Using Python for Spatially Resolved Modeling with Simmune

Bastian R Angermann; Martin Meier-Schellersheim

doi:10.1007/978-1-4939-9102-0_7

Using Python for Spatially Resolved Modeling with Simmune

Methods Mol Biol. 2019:1945:161-177. doi: 10.1007/978-1-4939-9102-0_7.

Authors

Bastian R Angermann¹, Martin Meier-Schellersheim²

Affiliations

¹ Computational Biology Section, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. bastianra@gmail.com.
² Computational Biology Section, Laboratory of Systems Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. mms@niaid.nih.gov.

PMID: 30945246
DOI: 10.1007/978-1-4939-9102-0_7

Abstract

Mechanistic models are an important tool to gain insights about the quantitative behavior of cell-biological signal transduction networks. Here we show how Simmune can be used in conjunction with IPython to create repeatable, self-contained analyses of signal transduction processes in spatially inhomogeneous environments.

Keywords: GPCR signaling; Gradient sensing; IPython; Mechanistic modeling.

Publication types

Research Support, N.I.H., Intramural

MeSH terms

Computational Biology / methods*
Computer Simulation
Humans
Models, Biological*
Programming Languages
Signal Transduction / genetics*
Software*