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Bovine fertility is the subject of extensive research in animal sciences, especially because fertility of dairy cows has declined during the last decades. The regulation of estrus is controlled by the complex interplay of various organs and hormones. Mathematical modeling of the bovine estrous cycle could help in understanding the dynamics of this complex biological system. In this paper we present a mathematical model of the bovine estrous cycle that includes the processes of follicle and corpus luteum development and the key hormones that interact to control these processes. Focus in this paper is on development of the model, but also some simulation results are presented, showing that a set of equations and parameters is obtained that describes the system consistent with empirical knowledge. Even though the majority of the mechanisms that are included are only known qualitatively as stimulatory or inhibitory effects, the model surprisingly well features quantitative observations made in reality. This model of the bovine estrous cycle could be used as a basis for more elaborate models with the ability to study effects of external manipulations and genetic differences.
This study presents a differential equation model for the feedback mechanisms between Gonadotropin-releasing Hormone (GnRH), Follicle-Stimulating Hormone (FSH), Luteinizing Hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle.
In contrast to other models, this model does not involve delay differential equations and is based on deterministic modelling of the GnRH pulse pattern, which allows for faster simulation times and efficient parameter identification.
These steps were essential to tackle the task of developing a mathematical model for the administration of GnRH analogues.
The focus of this paper is on model development for GnRH receptor binding and the integration of a pharmacokinetic/pharmacodynamic model for the GnRH agonist Nafarelin and the GnRH antagonist Cetrorelix into the menstrual cycle model.
The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle in 12 healthy women.
Moreover, it correctly predicts the changes in the cycle following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.
Our model of the bovine estrous cycle is a set of ordinary differential equations which generates hormone profiles of successive estrous cycles with several follicular waves per cycle. It describes the growth and decay of the follicles and the corpus luteum, as well as the change of the key substances over time. In this work we describe recent improvements of this model, including the introduction of new components, and elimination of time delays. We validate our model by showing that the simulations agree with observations from synchronization studies and with measured progesterone data after a single dose administration of synthetic prostaglandin F2alpha.
Potassium is fundamental for cell functioning including signal transduction, acid-base- and water-metabolism.
Since diet of dairy cows is generally rich in potassium, hypokalemia was not in the focus of research for long time. Furthermore, hypokalemia was not frequently diagnosed because blood potassium content is difficult to measure. In recent years, measurement methods have been improved. Nowadays hypokalemia is increasingly diagnosed in cows with disorders such as abomasal displacement, ketosis or down cow syndrome, calling for intensified research on this topic.
In this report we describe the development of a basic mechanistic, dynamic model of potassium balance based on ordinary differential and algebraic equations. Parameter values are obtained from data of a clinical trial in which potassium balance and the influence of therapeutic intervention in glucose and electrolyte metabolism on potassium balance in non-lactating dairy cows were studied. The model is formulated at a high abstraction level and includes information and hypotheses from literature. This work represents a first step towards the understanding and design of effective prophylactic feed additives and treatment strategies.
This work deals with the reduction of a previously developed ODE model for the bovine estrous cycle. After applying a method for exploring the parameter space by Apri et al. (2012), we perform structure-based reduction steps and several system specific adaptations. Overall, the original model consisting of 15 ODEs and 60 parameters is reduced to a model of 10 odes and 38 parameters. The reduced model qualitatively reproduces the state trajectories of the original model.
One of the main goals of mathematical modelling in systems biology related to medical applications is to obtain patient-specific parameterisations and model predictions.
In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Using these population data, we propose an iterative algorithm for contructing an informative prior distribution, which then serves as the basis for computing patient-specific posteriors and obtaining individual predictions. We demonsrate the performance of our method by applying it to a low-dimensional parameter estimation problem in a toy model as well as to a high-dimensional ODE model of the human menstrual cycle, which represents a typical example from systems biology modelling.
