Literature DB >> 22063267

Interaction of fast and slow dynamics in endocrine control systems with an application to β-cell dynamics.

Yi-Fang Wang1, Michael Khan, Hugo A van den Berg.   

Abstract

Endocrine dynamics spans a wide range of time scales, from rapid responses to physiological challenges to with slow responses that adapt the system to the demands placed on it. We outline a non-linear averaging procedure to extract the slower dynamics in a way that accounts properly for the non-linear dynamics of the faster time scale and is applicable to a hierarchy of more than two time scales, although we restrict our discussion to two scales for the sake of clarity. The procedure is exact if the slow time scale is infinitely slow (the dimensionless ε-quantity is the period of the fast time scale fluctuation times an upper bound to the slow time scale rate of change). However, even for an imperfect separation of time scales we find that this construction provides an excellent approximation for the slow-time dynamics at considerably reduced computational cost. Besides the computation advantage, the averaged equation provided a qualitative insight into the interaction of the time scales. We demonstrate the procedure and its advantages by applying the theory to the model described by Tolić et al. [I.M. Tolić, E. Mosekilde, J. Sturis, Modeling the insulin-glucose feedback system: the significance of pulsatile insulin secretion, J. Theor. Biol. 207 (2000) 361-375.] for ultradian dynamics of the glucose-insulin homeostasis feedback system, extended to include β-cell dynamics. We find that the dynamics of the β-cell mass are dependent not only on the glycemic load (amount of glucose administered to the system), but also on the way this load is applied (i.e. three meals daily versus constant infusion), effects that are lost in the inappropriate methods used by the earlier authors. Furthermore, we find that the loss of the protection against apoptosis conferred by insulin that occurs at elevated levels of insulin has a functional role in keeping the β-cell mass in check without compromising regulatory function. We also find that replenishment of β-cells from a rapidly proliferating pool of cells, as opposed to the slow turn-over which characterises fully differentiated β-cells, is essential to the prevention of type 1 diabetes.
Copyright © 2011 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 22063267     DOI: 10.1016/j.mbs.2011.10.003

Source DB:  PubMed          Journal:  Math Biosci        ISSN: 0025-5564            Impact factor:   2.144


  7 in total

Review 1.  Development, growth and maintenance of β-cell mass: models are also part of the story.

Authors:  Anmar Khadra; Santiago Schnell
Journal:  Mol Aspects Med       Date:  2015-02-23

2.  Type 2 diabetes: one disease, many pathways.

Authors:  Joon Ha; Arthur Sherman
Journal:  Am J Physiol Endocrinol Metab       Date:  2020-07-14       Impact factor: 4.310

3.  A Mathematical Model of the Pathogenesis, Prevention, and Reversal of Type 2 Diabetes.

Authors:  Joon Ha; Leslie S Satin; Arthur S Sherman
Journal:  Endocrinology       Date:  2015-12-28       Impact factor: 4.736

4.  Pim1 inhibition as a novel therapeutic strategy for Alzheimer's disease.

Authors:  Ramon Velazquez; Darren M Shaw; Antonella Caccamo; Salvatore Oddo
Journal:  Mol Neurodegener       Date:  2016-07-13       Impact factor: 14.195

Review 5.  Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction.

Authors:  Thomas McGrath; Kevin G Murphy; Nick S Jones
Journal:  J R Soc Interface       Date:  2018-01-24       Impact factor: 4.118

6.  A novel fast-slow model of diabetes progression: Insights into mechanisms of response to the interventions in the Diabetes Prevention Program.

Authors:  Andrea De Gaetano; Thomas Andrew Hardy
Journal:  PLoS One       Date:  2019-10-10       Impact factor: 3.240

Review 7.  Bridging scales through multiscale modeling: a case study on protein kinase A.

Authors:  Britton W Boras; Sophia P Hirakis; Lane W Votapka; Robert D Malmstrom; Rommie E Amaro; Andrew D McCulloch
Journal:  Front Physiol       Date:  2015-09-09       Impact factor: 4.566

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.