Publications by authors named "Daniel Forger"

61 Publications

Inferring causality in biological oscillators.

Bioinformatics 2021 Aug 31. Epub 2021 Aug 31.

Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.

Motivation: Fundamental to biological study is identifying regulatory interactions. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulations computationally. However, when components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test the reproducibility of time series given a specific model but require inefficient simulations and have limited applicability.

Results: We develop an inference method based on a general model of molecular, neuronal, and ecological oscillatory systems that merges the advantages of both model-based and model-free methods, namely accuracy, broad applicability, and usability. Our method successfully infers the positive and negative regulations within various oscillatory networks, e.g., the repressilator and a network of cofactors at the pS2 promoter, outperforming popular inference methods.

Availability: We provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to uncover the mechanisms by which diverse systems generate oscillations. Accompanying MATLAB code under a BSD-style license and examples are available at ttps://github.com/Mathbiomed/ION. Additionally, the code is available under a CC-BY 4.0 License at https://doi.org/10.6084/m9.figshare.16431408.v1.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab623DOI Listing
August 2021

High-frequency temperature monitoring for early detection of febrile adverse events in patients with cancer.

Cancer Cell 2021 Sep 12;39(9):1167-1168. Epub 2021 Aug 12.

Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address:

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http://dx.doi.org/10.1016/j.ccell.2021.07.019DOI Listing
September 2021

Genomic heterogeneity affects the response to Daylight Saving Time.

Sci Rep 2021 Jul 20;11(1):14792. Epub 2021 Jul 20.

Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, 48109, USA.

Circadian clocks control the timing of many physiological events in the 24-h day. When individuals undergo an abrupt external shift (e.g., change in work schedule or travel across multiple time zones), circadian clocks become misaligned with the new time and may take several days to adjust. Chronic circadian misalignment, e.g., as a result of shift work, has been shown to lead to several physical and mental health problems. Despite the serious health implications of circadian misalignment, relatively little is known about how genetic variation affects an individual's ability to entrain to abrupt external changes. Accordingly, we used the one-hour advance from the onset of daylight saving time (DST) as a natural experiment to comprehensively study how individual heterogeneity affects the shift of sleep/wake cycles in response to an abrupt external time change. We found that individuals genetically predisposed to a morning tendency adjusted to the advance in a few days, while genetically predisposed evening-inclined individuals had not shifted. Observing differential effects by genetic disposition after a one-hour advance underscores the importance of heterogeneity in adaptation to external schedule shifts. These genetic differences may affect how individuals adjust to jet lag or shift work as well.
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http://dx.doi.org/10.1038/s41598-021-94459-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292316PMC
July 2021

Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study.

JMIR Res Protoc 2021 Jun 4. Epub 2021 Jun 4.

Division of Pediatric Hematology Oncology, Department of Pediatrics, University of Michigan, 1500 E. Medical Center DrD4118 Medical Professional Building, Ann Arbor, US.

Background: The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student's mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being.

Objective: Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19.

Methods: We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints - at baseline, 1-month later, 2-months later, 3-months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population.

Results: This study enrolled 2,158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing.

Conclusions: This study examined student health and well-being during the COVID-19 pandemic. It also assessed the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19.

Clinicaltrial: ClinicalTrials.gov NCT04766788.
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http://dx.doi.org/10.2196/29561DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8386373PMC
June 2021

M1-Type, but Not M4-Type, Melanopsin Ganglion Cells Are Physiologically Tuned to the Central Circadian Clock.

Front Neurosci 2021 6;15:652996. Epub 2021 May 6.

Department of Mathematics, University of Michigan, Ann Arbor, MI, United States.

