Parameter uncertainty quantification using surrogate models applied to a spatial model of yeast mating polarization-Reference-Cited by-同舟云学术

Parameter uncertainty quantification using surrogate models applied to a spatial model of yeast mating polarization

Published:2018-05-29 Issue:5 Volume:14 Page:e1006181
ISSN:1553-7358
Container-title:PLOS Computational Biology
language:en
Short-container-title:PLoS Comput Biol

Author:

Renardy Marissa,Yi Tau-Mu^ORCID,Xiu Dongbin,Chou Ching-Shan^ORCID

Funder

National Science Foundation

National Institutes of Health

Air Force Office of Scientific Research

Defense Sciences Office, DARPA

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference77 articles.

1. Mathematical models in biology;L Edelstein-Keshet,2005

2. Introduction to Mathematical Biology: Modeling, Analysis, and Simulations;CS Chou,2016

3. The Nature of Mathematical Modeling;NA Gershenfeld,1999

4. Computational cell biology: spatiotemporal simulation of cellular events;BM Slepchenko;Annu Rev Biophys Biomol Struct,2002

5. Computational Cell Biology;CP Fall,2007

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