One of the main goals of mathematical modelling in systems medicine related to medical applications is to obtain patient-specific parameterizations and model predictions. In clinical practice, however, the number of available measurements for single patients is usually limited due to time and cost restrictions. This hampers the process of making patient-specific predictions about the outcome of a treatment. On the other hand, data are often available for many patients, in particular if extensive clinical studies have been performed. Therefore, before applying Bayes’ rule separately to the data of each patient (which is typically performed using a non-informative prior), it is meaningful to use empirical Bayes methods in order to construct an informative prior from all available data. We compare the performance of four priors - a non-informative prior and priors chosen by nonparametric maximum likelihood estimation (NPMLE), by maximum penalized lilelihood estimation (MPLE) and by doubly-smoothed maximum likelihood estimation (DS-MLE) - by applying them to a low-dimensional parameter estimation problem in a toy model as well as to a high-dimensional ODE model of the human menstrual cycle, which represents a typical example from systems biology modelling.
The estrous cycle of mono-ovulatory species such as cows or humans, is known to show two or more waves of follicular growth and decline between two successive ovulations. Within each wave, there is one dominant follicle escorted by subordinate follicles of varying number. Under the surge of the luteinizing hormone a growing dominant follicle ovulates. Rarely the number of ovulating follicles exceeds one. In the biological literature, the change of hormonal concentrations and individually varying numbers of follicular receptors are made responsible for the selection of exactly one dominant follicle, yet a clear cause has not been identified. In this paper, we suggest a synergistic explanation based on competition, formulated by a parsimoniously defined system of ordinary differential equations (ODEs) that quantifies the time evolution of multiple follicles and their competitive interaction during one wave. Not discriminating between follicles, growth and decline are given by fixed rates. Competition is introduced via a growth-suppressing term, equally supported by all follicles. We prove that the number of dominant follicles is determined exclusively by the ratio of follicular growth and competition. This number turns out to be independent of the number of subordinate follicles. The asymptotic behavior of the corresponding dynamical system is investigated rigorously, where we demonstrate that the omega-limit set only contains fixed points. When also including follicular decline, our ODEs perfectly resemble ultrasound data of bovine follicles. Implications for the involved but not explicitly modeled hormones are discussed.
Knochenremodellierung beinhaltet den Auf- und Abbau der Knochenmasse durch die verschiedenen Knochenzellen und findet fast überall, auch am Skelett Erwachsener, statt. Für die Erneuerung der Knochensubstanz sind die Osteoblasten zuständig. Sie ersetzen exakt die Menge der Knochenmasse, welche zuvor durch Osteoklasten abgebaut wurde. Störungen dieses, durch viele Faktoren beeinflussten Prozesses führen zu pathologischen Veränderungen, beispielsweise zu Osteoporose oder Arthritis.
Auf der Grundlage analysierter publizierter Modelle wurde ein Modell entwickelt, welches den Einfluss einiger dieser Faktoren realistisch abbildet. Auf diese Weise kann die Wirkung von basalem PTH, des RANKL-OPG-Systems ( RANKL: Receptor activator of nuclear factor kappa-B ligand, OPG: Osteoprotegerin) und des Estradiols auf den Knochenstoffwechsel am vorgestellten System untersucht werden.
Außerdem wurde durch Estradiolmangel hervorgerufene Osteoporose und der kurative Effekt von synthetischen Medikamenten wie Estradiol oder intermittierend verabreichtem PTH modelliert.
Mit der Parameterschätzung anhand des Gauß-Newton-Verfahrens wird des Weiteren eine Methode vorgestellt, die es ermöglicht, mathematische Modelle bestmöglich durch Variation der Parameterwerte an experimentelle Daten anzupassen. Das am Zuse-Institut Berlin entwickelte Softwarepaket POEM wendet diesen Algorithmus an und wird ebenfalls erläutert.
To counteract the antagonistic relationship between milk yield and fertility in dairy cow, a
deeper understanding of the underlying biological mechanisms is required. For this purpose,
we study physiological networks related to reproduction and metabolism in dairy cows. We
interactively develop dynamic, mechanistic models by fitting the models to experimental data
and mechanistic knowledge. We have already developed models for potassium balance and
hormonal regulation of fertility in the dairy cow, which will briefly be reviewed here. The main
focus of this article is a glucose-insulin model currently developed by us. This model links the
bovine hormonal cycle and the potassium balance to glucose and thus to energy metabolism.
The models can be applied in scientific research, education, experimental planning, drug
development and production on farms.
A deterministic model of tuberculosis in sub-Saharan Africa in general and Cameroon in particular including lack of access to the treatment and weak diagnose capacity is designed and analyzed with respect to its transmission dynamics.
