title: ÐÐ¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ðµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð¸ÑÑÐµÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½ÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ Ð³ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿Ð¾ Ð´Ð°Ð½Ð½ÑÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ
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: Restoration of a system of differential equations for a gene network according to the data by genetic programming method
Creators: name: Ð§ÐµÐºÐ¼Ð°ÑÐµÐ², ÐÐ¸ÑÐ°Ð¸Ð»
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: 2024
Subjects: ÑÐ¸Ð¼Ð²Ð¾Ð»ÑÐ½Ð°Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ñ
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð°Ð½ÑÐ°Ð¼Ð±Ð»Ð¸ Ð´ÐµÑÐµÐ²ÑÐµÐ²
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð³ÑÐ°Ð¼Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐ²Ð¾Ð»ÑÑÐ¸Ñ
python
symbolic regression
regression models
trees ensembles
genetic programming
grammatical evolution

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description: The subject of the graduate qualification work is Â«Reconstruction of the system of ordinary differential equations for the gene network from data using the genetic programming methodÂ». This work is dedicated to the task of reconstruction of the system of diffferential equations, describing a mutual regulation in a gene network, using quantitative data of the gene expression levels. The research set the following goals: 1. Development and testing of the genetic evolutionary algorythm for the reconstruction of systems of ordinary differential equations from the observations. 2. Application of the developed method of the differential equations systems reconstruction in a model problem and analysis of the obtained results. In this work, the gene expression programming method was used and a Python application was created for the calculation and the visualisation. Developed algorythm was tested on the model systems of differential equations, including the model of a gene regulatory network of a protein production. Obtained results was analysed using statistical tests. This work resulted in a development of the method, allowing for reconstruction of a system of differential equations for gene networks, using experimental data of the gene expression levels. This method could help in a study of mutual regulation in a gene regulatiory networks.