Macroscopic Models for Human Circadian Rhythms

Journal of Biological Rhythms - Tập 34 Số 6 - Trang 658-671 - 2019
Kevin M. Hannay1, Victoria Booth2,3, Daniel B. Forger4,3
1*Department of Mathematics, Schreiner University, Kerrville, Texas
2Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan
3Department of Mathematics, University of Michigan, Ann Arbor, Michigan
4Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan

Tóm tắt

Mathematical models have a long and influential history in the study of human circadian rhythms. Accurate predictive models for the human circadian light response have been used to study the impact of a host of light exposures on the circadian system. However, generally, these models do not account for the physiological basis of these rhythms. We illustrate a new paradigm for deriving models of the human circadian light response. Beginning from a high-dimensional model of the circadian neural network, we systematically derive low-dimensional models using an approach motivated by experimental measurements of circadian neurons. This systematic reduction allows for the variables and parameters of the derived model to be interpreted in a physiological context. We fit and validate the resulting models to a library of experimental measurements. Finally, we compare model predictions for experimental measurements of light levels and discuss the differences between our model’s predictions and previous models. Our modeling paradigm allows for the integration of experimental measurements across the single-cell, tissue, and behavioral scales, thereby enabling the development of accurate low-dimensional models for human circadian rhythms.

Từ khóa


Tài liệu tham khảo

10.1073/pnas.142039099

10.1073/pnas.1521178113

10.1371/journal.pone.0168954

10.1016/S0304-3940(98)00971-9

10.1113/jphysiol.2011.226555

10.1126/science.284.5423.2177

10.1126/science.2734611

10.5665/sleep.5774

10.1073/pnas.1420753112

10.1146/annurev-physiol-021909-135821

10.1016/S0304-3940(01)02427-2

10.1037/0735-7044.115.4.895

10.1016/j.neuroscience.2016.01.072

10.1177/0748730412455915

10.1016/j.neuron.2013.08.022

10.1523/JNEUROSCI.0469-12.2012

10.1111/j.1460-9568.2011.07682.x

10.1177/074873099129000867

10.1177/0748730411416341

10.1177/0748730413504277

10.1152/ajpendo.00385.2003

10.1126/sciadv.1701047

10.1007/s10552-005-9015-4

10.1126/science.3883493

10.1080/07420520500180371

10.1006/jtbi.1998.0667

10.1038/350059a0

10.1177/074873049400900310

10.1113/jphysiol.2003.040477

10.1038/msb.2012.62

10.1177/0748730407299200

Kronauer RE, 1982, Am J Physiol, 242, R3

10.1177/074873099129001073

10.1007/978-3-642-69689-3

10.1016/j.cub.2009.03.051

10.1007/s10552-005-9004-7

10.1073/pnas.0602425103

10.1016/S0092-8674(00)80473-0

10.1063/1.4954275

10.1177/0748730403018003006

10.1016/0006-8993(72)90054-6

10.1073/pnas.1421200112

10.1523/JNEUROSCI.5586-11.2012

10.1063/1.2930766

10.5665/sleep.4998

10.1177/0748730410369208

10.1007/BF01417856

10.1164/rccm.201309-1735OC

10.1152/ajpregu.2000.279.5.R1574

10.1016/j.smrv.2007.07.005

10.1371/journal.pcbi.1003523

10.1113/jphysiol.2012.227892

10.1073/pnas.69.6.1583

10.1371/journal.pone.0004976

10.1126/sciadv.1501705

10.1016/0022-5193(72)90181-6

10.1007/978-1-4757-3484-3

10.1177/0748730402239679

10.1111/j.1469-7793.2000.00695.x

10.1093/jamia/ocy064