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.
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References
de Sousa Abreu, R., Penalva, L.O., Marcotte, E.M., Vogel, C.: Global signatures of protein and mRNA expression levels. Molecular BioSystems 5(12), 1512–1526 (2009)
Defterli, O., Fügenschuh, A., Weber, G.W.: New discretization and optimization techniques with results in the dynamics of gene-environment networks. Proc. PCO 2–4 (2010)
Defterli, O., Fügenschuh, A., Weber, G.W.: Modern tools for the time-discrete dynamics and optimization of gene-environment networks. Commun. Nonlinear Sci. Numer. Simul. 16(12), 4768–4779 (2011)
Echternkamp, S.E., Spicer, L.J., Gregory, K.E., Canning, S.F., Hammond, J.M.: Concentrations of insulin-like growth factor-I in blood and ovarian follicular fluid of cattle selected for twins. Biol. Reprod. 43(1), 8–14 (1990)
Gebert, J., Lätsch, M., Pickl, S.W., Radde, N., Weber, G.-W., Wünschiers, R.: Genetic networks and anticipation of gene expression patterns. AIP Conf. Proc. 718(1), 474–485 (2004)
Gutierrez, C.G., Campbell, B.K., Webb, R.: Development of a long-term bovine granulosa cell culture system: induction and maintenance of estradiol production, response to follicle-stimulating hormone, and morphological characteristics. Biol. Reprod. 56(3), 608–616 (1997)
Harper, C.V., Finkenstädt, B., Woodcock, D.J., Friedrichsen, S., Semprini, S., Ashall, L., Spiller, D.G., Mullins, J.J., Rand, D.A., Davis, J.R., White, M.R.: Dynamic analysis of stochastic transcription cycles. PLoS Biology 9(4), e1000607 (2011)
Hojo, T., Siemieniuch, M.J., Lukasik, K., Piotrowska-Tomala, K.K., Jonczyk, A.W., Okuda, K., Skarzynski, D.J.: Programmed necrosis - a new mechanism of steroidogenic luteal cell death and elimination during luteolysis in cows. Scientific Reports 6, 38211 (2016)
Mani, A.M., Fenwick, M.A., Cheng, Z., Sharma, M.K., Singh, D., Wathes, D.C.: IGF1 induces up-regulation of steroidogenic and apoptotic regulatory genes via activation of phosphatidylinositol-dependent kinase/AKT in bovine granulosa cells. Reproduction 139(1), 139–151 (2010)
Maier, T., Güell, M., Serrano, L.: Correlation of mRNA and protein in complex biological samples. FEBS Letters 583(24), 3966–3973 (2009)
Miller, W.L., Auchus, R.J.: The molecular biology, biochemistry, and physiology of human steroidogenesis and its disorders. Endocrine Reviews 32(1), 81–151 (2011)
Monga, R., Sharma, I., Datta, T.K., Singh, D.: Characterization of serum-free buffalo granulosa cell culture and analysis of genes involved in terminal differentiation from FSH-to LH-responsive phenotype. Domest. Anim. Endocrinol. 41(4), 195–206 (2011)
Moreno, L.A., Defterli, O., Füenschuh, A., Weber, G.W.: Vester’s sensitivity model for genetic networks with time-discrete dynamics. In: International Conference on Algorithms for Computational Biology, pp. 35–46. Springer, Cham (2014)
Nguyen, P.: Mathematical modelling of steroid synthesis in steroid-producing tissues and steroid partitioning in circulation. Doctoral dissertation, ResearchSpace@ Auckland (2012)
Verma, C.K., Srivastava, N.: Study of gene regulatory network using polynomial, rational polynomial and circular function. Int. J. Adv. Sci. Technol. 42, 19–32 (2012)
Acknowledgements
Study was supported by the Grant OPUS of National Sciences Center No UMO-2018∕29∕B∕NZ9∕00391. The authors wish to acknowledge funding from IT Sligo’s President’s Bursary Award and from IT Sligo’s Research Capacity Building Fund.
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Wieteska, M.J. et al. (2020). Linear Dynamics of mRNA Expression and Hormone Concentration Levels in Primary Cultures of Bovine Granulosa Cells. In: Aguiar, M., Braumann, C., Kooi, B., Pugliese, A., Stollenwerk, N., Venturino, E. (eds) Current Trends in Dynamical Systems in Biology and Natural Sciences. SEMA SIMAI Springer Series, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-41120-6_12
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