Development of a dynamic mathematical model for investigating mammary gland metabolism in lactating cows
Notice bibliographique
Résumé
SUMMARY A dynamic mathematical model of a closed mammary system in lactating cows was developed to incorporate the setpoint concept of tissue activity, using equations where nutrient supply and absorption are locally regulated so as to maintain a given rate of milk protein yield. The model consists of 12 differential equations, 11 of which are concerned with intracellular biochemical compartments and one describes the volume of tissue actively perfused by blood (AP). The intracellular compartments are: amino acids (AAs), acetate, fatty acids (FAs), β-hydroxybutyrate, glucose-6-phosphate, fructose-6-phosphate, phospho-glyceraldehyde, pyruvate, mitochondrial acetyl-CoA, adenosine triphosphate (ATP) and adenosine diphosphate (ADP). The model simulates mechanisms which are aimed at reproducing and, thereby, explain variations in mammary plasma flow (MPF) observed experimentally. The AP changes according to variations in the metabolic status or in the metabolic requirements of the gland. Should the tissue energy charge (i.e. ATP/ADP ratio) exceed a baseline ratio, then AP decreases and consequently MPF declines. Conversely, when milk protein yield increases, AP increases and MPF rises. In the present model, AA uptake by the mammary gland is inhibited by intracellular AAs. It is also assumed that, when milk protein yield diminishes, the respiratory chain and ATP synthesis become uncoupled and consequently ATP yield is reduced. Model evaluation included behavioural analysis and sensitivity analysis. Behaviour analysis was conducted to test whether the model mechanisms reproduced the scenarios from which the model hypotheses were developed, and took into consideration: an increase in arterial glucose concentration (HIGLC), increases in arterial concentrations of non-esterified FAs, triacylglycerol and β-hydroxybutyrate (HIFAT), a 50% reduction of arterial histidine concentration (LOHIS), and a hyperinsulinaemic euglycaemic clamp (HIINS). Both HIGLC and HIFAT resulted in a decrease in MPF and in milk protein yield; moreover, the scenario HIGLC also produced a notable decrease in the extraction of glucose. The scenario LOHIS resulted in increased MPF and extraction of His from plasma. However these responses were not sufficiently large to prevent a severe reduction of milk protein yield which was accompanied by a reduction in the extraction of other essential AAs. The scenario HIINS resulted in an increase of MPF and of milk protein yield, in the extraction of His and of other essential AAs. Model sensitivity analysis focused on variation of both affinity and inhibition constants of some of the Michaelis–Menten equations. Improvements in model structure and directions for future research suggested by the modelling analysis are discussed.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».