Biomedical subjects
Publications and source records attributed to J R Reichl.
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Explore the source record for details and available documents.
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A linear programming model provides for analysis of general input-output relationships in the rumen, for evaluation of competitive relationships among rumen microbes, and for computation of optimal relationships in the rumen. Eight rumen microbial groups defined on the bases of substrate specificity, nutrient requirements for growth, fermentation products, and relative metabolic activities comprise the central core of the model. Relative metabolic rates of microbial groups calculated from their cell sized were used as coefficients in the objective function. The model was used to evaluate effects of different amounts of protein from feed and various carbohydrates upon microbial population and fermentation patterns as accommodated by current concepts. During the several solutions of the model, considerable simplification of the rumen microflora occurred. This implies that current data and concepts, and the hypothesis regarding relative metabolic rate, as represented in the model, do not accommodate adequately competitions among the several rumen microbial species and, thus, that additional data and concepts regarding rumen microbial interactions are required. Also evaluated were effects of ingestion of bacteria by protozoa upon over-all rumen function, absolute microbial cell yields, cell yields per mole of adenosine triphosphate, and factors affecting these.
Two models of rumen fermentative relationships expressed as systems of simultaneous linear equations and based on requirements for maintenance of balances of elementary imput and output and metabolic pathways are presented in matrix format consistent with solution by linear programs. Matrix entries defining the two models were verified carefully based upon a survey of the literature, and conceptual bases of the models were validated by comparisons of model outputs with experimental data not used in model construction. The models then were used to evaluate interactions among feed composition, volatile fatty acid yields and patterns, microbial growth yields and efficiencies, and microbial metabolic pathways.