Bridging Time Scales in Cellular Decision Making with a Stochastic Bistable Switch

Cellular transformations which involve a significant phenotypical change of the cell's state use bistable biochemical switches as underlying decision systems. In this work, we aim at linking cellular decisions taking place on a time scale of years to decades with the biochemical dynamics in signal transduction and gene regulation, occuring on a time scale of minutes to hours. We show that a stochastic bistable switch forms a viable biochemical mechanism to implement decision processes on long time scales. As a case study, the mechanism is applied to model the initiation of follicle growth in mammalian ovaries, where the physiological time scale of follicle pool depletion is on the order of the organism's lifespan. We construct a simple mathematical model for this process based on experimental evidence for the involved genetic mechanisms. Despite the underlying stochasticity, the proposed mechanism turns out to yield reliable behavior in large populations of cells subject to the considered decision process. Our model explains how the physiological time constant may emerge from the intrinsic stochasticity of the underlying gene regulatory network. Apart from ovarian follicles, the proposed mechanism may also be of relevance for other physiological systems where cells take binary decisions over a long time scale.Comment: 14 pages, 4 figure

Climacteric Lowers Plasma Levels of Platelet-Derived Microparticles: A Pilot Study in Pre-versus Postmenopausal Women

Background: Climacteric increases the risk of thrombotic events by alteration of plasmatic coagulation. Up to now, less is known about changes in platelet-(PMP) and endothelial cell-derived microparticles (EMP). Methods: In this prospective study, plasma levels of microparticles (MP) were compared in 21 premenopausal and 19 postmenopausal women. Results: No altered numbers of total MP or EMP were measured within the study groups. However, the plasma values of CD61-exposing MP from platelets/megakaryocytes were higher in premenopausal women (5,364 x 10(6)/l, range 4,384-17,167) as compared to postmenopausal women (3,808 x 10(6)/l, range 2,009-8,850; p = 0.020). This differentiation was also significant for the subgroup of premenopausal women without hormonal contraceptives (5,364 x 10(6)/l, range 4,223-15,916; p = 0.047; n = 15). Furthermore, in premenopausal women, higher plasma levels of PMP exposing CD62P were also present as compared to postmenopausal women (288 x 10(6)/l, range 139-462, vs. 121 x 10(6)/l, range 74-284; p = 0.024). This difference was also true for CD63+ PMP levels (281 x 10(6)/l, range 182-551, vs. 137 x 10(6)/l, range 64-432; p = 0.015). Conclusion: Climacteric lowers the level of PMP but has no impact on the number of EMP in women. These data suggest that PMP and EMP do not play a significant role in enhancing the risk of thrombotic events in healthy, postmenopausal women. Copyright (C) 2012 S. Karger AG, Base

A Validated Model of Serum Anti-Müllerian Hormone from Conception to Menopause

Background Anti-Müllerian hormone (AMH) is a product of growing ovarian follicles. The concentration of AMH in blood may also reflect the non-growing follicle (NGF) population, i.e. the ovarian reserve, and be of value in predicting reproductive lifespan. A full description of AMH production up to the menopause has not been previously reported. Methodology/Principal Findings By searching the published literature for AMH concentrations in healthy pre-menopausal females, and using our own data (combined ) we have generated and robustly validated the first model of AMH concentration from conception to menopause. This model shows that 34% of the variation in AMH is due to age alone. We have shown that AMH peaks at age 24.5 years, followed by a decline to the menopause. We have also shown that there is a neonatal peak and a potential pre-pubertal peak. Our model allows us to generate normative data at all ages. Conclusions/Significance These data highlight key inflection points in ovarian follicle dynamics. This first validated model of circulating AMH in healthy females describes a transition period in early adulthood, after which AMH reflects the progressive loss of the NGF pool. The existence of a neonatal increase in gonadal activity is confirmed for females. An improved understanding of the relationship between circulating AMH and age will lead to more accurate assessment of ovarian reserve for the individual woman.Publisher PDFPeer reviewe

Impact of female age and male infertility on ovarian reserve markers to predict outcome of assisted reproduction technology cycles

<p>Abstract</p> <p>Background</p> <p>This study was designed to assess the capability of ovarian reserve markers, including baseline FSH levels, baseline anti-Müllerian hormone (AMH) levels, and antral follicle count (AFC), as predictors of live births during IVF cycles, especially for infertile couples with advanced maternal age and/or male factors.</p> <p>Methods</p> <p>A prospective cohort of 336 first IVF/ICSI cycles undergoing a long protocol with GnRH agonist was investigated. Patients with endocrine disorders or unilateral ovaries were excluded.</p> <p>Results</p> <p>Among the ovarian reserve tests, AMH and age had a greater area under the receiving operating characteristic curve than FSH in predicting live births. Furthermore, AMH and age were the sole predictive factors of live births for women greater than or equal to 35 years of age; while AMH was the major determinant of live births for infertile couples with absence of male factors by multivariate logistic regression analysis. However, all the studied ovarain reserve tests were not preditive of live births for women < 35 years of age or infertile couples with male factors.</p> <p>Conclusion</p> <p>The serum AMH levels were prognostic for pregnancy outcome for infertile couples with advanced female age or absence of male factors. The predictive capability of ovarian reserve tests is clearly influenced by the etiology of infertility.</p

