Bayesian mechanistic modeling of thermodynamically controlled volatile fatty acid, hydrogen and methane production in the bovine rumen

Abstract

Dynamic modeling of mechanisms driving volatile fatty acid and hydrogen production in the rumen microbial ecosystem contributes to the heuristic prediction of CH4 emissions from dairy cattle into the environment. Existing mathematical rumen models, however, lack the representation of these mechanisms. A dynamic mechanistic model was developed that simulates the thermodynamic control of hydrogen partial pressure (pH2) on volatile fatty acid (VFA) fermentation pathways via the NAD+ to NADH ratio in fermentative microbes, and methanogenesis in the bovine rumen. This model is unique and closely aligns with principles of reaction kinetics and thermodynamics. Model state variables represent ruminal carbohydrate substrates, bacteria and protozoa, methanogens, and gaseous and dissolved fermentation end products. The model was extended with static equations to model the hindgut metabolism. Feed composition and twice daily feeding were used as model inputs. Model parameters were estimated to experimental data using a Bayesian calibration procedure, after which the uncertainty of the parameter distribution on the model output was assessed. The model predicted a marked peak in pH2 after feeding that rapidly declined in time. This peak in pH2 caused a decrease in NAD+ to NADH ratio followed by an increased propionate molar proportion at the expense of acetate molar proportion, and an increase in CH4 production that steadily decreased in time, although the magnitude of increase for CH4 emission was less than for pH2. A global sensitivity analysis indicated that parameters that determine the fractional passage rate and NADH oxidation rate altogether explained 86% of the variation in predicted daily CH4 emission. Model evaluation indicated over-prediction of in vivo CH4 emissions shortly after feeding, whereas under-prediction was indicated at later times. The present rumen fermentation modeling effort uniquely provides the integration of the pH2 controlled NAD+ to NADH ratio for dynamically predicting metabolic pathways that yield VFA, H2 and CH4.