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Combining in vitro data and modelling to predict in vivo animal response

Published online by Cambridge University Press:  27 February 2018

Jan Dijkstra  and
James France
Jan Dijkstra
Affiliation:
Wageningen Institute of Animal Sciences, Animal Nutrition Group, PO Box 338, 6700 AH Wageningen, the Netherlands
James France
Affiliation:
Wageningen Institute of Animal Sciences, Animal Nutrition Group, PO Box 338, 6700 AH Wageningen, the Netherlands
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Summary

Existing feed evaluation systems for ruminants assess the feed value in a rather empirical way, with a limited ability to integrate metabolism in a meaningful framework. For the quantitative description of the mechanisms, appropriate biological data can be obtained using in vitro methods. The aim of this paper is to examine the use of modelling and in vitro data to predict digestion processes in vivo. Suitable mathematical methods are required to describe and interpret substrate disappearance profiles or gas production profiles. The derivation of such models is important since this allows a clear definition of the underlying assumptions made. Such assumptions are related to the change in fractional rate of degradation (kd) during incubation that will determine the shape of the profile. Furthermore, the value of the fractional passage rate (kp) is of crucial importance in the prediction of extent of degradation in the rumen. The development and application of models, based on classic microbial growth equations, clearly shows that observed variation in microbial efficiency in batch cultures (including the gas production technique) is not necessarily related to that in vivo. Rather, kp is again a major factor contributing to explanation of variation in microbial efficiency. Similarly, the end products of fermentation (VFA) and the VFA molar proportions can be estimated in vitro, but its direct applicability to the in vivo situation is limited. It is concluded that some potential uses of in vitro techniques are ultimately misleading. Mechanistic models indicate that mechanisms governing microbial efficiency and VFA molar proportions in vitro are not necessarily valid for the in vivo situation. Therefore, the in vitro data cannot be used directly for a uniform system of feed evaluation to predict animal responses. Rather, the in vitro data obtained for substrate degradation may be used in whole rumen models as a basal input value to indicate the degradation potential.

Resumen

Resumen

Los sistemas de evaluación de alimentos para ruminates existentes estiman su valor de una forma muy empírica y tiene una limitada habilidad para integrar el metabolismo animal en su estructura. Para la descripción cuantitativa de los mecanismos, datos biológicos apropiados pueden ser obtenidos empleando métodos in vitro. El objetivo del presente trabajo es presentar el uso del modelaje e información proveniente de estudios in vitro para predecir los procesos de digestión in vivo. Métodos matemáticos adecuados son necesarios para describir e interpretar los perfiles de desaparición del sustrato o de la producción de gas. La construcción de dicho modelo es importante dado que permite una clara definición de las asunciones hechas para su diseño. Dichas asunciones se encuentran relacionadas al cambio en la tasa fraccional de degradación (Kd) durante la incubación y determinará la forma del perfil. Más aún, el valor de la tasa fraccional de pasaje (Kp) es de importancia crucial en la predicción de la degradación potencial en el rumen. El desarrollo y aplicación de modelos, basados en las ecuaciones clásicas de crecimiento microbial, muestran claramente que las variaciones observadas en la eficiencia microbial en cultivos (incluyendo la técnica de producción de gas) no se encuentra necesariamente relacionada la eficiencia in vivo. En cambio, kp es, de nuevo, un factor importante que contribuye a explicar la variación de la eficiencia microbial. De manera similar, los productos finales de la fermentación (AGV’s) y la proporción molar de estos puede ser estimada in vitro, pero su aplicación directa a situaciones in vivo es limitada. Se concluye que algunos usos potenciales de las técnicas in vitro pueden conducir a interpretaciones erróneas. Modelos mecanísticos indican que los mecanismos que gobiernan la eficiencia microbial y la proporción molar de AGV’s, no son necesariamente los mismos para la situación in vivo. Entonces, los datos originados de estudios in vitro no pueden ser empleados directamente para un sistema de evaluación de alimentos para la predicción del comportamiento animal. En cambio, datos obtenidos in vitro de la degradación de sustrato pueden ser emplados en modelos del rumen complete como información de entrada para señalar/estimar el potencial de degradación.

