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Colloquium February 9

Date: Friday February 9

Time: 16:00 to 17:00

Place: 455 McBryde (Commons Room)

Speaker: William S. Evans, Leon S. Farhi and Michael L Johnson of University of Virginia Health System

Title: Development And Application Of Biomathematical Techniques To Appraise Hormone Signaling Within Endocrine Systems: A Journey From Assessment Of Single Nodes To That Of The Entire Network

Abstract

Nearly four decades have elapsed since it was first recognized that signaling within certain endocrine systems is pulsatile in nature. This fundamental observation led to the question as to whether biomathematical techniques could be developed and subsequently utilized to separate hormone pulses from noise within hormone concentration-time series obtained in clinical studies. Early attempts to document pulsatile secretion utilized statistically-based constructs (e.g., Cluster; Detect), whereas later advances applied deconvolution procedures to separate "pulses" into their underlying secretory event and clearance functions. For example, the state-of-the-art multi-parameter deconvolution analysis (Deconv; AutoDecon) allows for the simultaneous determination of individual secretory burst parameters along with a subject-specific hormone half-life and basal hormone secretion via a fitting procedure of concentration hormone time-series.

More recently, communication between two nodes within endocrine systems (for example, the hypothalamus and pituitary gland) has been assessed using methods which quantitate approximate entropy within the system. One such program (ApEn) quantifies irregularity in concentration-time series and complements pulse and secretory burst detection procedures by evaluating both dominant and subordinate patterns in data. Notably, ApEn will detect changes in underlying episodic behavior not reflected in peak occurrences or amplitudes and provides an explicit barometer of feedback system change in many coupled systems.

Current (and future) efforts are being focused on appraisal of endocrine systems functioning as a network of multiple nodes which are constantly responding to both primary input and modulatory feedback signals which emanate from both within and outside of the primary system. Dynamic methods are employed to assist the intuitive reconstruction of the endocrine axes, which are otherwise challenged by their high complexity. Comparisons between model predictions and experimental outcomes allow for the identification of the dominant regulators and alterations in the control mechanisms related to certain pathological states. This approach which utilizes modeling with coupled differential equations is especially suitable for studying endocrine networks and has several advantages over most of the classical statistical methods typically applied in medical research.

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