Proper circadian photoentrainment is crucial for the survival of many organisms. In mammals, intrinsically photosensitive retinal ganglion cells (ipRGCs) can use the photopigment melanopsin to sense light independently from rod and cone photoreceptors and send this information to many brain nuclei such as the suprachiasmatic nucleus (SCN), the site of the central circadian pacemaker. Here, we measure ionic currents and develop mathematical models of the electrical activity of two types of ipRGCs: M1, which projects to the SCN, and M4, which does not. We illustrate how their ionic properties differ, mainly how ionic currents generate lower spike rates and depolarization block in M1 ipRGCs. Both M1 and M4 cells have large geometries and project to higher visual centers of the brain via the optic nerve. Using a partial differential equation model, we show how axons of M1 and M4 cells faithfully convey information from the soma to the synapse even when the signal at the soma is attenuated due to depolarization block. Finally, we consider an ionic model of circadian photoentrainment from ipRGCs synapsing on SCN neurons and show how the properties of M1 ipRGCs are tuned to create accurate transmission of visual signals from the retina to the central pacemaker, whereas M4 ipRGCs would not evoke nearly as efficient a postsynaptic response. This work shows how ipRGCs and SCN neurons' electrical activities are tuned to allow for accurate circadian photoentrainment.
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http://dx.doi.org/10.3389/fnins.2021.652996DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134526PMC
May 2021

Predicting circadian phase across populations: a comparison of mathematical models and wearable devices.

Sleep 2021 May 20. Epub 2021 May 20.

Department of Mathematics, University of Michigan, Ann Arbor, MI, USA.

From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase non-invasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 hour, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.
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http://dx.doi.org/10.1093/sleep/zsab126DOI Listing
May 2021

Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study.

JMIR Res Protoc 2021 May 12;10(5):e29562. Epub 2021 May 12.

Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, United States.

Background: Health care workers (HCWs) have been working on the front lines of the COVID-19 pandemic with high risks of viral exposure, infection, and transmission. Standard COVID-19 testing is insufficient to protect HCWs from these risks and prevent the spread of disease. Continuous monitoring of physiological data with wearable sensors, self-monitoring of symptoms, and asymptomatic COVID-19 testing may aid in the early detection of COVID-19 in HCWs and may help reduce further transmission among HCWs, patients, and families.

Objective: By using wearable sensors, smartphone-based symptom logging, and biospecimens, this project aims to assist HCWs in self-monitoring COVID-19.

Methods: We conducted a prospective, longitudinal study of HCWs at a single institution. The study duration was 1 year, wherein participants were instructed on the continuous use of two wearable sensors (Fitbit Charge 3 smartwatch and TempTraq temperature patches) for up to 30 days. Participants consented to provide biospecimens (ie, nasal swabs, saliva swabs, and blood) for up to 1 year from study entry. Using a smartphone app called Roadmap 2.0, participants entered a daily mood score, submitted daily COVID-19 symptoms, and completed demographic and health-related quality of life surveys at study entry and 30 days later. Semistructured qualitative interviews were also conducted at the end of the 30-day period, following completion of daily mood and symptoms reporting as well as continuous wearable sensor use.

Results: A total of 226 HCWs were enrolled between April 28 and December 7, 2020. The last participant completed the 30-day study procedures on January 16, 2021. Data collection will continue through January 2023, and data analyses are ongoing.

Conclusions: Using wearable sensors, smartphone-based symptom logging and survey completion, and biospecimen collections, this study will potentially provide data on the prevalence of COVID-19 infection among HCWs at a single institution. The study will also assess the feasibility of leveraging wearable sensors and self-monitoring of symptoms in an HCW population.

Trial Registration: ClinicalTrials.gov NCT04756869; https://clinicaltrials.gov/ct2/show/NCT04756869.

International Registered Report Identifier (irrid): DERR1-10.2196/29562.
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http://dx.doi.org/10.2196/29562DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117956PMC
May 2021

Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians.

NPJ Digit Med 2021 Feb 18;4(1):28. Epub 2021 Feb 18.

Department of Neurology, University of Michigan, Ann Arbor, MI, USA.

While 24-h total sleep time (TST) is established as a critical driver of major depression, the relationships between sleep timing and regularity and mental health remain poorly characterized because most studies have relied on either self-report assessments or traditional objective sleep measurements restricted to cross-sectional time frames and small cohorts. To address this gap, we assessed sleep with a wearable device, daily mood with a smartphone application and depression through the 9-item Patient Health Questionnaire (PHQ-9) over the demanding first year of physician training (internship). In 2115 interns, reduced TST (b = -0.11, p < 0.001), later bedtime (b = 0.068, p = 0.015), along with increased variability in TST (b = 0.4, p = 0.0012) and in wake time (b = 0.081, p = 0.005) were associated with more depressive symptoms. Overall, the aggregated impact of sleep variability parameters and of mean sleep parameters on PHQ-9 were similar in magnitude (both r = 0.01). Within individuals, increased TST (b = 0.06, p < 0.001), later wake time (b = 0.09, p < 0.001), earlier bedtime (b = - 0.07, p < 0.001), as well as lower day-to-day shifts in TST (b = -0.011, p < 0.001) and in wake time (b = -0.004, p < 0.001) were associated with improved next-day mood. Variability in sleep parameters substantially impacted mood and depression, similar in magnitude to the mean levels of sleep parameters. Interventions that target sleep consistency, along with sleep duration, hold promise to improve mental health.
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http://dx.doi.org/10.1038/s41746-021-00400-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892862PMC
February 2021

