tailieunhanh - Quantitative aspects of ruminant digestion and metabolism - Phần 16

Empirical models are fitted to experimental data to describe relationships between dependent and independent variables. By definition, they are best representations of the input:output data from which they were created. Also by definition | 21 Lactation Statistical and Genetic Aspects of Simulating Lactation Data from Individual Cows using a Dynamic Mechanistic Model of Dairy Cow Metabolism . Johnson . Famula and . Baldwin Department of Animal Science University of California Davis CA 95616-8521 USA Introduction Empirical models are fitted to experimental data to describe relationships between dependent and independent variables. By definition they are best representations of the input output data from which they were created. Also by definition testing predictions of empirical models against data not used in formulating the models often leads to failures. Thus it is generally recognized that empirical models are only valid for specific situations and do not generalize because they do not capture underlying reasons for relationships between dependent and independent variables. In contrast with empirical models mechanistic models are derived from theories about the nature of the system modelled and as a result are based upon our understanding of underlying mechanisms which drive the system France and Thornley 1984 Baldwin 1995 . Also parameter values in mechanistic equations are derived from experimental data on each mechanism and thus are not derived from statistical analyses of input output observations on the total system. For example a mechanistic model of dairy cow functions would incorporate data on nutrient uptake nutrient utilization by tissues metabolic pathways enzyme activities nutrient concentrations regulatory systems etc. while an empirical model would use data on intake of nutrients and amount of milk or milk components output and body weight changes. Failure of a mechanistic model to simulate new long-term data shows where understanding incorporated into the model is lacking and what old or new knowledge and experimental data must be incorporated to further refine and develop the model. The testing of mechanistic models in biology must consider two perspectives when evaluating