title: Ð¢ÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·Ð½ÑÑ Ð¾ÑÐµÐ½Ð¾Ðº ÑÐ½ÐµÑÐ³Ð¸Ð¸ ÑÐ²ÑÐ·ÑÐ²Ð°Ð½Ð¸Ñ ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ð¾Ð½Ð½ÑÑ ÑÐ°ÐºÑÐ¾ÑÐ¾Ð² Ñ ÐÐÐ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸ Ð² Ð´ÑÐ¾Ð·Ð¾ÑÐ¸Ð»Ðµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Testing of different estimates of transcription factor-DNA binding energy in the context of segmentation gene regulatory network model in Drosophila
Creators: name: ÐÐµÐ»ÑÐ½Ð¸ÐºÐ¾Ð²Ð°, ÐÐ½Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¿Ð¾Ð·Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð²ÐµÑÐ¾Ð²ÑÐµ Ð¼Ð°ÑÑÐ¸ÑÑ
ÑÑÐ°Ð½ÑÐºÑÐ¸Ð¿ÑÐ¸Ð¾Ð½Ð½ÑÐµ ÑÐ°ÐºÑÐ¾ÑÑ
ÑÐ°Ð¹ÑÑ ÑÐ²ÑÐ·ÑÐ²Ð°Ð½Ð¸Ñ
Ð¾ÑÐµÐ½ÐºÐ° Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð²
Ð¼Ð¾Ð´ÐµÐ»Ñ Ð³ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ð¸
position weight matrices
transcription factors
binding sites
parameter estimation
segmentation gene expression model

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description: The aim of the work is a comparative study of various approximations of the binding energy of transcription factors with DNA and testing them in the context of gene regulation model. Binding energy landscapes in the Drosophila genome predicted by two binding energy calculation methods for several transcription factors involved in Drosophila morphogenesis were constructed and studied. It is shown that the methods give significantly different estimates for these landscapes, which allowed us to suggest that this difference may manifest itself at the level of expression of target genes regulated by the considered transcription factors. To test this hypothesis, binding energy estimation methods were implemented in the giant gene transcriptional regulation model. Numerical experiments were performed to optimize the free parameters in two versions of the model based on the expression data. It is shown that the newer method for estimating binding energy (SMiLE method) is associated with a lower average error in the model than the old method. The considered versions of the model predict different distributions of regulatory weights of transcription factor binding sites that characterize the strength of the siteâs influence on gene expression. Also, the use of both energy estimation methods in the expression model leads to a weak correlation between binding strength and the regulatory weight of the site. The results obtained allow us to conclude that the new method of energy approximation is promising for use in models of gene regulation.