{"id":9273,"date":"2024-02-28T09:57:50","date_gmt":"2024-02-28T00:57:50","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=9273"},"modified":"2024-03-07T10:16:16","modified_gmt":"2024-03-07T01:16:16","slug":"2024-03-13-jc","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/2024-03-13-jc\/","title":{"rendered":"Brenda Lyn Gavina, Reduced model for female endocrine dynamics: Validation and functional variations"},"content":{"rendered":"

We will discuss about “Reduced model for female endocrine dynamics: Validation and functional variations.” Mathematical Biosciences 358 (2023): 108979.<\/p>\n

Abstract<\/p>\n

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A normally functioning\u00a0menstrual cycle<\/span>\u00a0requires significant crosstalk between hormones originating in ovarian and\u00a0brain<\/span>\u00a0tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this\u00a0endocrine system<\/span>\u00a0is a challenge to identifying mechanisms of cycle disruption, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype\u2014displaying relatively normal hormone levels and cycle dynamics\u2014that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone.<\/p>\n

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