Mathematical Modeling of Interaction of Hormones and Metabolites in the Negative Energy Balance Phase in Dairy Cows

Authors

1 Department of animal sciences, Faculty of agriculture, Yasouj University, Yasouj, Iran

2 Department of clinical studies, School of veterinary medicine, Shiraz University, Shiraz, Iran

3 Department of Chemical Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz 71345, Iran

4 Department of Electronics and Communication Engineering, Shiraz University, Shiraz, Iran

Abstract

During the last few decades, there has been an increase in the development of mathematical models, as both research tools and management tools that can integrate our current knowledge of reproductive events and then would be helpful in predicting the reproductive efficiency of farm animals. This paper presents a conceptualized mathematical modeling of hormonal and metabolites interactions over non-estrous and negative energy balance phase in dairy cow. Anestrus occurs annually and after each calving. The anestrus period in high milk-producing cows due to the negative balance of energy produced by high milk production in the first few days after parturition is a constant challenge in the modern world of dairy cow worldwide. For this reason, many scientific efforts have been made to explore the biological aspects of this phase. Due to the complexity of the existing relationships between effective parameters, descriptive-analytical biological analyses have not succeeded in elucidating this biological system. In this research, it was attempted to look at the elaborating factors playing in this phase, at same time, it was tired to pinpoint and explore this phase from biological system perspective.
Material and Methods: In the present study, first diverse information on the various aspects of the factors affecting on the postpartum negative balance of energy and reproduction performance of dairy cows were initially collected. Then, this information was converted to their sound mathematical equations. Efforts have been made to simplify the long and unprocessed equations from well-known factors including liver glucose, blood glucose, pancreatic insulin and blood insulin, IGF-1 liver and blood, and hypothalamic GnRH affecting on this biological phase to be simplified using ordinary differential equations. In other part of this study,
Findings: By using the data published in the scientific articles and expanding them by using Curve Expert software, the computer simulation process was performed by MATLAB software (Version 9.1) and the predictive models were developed for some system parameters.
Conclusion: The results showed that the system of ordinary differential equations was able to predict good blood glucose (AARD = 0.388) and insulin (AARD% = 0.638), but they were not able to provide accurate predictions for pancreatic insulin and blood IGF-1.
The present model was a starting point to reach a comprehensive model which should be completed gradually. In this model, it was tried to use empirical data from reliable sources in simulation model, a matter that was more or less successful in this research.

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References

1.Blanc, F., Martin, G.B. and Bocquier, F. 2001. Modeling reproduction in farm animals: a review. Reproduction, Fertility and Development. 13: 337-353.
2.Clark, L.H., Schlosser, P.M. and Selgrade, J.F. 2003. Multiple stable periodic solutions in a model for hormonal control of the menstrual cycle.Bulletin of Mathematical Biology. 65(1): 157-173.
3.Clément, F., Monniaux, D., Thalabard, J.C. and Claude, D. 2002. Contribution of a mathematical modeling approach to the understanding of the ovarian function. Comptes Rendus Biologies. 325(4): 473-485.
4.Dijkstra, J., Forbes, J.M. and France, J. 2005. Quantitative aspects of ruminant digestion and metabolism.2nd ed. CAB International, Wallingford, UK, 727p.
5.Ellis, J.L., Qiao, F. and Cant, J.P. 2006. Prediction of dry matter intake throughout lactation in a dynamic model of dairy cow performance. Journal of Dairy Science. 89: 1558-1570.
6.France, J. and Kebreab, E. 2008. Mathematical modeling in animal nutrition. CAB International. Wallingford, UK, 588p.
7.Fu, G., Wang, Z., Li, J. and Wu, R. 2011. A mathematical framework for functional mapping of complex phenotypes using delay differential equations. Journal of Theoretical Biology. 289: 206-216.
8.Garnsworthy, P.C., Sinclair, K.D. and Webb, R. 2008. Integration of physiological mechanisms that influence fertility in dairy cows. Animal. 2(8): 144-1152.
9.Heinze. K., Keener, R.W. and Midgley, J.R. 1998. A mathematical model of luteinizing hormone release from ovine pituitary cells in perfusion. Journal of Animal Physiology. 275: 61-71.
10.Ingalls, B. 2012. Mathematical modeling in systems biology: An Introduction.Applied Mathematics book, University of Waterloo. 396p.
11.Kitano, H. 2002. Computational systems biology. Nature. 420: 206-210.
12.Lazebnik, R.S., Weinberg, B.D., Breen, M.S., Lewin, J.S., and Wilson, D.L. 2002.Three-dimensional model of lesion geometry for evaluation of MR-guided thermal ablation therapy. Academic Radiology. 9(10): 1128-1138.
13.Namjo, M., Farhangfar, H., Bashteni, M. and Eghbal, A.R. 2016. Assessment of the impacts of different factors on the
occurrence of negative energy balance in Iranian dairy cows using a logistic generalised linear model. Journal of Ruminant Research. 4(3): 93-116
14.Radcliff, R.P., McCormack, B.L., Crooker, B.A. and Lucy, M.C. 2003. Plasma hormones and expression of growth hormone receptor and insulin-like growth factor-I mRNA in hepatic tissue of periparturient dairy cows. Journal of Dairy Science. 86: 3920-3926.
15.Reinecke, I. and Deuflhard, P. 2007. A complex mathematical model of the human menstrual cycle. Journal of Theoretical Biology. 247(2): 303-330.
16.Reynolds, C.K., Aikman, P.C., Lupoli, B., Humphries, D.J. and Beever, D.E. 2003. Splanchnic metabolism of dairy cows during the transition from late gestation through early lactation. Journal of Dairy Science.86: 1201-1217.
17.Vetharaniam, A.J., Peterson, K.P., McNatty, T.K. and Soboleva, M. 2010. Modelling female reproductive function in farmed animals. Animal Reproduction Science. 122: 164-173.
Journal of Ruminant Research
Volume 6, Issue 3
December 2018
Pages 27-44

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