The model includes both frequency- and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative.
A sensitivity analysis of model parameters is performed and most sensitive parameters of the model are identified using a state-of-the-art Gauss-Newton
Method. In particular, parameters representing the proportion of individuals having access to medical facilities have a large impact on the dynamics of the disease. It has been shown that an increase of these parameter values over the
time can significantly reduce the disease burden in the population within the next 15 years.
We present a mechanistic pharmacokinetic-pharmacodynamic model to simulate the effect of dexamethasone on the glucose metabolism in dairy cows.
The coupling of the pharmacokinetic model to the pharmacodynamic model
is based on mechanisms underlying homeostasis regulation by dexamethasone.
In particular, the coupling takes into account the predominant role of dexamethasone in stimulating glucagon secretion, glycogenolysis and lipolysis and in
impairing the sensitivity of cells to insulin. Simulating the effect of a single
dose of dexamethasone on the physiological behaviour of the system shows that
the adopted mechanisms are able to induce a temporary hyperglycemia and
hyperinsulinemia, which captures the observed data in non-lactating cows. In
lactating cows, the model simulations show that a single dose of dexamethasone
reduces the lipolytic effect, owing to the reduction of glucose uptake by the
mammary gland.
Nutrition plays a crucial role in regulating reproductive hormones and follicular
development in cattle. This is visible particularly during the time of negative
energy balance at the onset of milk production after calving. Here, elongated
periods of anovulation have been observed, resulting from alterations in luteiniz-
ing hormone concentrations, likely caused by lower glucose and insulin concen-
trations in the blood. The mechanisms that result in a reduced fertility are
not completely understood, although a close relationship to the glucose-insulin
metabolism is widely supported. Following this idea, a mathematical model of
the hormonal network combining reproductive hormones and hormones that are
coupled to the glucose compartments within the body of the cow was developed.
The model is built on ordinary differential equations and relies on previously
introduced models on the bovine estrous cycle and the glucose-insulin dynam-
ics. Necessary modifications and coupling mechanisms are thoroughly discussed.
Depending on the composition and the amount of food, in particular the glu-
cose content in the dry matter, the model quantifies reproductive hormones and
follicular development over time. Simulation results for different nutritional
regimes in lactating and non-lactating dairy cows are examined and compared
with experimental studies. Regarding its applicability, this work is an early
attempt towards developing in silico feeding strategies and may eventually help
refining and reducing animal experiments.
When estimating a probability density within the empirical Bayes framework, the non-parametric maximum likelihood estimate (NPMLE) usually tends to overfit the data. This issue is usually taken care of by regularization - a penalization term is subtracted from the marginal log-likelihood before the maximization step, so that the estimate favors smooth solutions, resulting in the so-called maximum penalized likelihood estimation (MPLE).
The majority of penalizations currently in use are rather arbitrary brute-force solutions, which lack invariance under transformation of the parameters(reparametrization) and measurements.
This contradicts the principle that, if the underlying model
has several equivalent formulations, the methods of inductive inference should lead to consistent results. Motivated by this principle and using an information-theoretic point of view, we suggest an entropy-based penalization term that guarantees this kind of invariance. The resulting density estimate can be seen as a generalization of reference priors. Using the reference prior as a hyperprior, on the other hand, is argued to be a poor choice for regularization. We also present an insightful connection between the NPMLE, the cross entropy
and the principle of minimum discrimination information suggesting another method of inference that contains the doubly-smoothed maximum likelihood estimation as a special case.
We present an overview of the current status of the European collaborative
project PAEON. The challenge of PAEON is to provide specialists in reproduc-
tive medicine with a computerised model of the menstrual cycle under normal
and various pathological conditions, which will allow them to get further in-
sight in fertility dynamics. This model also enables the simulation of treatment
protocols, which were used within in vitro fertilization. By the definition of
virtual patients through biologically admissible parametrizations our approach
allows not only the evaluation of a given treatment strategy in silico, but also
the design and optimization of such protocols. Once a protocol is formalized
in the virtual hospital, the success can be controlled by a treatment execution
monitor, which works then as a clinical decision support system. All these tools
will be combined in a virtual hospital environment, enabling the access to the
PAEON services through the web.