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Modelling nutrient utilization in farm animals.

Book cover for Modelling nutrient utilization in farm animals.

Description

This book presents edited and revised versions of papers presented at the Fifth International Workshop on Modelling Nutrient Utilization in Farm Animals, held at the University of Cape Town, Cape Town, South Africa, 25-28 October 1999. There are 31 chapters and 6 sections entitled ruminal metabolism, absorption and metabolism, growth and development, ruminant production in various situations, nutr...

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Chapter 23 (Page no: 303)

A rodent model of protein turnover to determine protein synthesis, amino acid channelling and recycling rates in tissues.

Amino acids for protein synthesis can arise from extracellular (Ec), plasma (Pls) or intracellular (Ic) sources or protein degradation through recycling. Experimentally, the amino acid sources sampled may not represent these specific pools and therefore not represent true sources of amino acids used for aminoacyl tRNA (Tr) formation. As a result, estimates of protein fractional synthesis rates (FSR) may not reflect true protein synthesis rates. Three models of brain, liver and muscle were created to represent different sampling methods used for partitioning free amino acid precursor pools. The Ec+Pls model separates the intracellular and aminoacyl tRNA pools of free leu from a combined sample of plasma and extracellular pools. The Ec+Ic+Tr model separates plasma amino acids from a combined extracellular, intracellular and aminoacyl tRNA pool. The Ec+Ic model separates plasma and aminoacyl tRNA from a combined extracellular and intracellular free amino acid pool. Data from a pulse dose of 14C-U-leu was used to estimate FSR and determine the source of amino acids for aminoacyl tRNA (protein synthesis). The pulse dose-specific radioactivity data were fitted to dynamic, theoretical models representing protein turnover in brain, liver and muscle for a non-growing 20 g mouse. Estimates of recycling were higher with the Ec+Pls than the Ec+Ic model, with liver at 90.4% and 76.2%, muscle at 91.1% and 86.1% and brain at 95.2 and 68.3%, respectively. Protein synthesis rates estimated using the Ec+Ic and Ec+Pls models were 81.9 and 117% day/1 in liver, 14.8 and 16.2% day-1 in brain and 14.7 and 14.0% day-1 in muscle, respectively. The Ec+Ic+Tr model underestimated protein synthesis in all tissues, because the specific radioactivity of the combined precursor pool (Ec+Ic+Tr) was very high relative to the specific radioactivity of the protein pool. At high rates of recycling, intracellular and extracellular specific radioactivities no longer approximate the true precursor specific radioactivity (aminoacyl tRNA). Therefore current methods of estimating FSR in tissues incorporate error associated with incorrectly estimating precursor pool specific radioactivity.

