Iterative development, evaluation and optimal parameter estimation of a dynamic simulation model: a case study.

Barioni, L. G.; Oltjen, J. W.; Sainz, Roberto Daniel; Kebreab, E.; Dijkstra, Jelke; Bannink, A.; Gerrits, W.J.J.

doi:10.1079/9781845930059.0251

Cited by 5 publications

(4 citation statements)

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“…In the Davis growth model, a single DNA pool represents the number of cells (nuclei) in the body, and this in turn determines the rate of protein synthesis. The Davis growth model has been tested extensively (Barioni et al, 2006;Garcia et al, 2007;McPhee et al, 2007a,b), is capable of simulating cattle growth and body composition under a wide range of conditions, and may be subsequently extended by adding explicit representations of hyperplasia and hypertrophy of adipocytes in 4 depots: visceral, intermuscular, subcutaneous, and intramuscular. However, additional research is needed to improve our quantitative understanding of the intrinsic and extrinsic factors that determine adipocyte hyperplasia and hypertrophy in different adipose depots.…”

Section: Adipogenesismentioning

confidence: 99%

BOARD-INVITED REVIEW: The biology and regulation of preadipocytes and adipocytes in meat animals1,2

Hausman

Dodson

Ajuwon

et al. 2009

Journal of Animal Science

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The quality and value of the carcass in domestic meat animals are reflected in its protein and fat content. Preadipocytes and adipocytes are important in establishing the overall fatness of a carcass, as well as being the main contributors to the marbling component needed for consumer preference of meat products. Although some fat accumulation is essential, any excess fat that is deposited into adipose depots other than the marbling fraction is energetically unfavorable and reduces efficiency of production. Hence, this review is focused on current knowledge about the biology and regulation of the important cells of adipose tissue: preadipocytes and adipocytes.

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Section: Adipogenesismentioning

confidence: 99%

BOARD-INVITED REVIEW: The biology and regulation of preadipocytes and adipocytes in meat animals1,2

Hausman

Dodson

Ajuwon

et al. 2009

Journal of Animal Science

Self Cite

291

309

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“…The model provides the structure for predicting composition of growing cattle as well, but not all its parameters have been estimated and evaluated. Barioni et al (2006) added the variable maintenance representation from the sheep model to the DGM for beef cattle. Fitting beef cattle growth data, variable instead of fixed maintenance requirements for each experimental group significantly improved the precision of the model for f and RE, confirming the conclusions of Sainz et al (1995) that previous nutrition had substantial effects on maintenance energy expenditures and indicates that variable maintenance can significantly improve model predictions.…”

Section: Representation Of Biology-modelsmentioning

confidence: 99%

ASAS CENTENNIAL PAPER: Net energy systems for beef cattle--Concepts, application, and future models

Ferrell¹,

Oltjen²

2008

Journal of Animal Science

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Development of nutritional energetics can be traced to the 1400s. Lavoisier established relationships among O(2) use, CO(2) production and heat production in the late 1700s, and the laws of thermodynamics and law of Hess were discovered during the 1840s. Those discoveries established the fundamental bases for nutritional energetics and enabled the fundamental entity ME = retained energy + heat energy to be established. Objectives became: 1) to establish relationships between gas exchange and heat energy, 2) to devise bases for evaluation of foods that could be related to energy expenditures, and 3) to establish causes of energy expenditures. From these endeavors, the basic concepts of energy partitioning by animals were developed, ultimately resulting in the development of feeding systems based on NE concepts. The California Net Energy System, developed for finishing beef cattle, was the first to be based on retained energy as determined by comparative slaughter and the first to use 2 NE values (NE(m) and NE(g)) to describe feed and animal requirements. The system has been broadened conceptually to encompass life cycle energy requirements of beef cattle and modified by the inclusion of numerous adjustments to address factors known to affect energy requirements and value of feed to meet those needs. The current NE system remains useful but is empirical and static in nature and thus fails to capture the dynamics of energy utilization by diverse animals as they respond to changing environmental conditions. Consequently, efforts were initiated to develop dynamic simulation models that captured the underlying biology and thus were sensitive to variable genetic and environmental conditions. Development of a series of models has been described to show examples of the conceptual evolution of dynamic, mechanistic models and their applications. Generally with each new system, advances in prediction accuracy came about by adding new terms to conceptually validated models. However, complexity of input requirements often limits general use of these larger models. Expert systems may be utilized to provide many of the additional inputs needed for application of the more complex models. Additional information available from these systems is expected to result in an ever-increasing range of application. These systems are expected to have increased generality and the capability to be integrated with other models to allow economic evaluation. This will eventually allow users to compute solutions that allow development of optimal production strategies.

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“…MecSic (Hoch and Agabriel 2004) predicts carcass and non-carcass protein and lipids from synthesis and degradation of each based on physiological stage and MEI. Each model was fitted to three datasets with different genotypes and growth trajectories (Salers heifers, continuous or discontinuous growth from 8 to 30 mo; Charolais bulls, continuous growth from 9 to 18 mo old; and Angus-Hereford steers, continuous or discontinuous growth from 8 to 16 mo) using a non-linear optimization procedure (Barioni et al 2004). Mean bias (MB), the residuals and the mean square error of prediction (MSEP) (Tedeschi 2004) were compared between models and datasets.…”

Section: Summaries Of Communications 549mentioning

confidence: 99%

Proceedings of the 2005 Meeting of the Animal Science Modelling Group

2005

Can. J. Anim. Sci.

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This group meets yearly for one-day meetings. The 2005 meeting, sponsored by Church & Dwight Co., Inc., Princeton, NJ, was held on July 23 at the Cincinnati Zoo and Botanical Garden in Ohio, prior to the ASAS-ADSA-CSAS Joint Annual Meeting. Summaries of the papers presented follow. Each summary has been peer reviewed and edited for clarity. The person who presented the paper is identified with an e-mail address. Definitions of feed analysis methods as sources of error in nutritional models. M.

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Iterative development, evaluation and optimal parameter estimation of a dynamic simulation model: a case study.

Cited by 5 publications

References 0 publications

BOARD-INVITED REVIEW: The biology and regulation of preadipocytes and adipocytes in meat animals1,2

BOARD-INVITED REVIEW: The biology and regulation of preadipocytes and adipocytes in meat animals1,2

ASAS CENTENNIAL PAPER: Net energy systems for beef cattle--Concepts, application, and future models

Proceedings of the 2005 Meeting of the Animal Science Modelling Group

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