Optimal adjustment of the human circadian clock in the real world.

PLoS Comput Biol 2020 12 28;16(12):e1008445. Epub 2020 Dec 28.

Department of Mathematics, Department of Computational Medicine and Bioinformatics, and Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America.

Which suggestions for behavioral modifications, based on mathematical models, are most likely to be followed in the real world? We address this question in the context of human circadian rhythms. Jet lag is a consequence of the misalignment of the body's internal circadian (~24-hour) clock during an adjustment to a new schedule. Light is the clock's primary synchronizer. Previous research has used mathematical models to compute light schedules that shift the circadian clock to a new time zone as quickly as possible. How users adjust their behavior when provided with these optimal schedules remains an open question. Here, we report data collected by wearables from more than 100 travelers as they cross time zones using a smartphone app, Entrain. We find that people rarely follow the optimal schedules generated through mathematical modeling entirely, but travelers who better followed the optimal schedules reported more positive moods after their trips. Using the data collected, we improve the optimal schedule predictions to accommodate real-world constraints. We also develop a scheduling algorithm that allows for the computation of approximately optimal schedules "on-the-fly" in response to disruptions. User burnout may not be critically important as long as the first parts of a schedule are followed. These results represent a crucial improvement in making the theoretical results of past work viable for practical use and show how theoretical predictions based on known human physiology can be efficiently used in real-world settings.
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http://dx.doi.org/10.1371/journal.pcbi.1008445DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808694PMC
December 2020

Seasonality and light phase-resetting in the mammalian circadian rhythm.

Sci Rep 2020 11 11;10(1):19506. Epub 2020 Nov 11.

Department of Mathematics, University of Michigan, Ann Arbor, 48109, USA.

We study the impact of light on the mammalian circadian system using the theory of phase response curves. Using a recently developed ansatz we derive a low-dimensional macroscopic model for the core circadian clock in mammals. Significantly, the variables and parameters in our model have physiological interpretations and may be compared with experimental results. We focus on the effect of four key factors which help shape the mammalian phase response to light: heterogeneity in the population of oscillators, the structure of the typical light phase response curve, the fraction of oscillators which receive direct light input and changes in the coupling strengths associated with seasonal day-lengths. We find these factors can explain several experimental results and provide insight into the processing of light information in the mammalian circadian system. In particular, we find that the sensitivity of the circadian system to light may be modulated by changes in the relative coupling forces between the light sensing and non-sensing populations. Finally, we show how seasonal day-length, after-effects to light entrainment and seasonal variations in light sensitivity in the mammalian circadian clock are interrelated.
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http://dx.doi.org/10.1038/s41598-020-74002-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658258PMC
November 2020

Gene-set Enrichment with Mathematical Biology (GEMB).

Gigascience 2020 10;9(10)

Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.

Background: Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.

Results: We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199).

Conclusions: Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.
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http://dx.doi.org/10.1093/gigascience/giaa091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546080PMC
October 2020

Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers.

Sleep 2021 02;44(2)

Sleep Disorders and Research Center, Henry Ford Health System, Detroit, MI.

Study Objectives: A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers.

Methods: A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (<10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and an open-source prototype assessment tool are available (www.predictDLMO.com).

Results: Model predictions of DLMO showed good concordance with in-lab DLMO, with Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively.

Conclusion: This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.
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http://dx.doi.org/10.1093/sleep/zsaa180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8240654PMC
February 2021

Macroscopic Models for Human Circadian Rhythms.

J Biol Rhythms 2019 12 16;34(6):658-671. Epub 2019 Oct 16.

Department of Mathematics, University of Michigan, Ann Arbor, Michigan.

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.
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http://dx.doi.org/10.1177/0748730419878298DOI Listing
December 2019

Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device.

