A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation

Published: May 17, 2021
Abstract
Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level...
Paper Details
Title
A pipeline for systematic comparison of model levels and parameter inference settings applied to negative feedback gene regulation
Published Date
May 17, 2021
Journal
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