Charting the evolution of rumen microbial models from past to present.

This review delves into the intricate realm of ruminal microbes, shedding light on their complexity beyond previously recognized dimensions. Existing publications have probed the essential facets of these microorganisms on a finer scale than previously acknowledged. Mathematical models have emerged to simulate the behavior of approximately 200 distinct bacterial species, 25 genera of ciliate protozoa, and five genera of anaerobic fungi, constituting about 10% of the total viable bacteria in the rumen. However, the expansive diversity and density of rumen microbes challenge the complete characterization of species or genera in relation to substrates or end products. Consequently, continuous revision and re-engineering of mathematical models become imperative. In the context of ruminants, prevalent models often categorize bacteria into fiber-fermenting and nonfiber-fermenting groups. An additional subgroup featuring hyper-ammoniaproducing bacteria has been proposed, but its explicit integration into nutrition models remains unexplored. Furthermore, incorporating a more mechanistic evaluation of protozoa and fungal growth may be warranted to improve nutrition models. The time horizon and intervals at which models are constructed present another critical consideration, with feed efficiency variations among ruminants believed to be closely tied to differences in the ruminal microbiota. Addressing immediate concerns, this review underscores the need for a comprehensive exploration of factors influencing microbial growth in the rumen. Several indices have been suggested and adopted to predict microbial efficiencies, but they may provide simplification for practical application. Thus, a mechanistic approach should be considered to remove dependencies between estimates and improve predictive accuracy and precision. While artificial intelligence holds promise in elucidating gaps within empirical and mechanistic models, the generation of accurate, vetted datasets is paramount. Furthermore, this review posits that factors such as recycled nitrogen, endotoxins, and microbial population dynamics (i.e., cross-feeding and competition) may be limiting our understanding of microbial growth, suggesting the necessity of modeling these intricacies concurrently to achieve a more nuanced comprehension. This multifaceted approach aims to propel the understanding of ruminal microbial dynamics into new frontiers, paving the way for more informed and effective modeling in the future. [ABSTRACT FROM AUTHOR]