Sleep 2019 12;42(12)

Department of Neurology, University of Michigan, Ann Arbor, MI.

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and "clock proxy") to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.
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http://dx.doi.org/10.1093/sleep/zsz180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6930135PMC
December 2019

Macroscopic models for networks of coupled biological oscillators.

Sci Adv 2018 08 3;4(8):e1701047. Epub 2018 Aug 3.

Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.

The study of synchronization of coupled biological oscillators is fundamental to many areas of biology including neuroscience, cardiac dynamics, and circadian rhythms. Mathematical models of these systems may involve hundreds of variables in thousands of individual cells resulting in an extremely high-dimensional description of the system. This often contrasts with the low-dimensional dynamics exhibited on the collective or macroscopic scale for these systems. We introduce a macroscopic reduction for networks of coupled oscillators motivated by an elegant structure we find in experimental measurements of circadian protein expression and several mathematical models for coupled biological oscillators. The observed structure in the collective amplitude of the oscillator population differs from the well-known Ott-Antonsen ansatz, but its emergence can be characterized through a simple argument depending only on general phase-locking behavior in coupled oscillator systems. We further demonstrate its emergence in networks of noisy heterogeneous oscillators with complex network connectivity. Applying this structure, we derive low-dimensional macroscopic models for oscillator population activity. This approach allows for the incorporation of cellular-level experimental data into the macroscopic model whose parameters and variables can then be directly associated with tissue- or organism-level properties, thereby elucidating the core properties driving the collective behavior of the system.
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http://dx.doi.org/10.1126/sciadv.1701047DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6070363PMC
August 2018

CK1δ/ε protein kinase primes the PER2 circadian phosphoswitch.

Proc Natl Acad Sci U S A 2018 06 21;115(23):5986-5991. Epub 2018 May 21.

Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, 169857 Singapore;

Multisite phosphorylation of the PERIOD 2 (PER2) protein is the key step that determines the period of the mammalian circadian clock. Previous studies concluded that an unidentified kinase is required to prime PER2 for subsequent phosphorylation by casein kinase 1 (CK1), an essential clock component that is conserved from algae to humans. These subsequent phosphorylations stabilize PER2, delay its degradation, and lengthen the period of the circadian clock. Here, we perform a comprehensive biochemical and biophysical analysis of mouse PER2 (mPER2) priming phosphorylation and demonstrate, surprisingly, that CK1δ/ε is indeed the priming kinase. We find that both CK1ε and a recently characterized CK1δ2 splice variant more efficiently prime mPER2 for downstream phosphorylation in cells than the well-studied splice variant CK1δ1. While CK1 phosphorylation of PER2 was previously shown to be robust to changes in the cellular environment, our phosphoswitch mathematical model of circadian rhythms shows that the CK1 carboxyl-terminal tail can allow the period of the clock to be sensitive to cellular signaling. These studies implicate the extreme carboxyl terminus of CK1 as a key regulator of circadian timing.
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http://dx.doi.org/10.1073/pnas.1721076115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6003379PMC
June 2018

Testing frameworks for personalizing bipolar disorder.

Transl Psychiatry 2018 02 2;8(1):36. Epub 2018 Feb 2.

Department of Mathematics, University of Michigan, Ann Arbor, MI, 48105, USA.

The hallmark of bipolar disorder is a clinical course of recurrent manic and depressive symptoms of varying severity and duration. Mathematical modeling of bipolar disorder holds the promise of an ability to personalize diagnoses, to predict future mood episodes, to directly compare diverse datasets, and to link basic mechanisms to behavioral data. Several modeling frameworks have been proposed for bipolar disorder, which represent competing hypothesis about the basic framework of the disorder. Here, we test these hypotheses with self-report assessments of mania and depression symptoms from 178 bipolar patients followed prospectively for 4 or more years. Statistical analysis of the data did not support the hypotheses that mood arises from a rhythmic process or multiple stable states (e.g., mania or depression) or that manic and depressive symptoms are highly anti-correlated. Alternatively, it is shown that bipolar disorder could arise from an inability for mood to quickly return to normal when perturbed. This latter concept is embodied by an affective instability model that can be personalized to the clinical course of any individual with chronic disorders that have an affective component.
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http://dx.doi.org/10.1038/s41398-017-0084-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5804032PMC
February 2018

Multiplexing Visual Signals in the Suprachiasmatic Nuclei.

