Perspectives on Mathematical Modeling for Receptor-Mediated Processes

Estrous behavior in dairy cows: identification of underlying mechanisms and gene functions

Selection in dairy cattle for a higher milk yield has coincided with declined fertility. One of the factors is reduced expression of estrous behavior. Changes in systems that regulate the estrous behavior could be manifested by altered gene expression. This literature review describes the current knowledge on mechanisms and genes involved in the regulation of estrous behavior. The endocrinological regulation of the estrous cycle in dairy cows is well described. Estradiol (E2) is assumed to be the key regulator that synchronizes endocrine and behavioral events. Other pivotal hormones are, for example, progesterone, gonadotropin releasing hormone and insulin-like growth factor-1. Interactions between the latter and E2 may play a role in the unfavorable effects of milk yield-related metabolic stress on fertility in high milk-producing dairy cows. However, a clear understanding of how endocrine mechanisms are tied to estrous behavior in cows is only starting to emerge. Recent studies on gene expression and signaling pathways in rodents and other animals contribute to our understanding of genes and mechanisms involved in estrous behavior. Studies in rodents, for example, show that estrogen-induced gene expression in specific brain areas such as the hypothalamus play an important role. Through these estrogen-induced gene expressions, E2 alters the functioning of neuronal networks that underlie estrous behavior, by affecting dendritic connections between cells, receptor populations and neurotransmitter releases. To improve the understanding of complex biological networks, like estrus regulation, and to deal with the increasing amount of genomic information that becomes available, mathematical models can be helpful. Systems biology combines physiological and genomic data with mathematical modeling. Possible applications of systems biology approaches in the field of female fertility and estrous behavior are discussed.

A physiologically-based kinetic model for the prediction of plasma cholesterol concentrations in the mouse

The LDL cholesterol (LDL-C) and HDL cholesterol (HDL-C) concentrations are determined by the activity of a complex network of reactions in several organs. Physiologically-based kinetic (PBK) computational models can be used to describe these different reactions in an integrated, quantitative manner. A PBK model to predict plasma cholesterol levels in the mouse was developed, validated, and analyzed. Kinetic parameters required for defining the model were obtained using data from published experiments. To construct the model, a set of appropriate submodels was selected from a set of 65,536 submodels differing in the kinetic expressions of the reactions. A submodel was considered appropriate if it had the ability to correctly predict an increased or decreased plasma cholesterol level for a training set of 5 knockout mouse strains. The model thus defined consisted of 8 appropriate submodels and was validated using data from an independent set of 9 knockout mouse strains. The model prediction is the average prediction of 8 appropriate submodels. Remarkably, these submodels had in common that the rate of cholesterol transport from the liver to HDL was not dependent on hepatic cholesterol concentrations. The model appeared able to accurately predict in a quantitative way the plasma cholesterol concentrations of all 14 knockout strains considered, including the frequently used Ldlr-/- and Apoe-/- mouse strains. The model presented is a useful tool to predict the effect of knocking out genes that act in important steps in cholesterol metabolism on total plasma cholesterol, HDL-C and LDL-C in the mouse.