Other chapters from this book

Chapter: 1 (Page no: 11) The role of thermodynamics in controlling rumen metabolism. Author(s): Kohn, R. A. Boston, R. C.
Chapter: 2 (Page no: 25) Modelling lipid metabolism in the rumen. Author(s): Dijkstra, J. Gerrits, W. J. J. Bannink, A. France, J.
Chapter: 3 (Page no: 37) Towards a more accurate representation of fermentation in mathematical models of the rumen. Author(s): Nagorcka, B. N. Gordon, G. L. R. Dynes, R. A.
Chapter: 4 (Page no: 49) Simple allometric models to predict rumen feed passage rate in domestic ruminants. Author(s): Cannas, A. Soest, P. J. van
Chapter: 5 (Page no: 63) Ruminal metabolism of buffersoluble proteins, peptides and amino acids in vitro. Author(s): Udén, P.
Chapter: 6 (Page no: 73) Models to interpret degradation profiles obtained from in vitro and in situ incubation of ruminant feeds. Author(s): López, S. France, J. Dijkstra, J. Dhanoa, M. S.
Chapter: 7 (Page no: 87) Modelling production and portal appearance of volatile fatty acids in dairy cows. Author(s): Bannink, A. Kogut, J. Dijkstra, J. France, J. Tamminga, S. Vuuren, A. M. van
Chapter: 8 (Page no: 103) Modelling energy expenditure in pigs. Author(s): Milgen, J. van Noblet, J.
Chapter: 9 (Page no: 115) Aspects of modelling kidney dynamics. Author(s): Robson, B. Vlieg, M.
Chapter: 10 (Page no: 127) Evaluation of a representation of the limiting amino acid theory for milk protein synthesis. Author(s): Hanigan, M. D. France, J. Crompton, L. A. Bequette, B. J.
Chapter: 11 (Page no: 145) Multiple-entry urea kinetic model: effects of incomplete data collection. Author(s): Zuur, G. Russell, K. Lobley, G. E.
Chapter: 12 (Page no: 163) Evaluation of a growth model of preruminant calves and modifications to simulate shortterm responses to changes in protein intake. Author(s): Gerrits, W. J. J. Togt, P. L. van der Dijkstra, J. France, J.
Chapter: 13 (Page no: 175) Simulation of the development of adipose tissue in beef cattle. Author(s): Sainz, R. D. Hasting, E.
Chapter: 14 (Page no: 183) A simple nutrient-based production model for the growing pig. Author(s): Boisen, S.
Chapter: 15 (Page no: 197) Second-generation dynamic cattle growth and composition models. Author(s): Oltjen, J. W. Pleasants, A. B. Soboleva, T. K. Oddy, V. H.
Chapter: 16 (Page no: 211) Modelling interactions between cow milk yield and growth of its suckling calf. Author(s): Blanc, F. Agabriel, J. Sabatier, P.
Chapter: 17 (Page no: 227) A mechanistic dynamic model of beef cattle growth. Author(s): Hoch, T. Agabriel, J.
Chapter: 18 (Page no: 241) Modelling nutrient utilization in growing cattle subjected to short or long periods of moderate to severe undernutrition. Author(s): Witten, G. Q. Richardson, F. D.
Chapter: 19 (Page no: 253) An integrated cattle and crop production model to develop whole-farm nutrient management plans. Author(s): Tylutki, T. P. Fox, D. G.
Chapter: 20 (Page no: 263) Modelling nutrient utilization by livestock grazing semiarid rangeland. Author(s): Richardson, F. D. Hahn, B. D. Schoeman, S. J.
Chapter: 21 (Page no: 281) Using the cornell net carbohydrate and protein system model to evaluate the effects of variation in maize silage quality on a dairy farm. Author(s): Tylutki, T. P. Fox, D. G. McMahon, M. McMahon, P.
Chapter: 22 (Page no: 289) Challenge and improvement of a model of post-absorptive metabolism in dairy cattle. Author(s): McNamara, J. P. Phillips, G. J.
Chapter: 24 (Page no: 317) Modelling relationships between homoeorhetic and homoeostatic control of metabolism: application to growing pigs. Author(s): Sauvant, D. Lovatto, P. A.
Chapter: 25 (Page no: 329) Model for the interpretation of energy metabolism in farm animals. Author(s): Chudy, A.
Chapter: 26 (Page no: 347) Linear models of nitrogen utilization in dairy cows. Author(s): Kebreab, E. Allison, R. Mansbridge, R. Beever, D. E. France, J.
Chapter: 27 (Page no: 353) Isotope dilution models for partitioning amino acid uptake by the liver, mammary gland and hindlimb tissues of ruminants. Author(s): Crompton, L. A. France, J. Bequette, B. J. Maas, J. A. Hanigan, M. D. Lomax, M. A. Dijkstra, J.
Chapter: 28 (Page no: 361) The conversion of a scientific model describing dairy cow nutrition and production to an industry tool: the CPM dairy project. Author(s): Boston, R. C. Fox, D. G. Sniffen, C. Janczewski, E. Munson, R. Chalupa, W.
Chapter: 29 (Page no: 379) The utilization of prediction models to optimize farm animal production systems: the case of a growing pig model. Author(s): Bailleul, P. J. dit Bernier, J. F. Milgen, J. van Sauvant, D. Pomar, C.
Chapter: 30 (Page no: 393) A pig model for feed evaluation. Author(s): Danfær, A.

Chapter details

  • Author Affiliation
  • Department of Animal Science, University of California, Davis, CA 95616, USA.
  • Year of Publication
  • 2000
  • ISBN
  • 9780851994499
  • Record Number
  • 20083014705