Cell Rep 2017 Nov;21(6):1418-1425

Department of Mathematics, University of Michigan, 2074 East Hall, 530 Church Street, Ann Arbor, MI 48109-1043, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. Electronic address:

The suprachiasmatic nuclei (SCN), the site of the mammalian circadian (daily) pacemaker, contains thousands of interconnected neurons, some of which receive direct retinal input. Here, we study the fast (<1 s) responses of SCN neurons to visual stimuli with a large-scale mathematical model tracking the ionic currents and voltage of all SCN neurons. We reconstruct the SCN network connectivity and reject 99.99% of theoretically possible SCN networks by requiring that the model reproduces experimentally determined receptive fields of SCN neurons. The model shows how the SCN neuronal network can enhance circadian entrainment by sensitizing a population of neurons in the ventral SCN to irradiance. This SCN network also increases the spatial acuity of neurons and increases the accuracy of a simulated subconscious spatial visual task. We hypothesize that much of the fast electrical activity within the SCN is related to the processing of spatial information.
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http://dx.doi.org/10.1016/j.celrep.2017.10.045DOI Listing
November 2017

Guidelines for Genome-Scale Analysis of Biological Rhythms.

J Biol Rhythms 2017 Oct 3;32(5):380-393. Epub 2017 Nov 3.

5 Center for Integrative Genomics, Génopode, University of Lausanne, Lausanne, Switzerland.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding "big data" that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.
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http://dx.doi.org/10.1177/0748730417728663DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5692188PMC
October 2017

Responses to Spatial Contrast in the Mouse Suprachiasmatic Nuclei.

Curr Biol 2017 Jun 18;27(11):1633-1640.e3. Epub 2017 May 18.

Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, UK. Electronic address:

A direct retinal projection targets the suprachiasmatic nucleus (SCN) (an important hypothalamic control center). The accepted function of this projection is to convey information about ambient light (irradiance) to synchronize the SCN's endogenous circadian clock with local time and drive the diurnal variations in physiology and behavior [1-4]. Here, we report that it also renders the SCN responsive to visual images. We map spatial receptive fields (RFs) for SCN neurons and find that only a minority are excited (or inhibited) by light from across the scene as expected for irradiance detectors. The most commonly encountered units have RFs with small excitatory centers, combined with very extensive inhibitory surrounds that reduce their sensitivity to global changes in light in favor of responses to spatial patterns. Other units have larger excitatory RF centers, but these always cover a coherent region of visual space, implying visuotopic order at the single-unit level. Approximately 75% of light-responsive SCN units modulate their firing according to simple spatial patterns (drifting or inverting gratings) without changes in irradiance. The time-averaged firing rate of the SCN is modestly increased under these conditions, but including spatial contrast did not significantly alter the circadian phase resetting efficiency of light. Our data indicate that the SCN contains information about irradiance and spatial patterns. This newly appreciated sensory capacity provides a mechanism by which behavioral and physiological systems downstream of the SCN could respond to visual images [5].
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http://dx.doi.org/10.1016/j.cub.2017.04.039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462621PMC
June 2017

Regulation of persistent sodium currents by glycogen synthase kinase 3 encodes daily rhythms of neuronal excitability.

Nat Commun 2016 11 14;7:13470. Epub 2016 Nov 14.

Department of Psychiatry, University of Alabama at Birmingham, 1720 7th Avenue South, Birmingham, Alabama 35294, USA.

How neurons encode intracellular biochemical signalling cascades into electrical signals is not fully understood. Neurons in the central circadian clock in mammals provide a model system to investigate electrical encoding of biochemical timing signals. Here, using experimental and modelling approaches, we show how the activation of glycogen synthase kinase 3 (GSK3) contributes to neuronal excitability through regulation of the persistent sodium current (I). I exhibits a day/night difference in peak magnitude and is regulated by GSK3. Using mathematical modelling, we predict and confirm that GSK3 activation of I affects the action potential afterhyperpolarization, which increases the spontaneous firing rate without affecting the resting membrane potential. Together, these results demonstrate a crucial link between the molecular circadian clock and electrical activity, providing examples of kinase regulation of electrical activity and the propagation of intracellular signals in neuronal networks.
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http://dx.doi.org/10.1038/ncomms13470DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114562PMC
November 2016

A global quantification of "normal" sleep schedules using smartphone data.