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Information
BSAP Occasional Publication , Volume 34: Herbivores: Assessment of intake, digestibility and the roles of secondary compounds , 2006 , pp. 135 - 144
Copyright
Copyright © British Society of Animal Science 2006

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References

Baldwin, R.L. (1995). Modeling Ruminant Digestion and Metabolism. Chapman and Hall, New York, USAGoogle Scholar
Bannink, A., Kogut, J., Dijkstra, J., France, J., Tamminga, S. and Van Vuuren, A.M. (2000). Modelling production and portal appearance of volatile fatty acids in dairy cows. In: Modelling Nutrient Utilization in Farm Animals. Edited by McNamara, J.P., France, J. and Beever, D.E., CAB International, Wallingford, UK, pp. 87–102 CrossRefGoogle Scholar
Blummel, M., Schroder, A., Sudekum, K.H. and Becker, K. (1999). Estimating ruminal microbial efficiencies in silage-fed cattle: comparison of an in vitro method with a combination of in situ and in vivo measurements. Journal of Animal Physiology and Animal Nutrition 81: 57–67 CrossRefGoogle Scholar
Dijkstra, J. (1994). Production and absorption of volatile fatty acids in the rumen. Livestock Production Science 39: 61–69 CrossRefGoogle Scholar
Dijkstra, J., France, J. and Davies, D.R. (1998). Different mathematical approaches to estimating microbial protein supply in ruminants. Journal of Dairy Science 81: 3370–3384 Google Scholar
Dijkstra, J., France, J., Lopez, S. and Dhanoa, M.S. (1999). Impact of variation in yield of gas during incubation on simulated gas production and extent of ruminal degradation. In: Proceedings of the British Society of Animal Science, British Society of Animal Science, Edinburgh, UK. pp. 38 Google Scholar
Dijkstra, J., France, J., Dhanoa, M.S. and Lopez, S. (2000). Simulation of substrate degradation, microbial synthesis and gas production in the gas production technique and in vivo. In: Gas production: Fermentation kinetics for feed evaluation and to assess microbial activity, Wageningen University / British Society of Animal Science, Wageningen, The Netherlands. pp. 42–43 Google Scholar
Dijkstra, J., Mills, J.A.N. and France, J. (2002). The role of dynamic modelling in understanding the microbial contribution to rumen function. Nutrition Research Reviews 15: 67–90 Google Scholar
Dijkstra, J., France, J., Tamminga, S. and Mills, J.A.N. (2003). Predicting the yield of nutrients from microbial metabolism in the rumen. In: Matching Herbivore Nutrition to Ecosystems Biodiversity. Proceedings of the VIth International Symposium on the Nutrition of Herbivores, edited by 't Mannetje, L., Ramírez- Avilés, L., Sandoval-Castro, C. and Ku-Vera, J.C., Universidad Autónoma de Yucatán, Mérida, México, pp. 101–127 Google Scholar
France, J. and Thornley, J.H.M. (1984). Mathematical Models in Agriculture. Butterworths, London, UKGoogle Scholar
France, J., Dijkstra, J., Dhanoa, M.S., Lopez, S. and Bannink, A. (2000). Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations. British Journal of Nutrition 83: 143–150 CrossRefGoogle ScholarPubMed
Huhtanen, P. and Hristov, A.N. (2001). Estimating passage kinetics using fibre-bound 15N as an internal marker. Animal Feed Science and Technology 94: 29–41 CrossRefGoogle Scholar
Lopez, S., France, J., Dhanoa, M.S., Mould, F. and Dijkstra, J. (1999). Comparison of mathematical models to describe disappearance curves obtained using the polyester bag technique for incubating feeds in the rumen. Journal of Animal Science 77: 1875–1888 CrossRefGoogle ScholarPubMed
Pellikaan, W.F., Boer, H., Dijkstra, J., Hof, G., Schreurs, V.A.M.M., Tamminga, S. and Vos, J. (1999). Passage dynamics of [13C]- enriched feed particles in grass silage fed dairy cows. South African Journal of Animal Science 29: 274–275 Google Scholar
Pirt, S.J. (1975). Principles of Microbe and Cell Cultivation. Blackwell Scientific Publications, Oxford, UK.Google Scholar
Robinson, P.H., Tamminga, S. and Van Vuuren, A.M. (1986). Influence of declining level of feed intake and varying proportion of starch in the concentrate on rumen fermentation in dairy cows. Livestock Production Science 15: 173–189 CrossRefGoogle Scholar