Mechanistic model of hormonal contraception

Guidelines for mechanistic modeling and analysis in cardiovascular research

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data.

Toward an optimal contraception dosing strategy

Anovulation refers to a menstrual cycle characterized by the absence of ovulation. Exogenous hormones such as synthetic progesterone and estrogen have been used to attain this state to achieve contraception. However, large doses are associated with adverse effects such as increased risk for thrombosis and myocardial infarction. This study utilizes optimal control theory on a modified menstrual cycle model to determine the minimum total exogenous estrogen/progesterone dose, and timing of administration to induce anovulation. The mathematical model correctly predicts the mean daily levels of pituitary hormones LH and FSH, and ovarian hormones E2, P4, and Inh throughout a normal menstrual cycle and reflects the reduction in these hormone levels caused by exogenous estrogen and/or progesterone. Results show that it is possible to reduce the total dose by 92% in estrogen monotherapy, 43% in progesterone monotherapy, and that it is most effective to deliver the estrogen contraceptive in the mid follicular phase. Finally, we show that by combining estrogen and progesterone the dose can be lowered even more. These results may give clinicians insights into optimal formulations and schedule of therapy that can suppress ovulation.

A Mathematical Model of Estradiol Production from Ultrasound Data for Bovine Ovarian Follicles

In this paper, we present a new way to assess the concentration of estradiol (E2) and Insulin Growth Factor-1 (IGF) based on the results from ultrasound scans combined with mathematical models. The IGF1 model is based on the progesterone (P4) concentration, which can be estimated with models calculating P4 level based on the size/volume of corpus luteum (CL) measured during ultrasound scans. At this moment little is known about the underlying reasons for double ovulation and silent heat occurrences. Both of these are linked to the level of IGF1: double ovulations are linked to higher IGF1 levels and and silent heat is linked to lower E2 to P4 ratio. These models can help to improve understanding of the related concentrations of E2 and IGF1. Currently, it is known that diet and genetic factors have an impact on ovulation rates and silent heat. In this study, we also examine the decline of the production of E2 in vivo by atretic follicles throughout the process of atresia. This is the first recorded quantitative description of this decline.

The Impact of Estrogen and Estrogen-Like Molecules in Neurogenesis and Neurodegeneration: Beneficial or Harmful?

Estrogens and estrogen-like molecules can modify the biology of several cell types. Estrogen receptors alpha (ERα) and beta (ERβ) belong to the so-called classical family of estrogen receptors, while the G protein-coupled estrogen receptor 1 (GPER-1) represents a non-classical estrogen receptor mainly located in the plasma membrane. As estrogen receptors are ubiquitously distributed, they can modulate cell proliferation, differentiation, and survival in several tissues and organs, including the central nervous system (CNS). Estrogens can exert neuroprotective roles by acting as anti-oxidants, promoting DNA repair, inducing the expression of growth factors, and modulating cerebral blood flow. Additionally, estrogen-dependent signaling pathways are involved in regulating the balance between proliferation and differentiation of neural stem/progenitor cells (NSPCs), thus influencing neurogenic processes. Since several estrogen-based therapies are used nowadays and estrogen-like molecules, including phytoestrogens and xenoestrogens, are omnipresent in our environment, estrogen-dependent changes in cell biology and tissue homeostasis have gained attention in human health and disease. This article provides a comprehensive literature review on the current knowledge of estrogen and estrogen-like molecules and their impact on cell survival and neurodegeneration, as well as their role in NSPCs proliferation/differentiation balance and neurogenesis.