasper A. Vrugt

P577

2018-02-01T00:00:00Z

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P698

29028896

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P932

5860607

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