Candidate mechanisms underlying atypical progesterone profiles as deduced from parameter perturbations in a mathematical model of the bovine estrous cycle

Bôer, H.; Apri, Mochamad; Molenaar, Jaap; Stötzel, C.; Veerkamp, R.F.; Woelders, H.

doi:10.3168/jds.2011-5241

Cited by 7 publications

(26 citation statements)

References 13 publications

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“…The next step is to characterise how and why the components are interlinked. One approach is to explore the underlying mechanisms, for example, associating production with reproduction (Boer et al, 2012). This is extremely useful for linking key robustness mechanisms to gene expression and genomics.…”

Section: Friggens Blanc Berry and Puilletmentioning

confidence: 99%

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management

Friggens

Blanc

Berry

et al. 2017

Animal

151

145

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As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.

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Section: Friggens Blanc Berry and Puilletmentioning

confidence: 99%

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management

Friggens

Blanc

Berry

et al. 2017

Animal

151

145

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show abstract

“…Bien que les facteurs majeurs (production laitière, bilan énergétique…) influençant la fertilité soient identifiés, les mécanismes physiologiques sous-jacents sont encore mal connus (Friggens et al 2010a). Néanmoins, les modèles systémiques de la physiologie de la reproduction peuvent être utilisés pour identifier les mécanismes potentiellement impliqués dans divers types de profils hormonaux anormaux (Boer et al 2012. Cette stratégie est puissante, non seulement pour rechercher des biomarqueurs, mais aussi pour explorer les différentes voies métaboliques par lesquelles les facteurs environ-nementaux et les compromis entre fonctions peuvent influer sur la reproduction.…”

Section: / Définir De Nouveaux Critères Associés à La Robustesseunclassified

Des animaux plus robustes : un enjeu majeur pour le développement durable des productions animales nécessitant l’essor du phénotypage fin et à haut débit

Phocas¹,

Bobe²,

Bodin³

et al. 2014

INRA Prod. Anim.

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L’enjeu majeur du phénotypage animal est d’acquérir une connaissance systémique de la robustesse des animaux d’élevage pour répondre au mieux à une double finalité en termes d’exploitation de la variabilité des aptitudes animales : une sélection éclairée vers des objectifs majeurs pour améliorer l’efficience et la robustesse des génotypes, et un élevage de précision exploitant la variabilité individuelle des animaux pour gagner en résilience à l’échelle du troupeau, ou pour améliorer la conduite à génotype donné. Deux défis majeurs de connaissance, interdépendants et communs à toutes les filières animales, sont à relever pour améliorer la robustesse des animaux : i) comprendre et prédire les compromis entre fonctions vitales, c’est-à-dire les changements de priorités dans l’allocation de ressources limitées ; ii) comprendre et exploiter les aspects temporels de la robustesse au cours de la vie de l’animal pour lui permettre de maintenir son niveau de production dans une large gamme d’environnements sans pour autant compromettre sa reproduction, sa santé et son bien-être. Atteindre ces objectifs nécessite de lever les verrous techniques suivants : i) définir des phénotypes complexes à partir de l’intégration de données obtenues à différentes échelles moléculaires, tissulaires, de l’animal ou d’une de ses fonctions ; ii) mettre en oeuvre des technologies à haut débit pour caractériser à moindre coût et le plus précisément possible le plus grand nombre d’animaux ; iii) développer d’importantes bases de données, des méthodes et outils informatiques performants, nécessaires aux traitements des informations à des fins de modélisation et de biologie prédictive.

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“…A systems modeling approach to reproduction was used in several studies (as reviewed in Vetharaniam et al, 2010). More recently, deterministic mathematical models for cattle reproduction were proposed by Boer et al (2011aBoer et al ( , 2011bBoer et al ( and 2012 and Martin et al (2012), and these groups noted that these models could be used as a framework for studying nutritional and physiological effects on estrus cyclicity. The mathematical model by Boer et al (2011aBoer et al ( , 2011bBoer et al ( and 2012 describes the dynamics of the bovine estrous cycle as a set of linked differential equations.…”

Section: Introductionmentioning

confidence: 99%

“…As a first step in model validation, Boer et al (2011b) demonstrated that it is possible to find parameter values that can simulate a normal 'average' cow. A further test of the model was to demonstrate that it is possible to find parameter values able to simulate disruptions of normal cyclicity like cystic ovaries (Boer et al, 2012). Furthermore, the model was validated by simulating estrous cycle synchronization protocols based on prostaglandin F2α analogue (PGF2α) administration (Stötzel et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the model was validated by simulating estrous cycle synchronization protocols based on prostaglandin F2α analogue (PGF2α) administration (Stötzel et al, 2012). All these previous studies had been aimed at finding parameterizations for an 'average' cow that could simulate estrous cycle characteristics like the number of follicular waves (Boer et al, 2011a) or cystic ovaries (Boer et al, 2012). However, it is an important step in model validation to estimate parameter values from an independent experimental data set that was not used for initial model parameterization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of a mathematical model of the bovine estrous cycle for cows with different estrous cycle characteristics

Bôer

Butler

Stötzel

et al. 2017

Animal

Self Cite

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A recently developed mechanistic mathematical model of the bovine estrous cycle was parameterized to fit empirical data sets collected during one estrous cycle of 31 individual cows, with the main objective to further validate the model. The a priori criteria for validation were (1) the resulting model can simulate the measured data correctly (i.e. goodness of fit), and (2) this is achieved without needing extreme, probably non-physiological parameter values. We used a least squares optimization procedure to identify parameter configurations for the mathematical model to fit the empirical in vivo measurements of follicle and corpus luteum sizes, and the plasma concentrations of progesterone, estradiol, FSH and LH for each cow. The model was capable of accommodating normal variation in estrous cycle characteristics of individual cows. With the parameter sets estimated for the individual cows, the model behavior changed for 21 cows, with improved fit of the simulated output curves for 18 of these 21 cows. Moreover, the number of follicular waves was predicted correctly for 18 of the 25 two-wave and three-wave cows, without extreme parameter value changes. Estimation of specific parameters confirmed results of previous model simulations indicating that parameters involved in luteolytic signaling are very important for regulation of general estrous cycle characteristics, and are likely responsible for differences in estrous cycle characteristics between cows.

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Candidate mechanisms underlying atypical progesterone profiles as deduced from parameter perturbations in a mathematical model of the bovine estrous cycle

Cited by 7 publications

References 13 publications

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management

Des animaux plus robustes : un enjeu majeur pour le développement durable des productions animales nécessitant l’essor du phénotypage fin et à haut débit

Validation of a mathematical model of the bovine estrous cycle for cows with different estrous cycle characteristics

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