Bayesian mechanistic modeling of thermodynamically controlled volatile fatty acid, hydrogen and methane production in the bovine rumen
Bayesian mechanistic modeling of thermodynamically controlled volatile fatty acid, hydrogen and methane production in the bovine rumen
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DOI：
10.1016/j.jtbi.2019.08.008
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发表时间：
2019-11-07
影响因子：
2
通讯作者：
Dijkstra, Jan
中科院分区：
文献类型：
--
作者：
van Lingen, Henk J.;Fadel, James G.;Dijkstra, Jan
瘤胃微生物生态系统中挥发性脂肪酸和氢气产生的驱动机制的动态模拟有助于启发式预测从奶牛到环境中的CH 4排放量。然而，现有的数学瘤胃模型缺乏这些机制的代表性。建立了一个动态机理模型，模拟了氢气分压（p（H2））对挥发性脂肪酸（VFA）发酵途径的热力学控制，包括发酵微生物中NAD（+）与NADH的比例，以及牛瘤胃中甲烷生成。该模型是独特的，与反应动力学和热力学原理密切相关。模型状态变量代表瘤胃碳水化合物底物，细菌和原生动物，产甲烷菌，气体和溶解的发酵终产物。该模型扩展了静态方程模型后肠代谢。饲料组成和每日两次饲喂用作模型输入。使用贝叶斯校准程序估计模型参数到实验数据，之后评估模型输出上的参数分布的不确定性。该模型预测，在喂食后，p（H2）会出现一个明显的峰值，并随时间迅速下降。p（H2）中的该峰导致NAD（+）与NADH的比率降低，随后以乙酸酯摩尔比例为代价增加丙酸酯摩尔比例，并且CH 4产生增加，其随时间稳定地降低，尽管CH 4排放增加的幅度小于p（H2）。一个全球性的敏感性分析表明，参数，确定分数通过率和NADH氧化率共解释86%的预测每日甲烷排放量的变化。模型评估表明，在体内甲烷排放量的过度预测后不久，喂养，而预测不足，在以后的时间。目前的瘤胃发酵建模工作独特地提供了p（H2）控制的NAD（+）与NADH比率的整合，用于动态预测产生VFA、H-2和CH 4的代谢途径。(C)2019爱思唯尔有限公司版权所有。
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 (p(H2)) 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 p(H2) after feeding that rapidly declined in time. This peak in p(H2) 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 p(H2). 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 p(H2) controlled NAD(+) to NADH ratio for dynamically predicting metabolic pathways that yield VFA, H-2 and CH4. (C) 2019 Elsevier Ltd. All rights reserved.