Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells

Malgorzata J. Wieteska; John A. Hession; Katarzyna K. Piotrowska-Tomala; Agnieszka Jonczyk; Pawel Kordowitzki; Karolina Lukasik; Dariusz J. Skarzynski; Leo Creedon

doi:10.1007/978-3-030-41120-6_12

Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells

Malgorzata J. Wieteska
, John A. Hession
, Katarzyna K. Piotrowska-Tomala
, Agnieszka Jonczyk
, Pawel Kordowitzki
, Karolina Lukasik
, Dariusz J. Skarzynski
, Leo Creedon

Atlantic Technological University
Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Gene Regulatory Matrices (GRMs) are a well known technique for modelling the interactions between genes. This technique is used here, but with genes and hormones, to create Gene and Hormone Regulatory Matrices (GHRMs). In addition, a network (a directed weighted graph) is constructed from the underlying interactions of several mRNA encoding enzymes and receptors and two hormones: estradiol (E2) and progesterone (P4). This also permits comparison of the impact of each given environmental condition on E2 and P4 production, as well as mRNA expression levels. Apart from differential equations techniques (which require knowledge of rates of decay of a given hormone and mRNA) there is no existing technique to accurately predict the concentration of hormones based on the concentration of mRNA. This novel approach using GHRMs permits the use of nodes to accurately model the concentrations of the remaining ones. Experiments were performed to collect data on the gene expression and hormone concentration levels for primary bovine granulosa cells under different treatments. This data was used to build the GHRM models.

Original language	English
Title of host publication	SEMA SIMAI Springer Series
Publisher	Springer
Pages	223-243
Number of pages	21
DOIs	https://doi.org/10.1007/978-3-030-41120-6_12
Publication status	Published - 2020

Publication series

Name	SEMA SIMAI Springer Series
Volume	21
ISSN (Print)	2199-3041
ISSN (Electronic)	2199-305X

Keywords

Bovine
Gene expression
Hormone
Regulatory matrices

Access to Document

10.1007/978-3-030-41120-6_12

Cite this

Wieteska, M. J., Hession, J. A., Piotrowska-Tomala, K. K., Jonczyk, A., Kordowitzki, P., Lukasik, K., Skarzynski, D. J., & Creedon, L. (2020). Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells. In SEMA SIMAI Springer Series (pp. 223-243). (SEMA SIMAI Springer Series; Vol. 21). Springer. https://doi.org/10.1007/978-3-030-41120-6_12

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title = "Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells",

abstract = "Gene Regulatory Matrices (GRMs) are a well known technique for modelling the interactions between genes. This technique is used here, but with genes and hormones, to create Gene and Hormone Regulatory Matrices (GHRMs). In addition, a network (a directed weighted graph) is constructed from the underlying interactions of several mRNA encoding enzymes and receptors and two hormones: estradiol (E2) and progesterone (P4). This also permits comparison of the impact of each given environmental condition on E2 and P4 production, as well as mRNA expression levels. Apart from differential equations techniques (which require knowledge of rates of decay of a given hormone and mRNA) there is no existing technique to accurately predict the concentration of hormones based on the concentration of mRNA. This novel approach using GHRMs permits the use of nodes to accurately model the concentrations of the remaining ones. Experiments were performed to collect data on the gene expression and hormone concentration levels for primary bovine granulosa cells under different treatments. This data was used to build the GHRM models.",

keywords = "Bovine, Gene expression, Hormone, Regulatory matrices",

author = "Wieteska, \{Malgorzata J.\} and Hession, \{John A.\} and Piotrowska-Tomala, \{Katarzyna K.\} and Agnieszka Jonczyk and Pawel Kordowitzki and Karolina Lukasik and Skarzynski, \{Dariusz J.\} and Leo Creedon",

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year = "2020",

doi = "10.1007/978-3-030-41120-6\_12",

language = "English",

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Wieteska, MJ, Hession, JA, Piotrowska-Tomala, KK, Jonczyk, A, Kordowitzki, P, Lukasik, K, Skarzynski, DJ & Creedon, L 2020, Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells. in SEMA SIMAI Springer Series. SEMA SIMAI Springer Series, vol. 21, Springer, pp. 223-243. https://doi.org/10.1007/978-3-030-41120-6_12

Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells. / Wieteska, Malgorzata J.; Hession, John A.; Piotrowska-Tomala, Katarzyna K. et al.
SEMA SIMAI Springer Series. Springer, 2020. p. 223-243 (SEMA SIMAI Springer Series; Vol. 21).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells

AU - Wieteska, Malgorzata J.

AU - Hession, John A.

AU - Piotrowska-Tomala, Katarzyna K.

AU - Jonczyk, Agnieszka

AU - Kordowitzki, Pawel

AU - Lukasik, Karolina

AU - Skarzynski, Dariusz J.

AU - Creedon, Leo

PY - 2020

Y1 - 2020

N2 - Gene Regulatory Matrices (GRMs) are a well known technique for modelling the interactions between genes. This technique is used here, but with genes and hormones, to create Gene and Hormone Regulatory Matrices (GHRMs). In addition, a network (a directed weighted graph) is constructed from the underlying interactions of several mRNA encoding enzymes and receptors and two hormones: estradiol (E2) and progesterone (P4). This also permits comparison of the impact of each given environmental condition on E2 and P4 production, as well as mRNA expression levels. Apart from differential equations techniques (which require knowledge of rates of decay of a given hormone and mRNA) there is no existing technique to accurately predict the concentration of hormones based on the concentration of mRNA. This novel approach using GHRMs permits the use of nodes to accurately model the concentrations of the remaining ones. Experiments were performed to collect data on the gene expression and hormone concentration levels for primary bovine granulosa cells under different treatments. This data was used to build the GHRM models.

AB - Gene Regulatory Matrices (GRMs) are a well known technique for modelling the interactions between genes. This technique is used here, but with genes and hormones, to create Gene and Hormone Regulatory Matrices (GHRMs). In addition, a network (a directed weighted graph) is constructed from the underlying interactions of several mRNA encoding enzymes and receptors and two hormones: estradiol (E2) and progesterone (P4). This also permits comparison of the impact of each given environmental condition on E2 and P4 production, as well as mRNA expression levels. Apart from differential equations techniques (which require knowledge of rates of decay of a given hormone and mRNA) there is no existing technique to accurately predict the concentration of hormones based on the concentration of mRNA. This novel approach using GHRMs permits the use of nodes to accurately model the concentrations of the remaining ones. Experiments were performed to collect data on the gene expression and hormone concentration levels for primary bovine granulosa cells under different treatments. This data was used to build the GHRM models.

KW - Bovine

KW - Gene expression

KW - Hormone

KW - Regulatory matrices

UR - https://www.scopus.com/pages/publications/85085180327

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AN - SCOPUS:85085180327

T3 - SEMA SIMAI Springer Series

SP - 223

EP - 243

BT - SEMA SIMAI Springer Series

PB - Springer

ER -

Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells

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Keywords

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