Error Bound for Hill-Function Approximations in a Class of Stochastic Transcriptional Network Models
Name
CDC_time_scale_separation_v11-4.pdf
Description
Main article
Size
1.82 MB
Format
Adobe PDF
Checksum (MD5)
9e3894dee2315acbc2c1ad5bb189c03a
Author(s)
Hirsch, Dylan
Grunberg, Theodore W.
Del Vecchio, Domitilla
Date Issued
April 1, 2023
Abstract
Hill functions are often used in stochastic models of gene regulation to approximate the dependence of gene activity on the concentration of the transcription factor which regulates the gene. It is incompletely known, however, how much error one may incur from this approximation. We investigate this question in the context of transcriptional networks (TN). In particular, under the assumption of rapid binding and unbinding of transcription factors with their gene targets, we bound the approximation error associated with Hill functions for TNs in which each transcription factor regulates a gene in a one-to-one fashion and each regulated gene produces a single transcription factor. We also assume that transcription factors do not homodimerize or heterodimerize and that each gene only has a single transcription factor binding site. These results are pertinent for the modeling of TNs and may also carry relevance for more general biological processes.
Description
Extended version
Subjects
Biomolecular systems
Systems biology
Genetic regulatory systems
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