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