A general piecewise multi-state survival model: Application to breast cancer

Multi-state models are considered in the field of survival analysis for modelling illnesses that evolve through several stages over time. Multi-state models can be developed by applying several techniques, such as non-parametric, semi-parametric and stochastic processes, particularly Markov processes. When the development of an illness is being analysed, its progression is tracked periodically. Medical reviews take place at discrete times, and a panel data analysis can be formed. In this paper, a discrete-time piecewise non-homogeneous Markov process is constructed for modelling and analysing a multi-state illness with a general number of states. The model is built, and relevant measures, such as survival function, transition probabilities, mean total times spent in a group of states and the conditional probability of state change, are determined. A likelihood function is built to estimate the parameters and the general number of cut-points included in the model. Time-dependent covariates are introduced, the results are obtained in a matrix algebraic form and the algorithms are shown. The model is applied to analyse the behaviour of breast cancer. A study of the relapse and survival times of 300 breast cancer patients who have undergone mastectomy is developed. The results of this paper are implemented computationally with MATLAB and R.Ministerio de Economía y Competitividad FQM-307European Regional Development Fund (ERDF) MTM2017-88708-PUniversity of Milano-Bicocca 2014-ATE-022

Menstrual function among women exposed to polybrominated biphenyls: A follow-up prevalence study

BACKGROUND: Alteration in menstrual cycle function is suggested among rhesus monkeys and humans exposed to polybrominated biphenyls (PBBs) and structurally similar polychlorinated biphenyls (PCBs). The feedback system for menstrual cycle function potentially allows multiple pathways for disruption directly through the hypothalamic-pituitary-ovarian axis and indirectly through alternative neuroendocrine axes. METHODS: The Michigan Female Health Study was conducted during 1997–1998 among women in a cohort exposed to PBBs in 1973. This study included 337 women with self-reported menstrual cycles of 20–35 days (age range: 24–56 years). Current PBB levels were estimated by exponential decay modeling of serum PBB levels collected from 1976–1987 during enrollment in the Michigan PBB cohort. Linear regression models for menstrual cycle length and the logarithm of bleed length used estimated current PBB exposure or enrollment PBB exposure categorized in tertiles, and for the upper decile. All models were adjusted for serum PCB levels, age, body mass index, history of at least 10% weight loss in the past year, physical activity, smoking, education, and household income. RESULTS: Higher levels of physical activity were associated with shorter bleed length, and increasing age was associated with shorter cycle length. Although no overall association was found between PBB exposure and menstrual cycle characteristics, a significant interaction between PBB exposures with past year weight loss was found. Longer bleed length and shorter cycle length were associated with higher PBB exposure among women with past year weight loss. CONCLUSION: This study suggests that PBB exposure may impact ovarian function as indicated by menstrual cycle length and bleed length. However, these associations were found among the small number of women with recent weight loss suggesting either a chance finding or that mobilization of PBBs from lipid stores may be important. These results should be replicated with larger numbers of women exposed to similar lipophilic compounds

Qualitative modelling via constraint programming

Qualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications are the study of the dynamics of physical and biological systems at a higher level of abstraction than that obtained by estimation of numerical parameter values for a fixed quantitative model. Qualitative modelling has been studied and implemented to varying degrees of sophistication in Petri nets, process calculi and constraint programming. In this paper we reflect on the strengths and weaknesses of existing frameworks, we demonstrate how recent advances in constraint programming can be leveraged to produce high quality qualitative models, and we describe the advances in theory and technology that would be needed to make constraint programming the best option for scientific investigation in the broadest sense

Biological versus chronological ovarian age:implications for assisted reproductive technology

<p>Abstract</p> <p>Background</p> <p>Women have been able to delay childbearing since effective contraception became available in the 1960s. However, fertility decreases with increasing maternal age. A slow but steady decrease in fertility is observed in women aged between 30 and 35 years, which is followed by an accelerated decline among women aged over 35 years. A combination of delayed childbearing and reduced fecundity with increasing age has resulted in an increased number and proportion of women of greater than or equal to 35 years of age seeking assisted reproductive technology (ART) treatment.</p> <p>Methods</p> <p>Literature searches supplemented with the authors' knowledge.</p> <p>Results</p> <p>Despite major advances in medical technology, there is currently no ART treatment strategy that can fully compensate for the natural decline in fertility with increasing female age. Although chronological age is the most important predictor of ovarian response to follicle-stimulating hormone, the rate of reproductive ageing and ovarian sensitivity to gonadotrophins varies considerably among individuals. Both environmental and genetic factors contribute to depletion of the ovarian oocyte pool and reduction in oocyte quality. Thus, biological and chronological ovarian age are not always equivalent. Furthermore, biological age is more important than chronological age in predicting the outcome of ART. As older patients present increasingly for ART treatment, it will become more important to critically assess prognosis, counsel appropriately and optimize treatment strategies. Several genetic markers and biomarkers (such as anti-Müllerian hormone and the antral follicle count) are emerging that can identify women with accelerated biological ovarian ageing. Potential strategies for improving ovarian response include the use of luteinizing hormone (LH) and growth hormone (GH). When endogenous LH levels are heavily suppressed by gonadotrophin-releasing hormone analogues, LH supplementation may help to optimize treatment outcomes for women with biologically older ovaries. Exogenous GH may improve oocyte development and counteract the age-related decline of oocyte quality. The effects of GH may be mediated by insulin-like growth factor-I, which works synergistically with follicle-stimulating hormone on granulosa and theca cells.</p> <p>Conclusion</p> <p>Patients with biologically older ovaries may benefit from a tailored approach based on individual patient characteristics. Among the most promising adjuvant therapies for improving ART outcomes in women of advanced reproductive age are the administration of exogenous LH or GH.</p