Sci Adv 2016 05 6;2(5):e1501705. Epub 2016 May 6.

Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

The influence of the circadian clock on sleep scheduling has been studied extensively in the laboratory; however, the effects of society on sleep remain largely unquantified. We show how a smartphone app that we have developed, ENTRAIN, accurately collects data on sleep habits around the world. Through mathematical modeling and statistics, we find that social pressures weaken and/or conceal biological drives in the evening, leading individuals to delay their bedtime and shorten their sleep. A country's average bedtime, but not average wake time, predicts sleep duration. We further show that mathematical models based on controlled laboratory experiments predict qualitative trends in sunrise, sunset, and light level; however, these effects are attenuated in the real world around bedtime. Additionally, we find that women schedule more sleep than men and that users reporting that they are typically exposed to outdoor light go to sleep earlier and sleep more than those reporting indoor light. Finally, we find that age is the primary determinant of sleep timing, and that age plays an important role in the variability of population-level sleep habits. This work better defines and personalizes "normal" sleep, produces hypotheses for future testing in the laboratory, and suggests important ways to counteract the global sleep crisis.
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http://dx.doi.org/10.1126/sciadv.1501705DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928979PMC
May 2016

Neural Integration Underlying a Time-Compensated Sun Compass in the Migratory Monarch Butterfly.

Cell Rep 2016 Apr 14;15(4):683-691. Epub 2016 Apr 14.

Department of Neurobiology, University of Massachusetts Medical School, Worcester, MA 01605, USA.

Migrating eastern North American monarch butterflies use a time-compensated sun compass to adjust their flight to the southwest direction. Although the antennal genetic circadian clock and the azimuth of the sun are instrumental for proper function of the compass, it is unclear how these signals are represented on a neuronal level and how they are integrated to produce flight control. To address these questions, we constructed a receptive field model of the compound eye that encodes the solar azimuth. We then derived a neural circuit model that integrates azimuthal and circadian signals to correct flight direction. The model demonstrates an integration mechanism, which produces robust trajectories reaching the southwest regardless of the time of day and includes a configuration for remigration. Comparison of model simulations with flight trajectories of butterflies in a flight simulator shows analogous behaviors and affirms the prediction that midday is the optimal time for migratory flight.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5063661PMC
http://dx.doi.org/10.1016/j.celrep.2016.03.057DOI Listing
April 2016

A Period2 Phosphoswitch Regulates and Temperature Compensates Circadian Period.

Mol Cell 2015 Oct;60(1):77-88

Program in Cancer and Stem Cell Biology, Duke-NUS Graduate Medical School, 8 College Road, Singapore 169857, Singapore; Department of Pediatrics, Duke University Medical Center, Durham, NC 27710, USA. Electronic address:

Period (PER) protein phosphorylation is a critical regulator of circadian period, yet an integrated understanding of the role and interaction between phosphorylation sites that can both increase and decrease PER2 stability remains elusive. Here, we propose a phosphoswitch model, where two competing phosphorylation sites determine whether PER2 has a fast or slow degradation rate. This mathematical model accurately reproduces the three-stage degradation kinetics of endogenous PER2. We predict and demonstrate that the phosphoswitch is intrinsically temperature sensitive, slowing down PER2 degradation as a result of faster reactions at higher temperatures. The phosphoswitch provides a biochemical mechanism for circadian temperature compensation of circadian period. This phosphoswitch additionally explains the phenotype of Familial Advanced Sleep Phase (FASP) and CK1ε(tau) genetic circadian rhythm disorders, metabolic control of PER2 stability, and how drugs that inhibit CK1 alter period. The phosphoswitch provides a general mechanism to integrate diverse stimuli to regulate circadian period.
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http://dx.doi.org/10.1016/j.molcel.2015.08.022DOI Listing
October 2015

Characterizing and modeling the intrinsic light response of rat ganglion-cell photoreceptors.

J Neurophysiol 2015 Nov 23;114(5):2955-66. Epub 2015 Sep 23.

Department of Ophthalmology & Visual Sciences, University of Michigan, Ann Arbor, Michigan; Department of Molecular, Cellular & Developmental Biology, University of Michigan, Ann Arbor, Michigan

Intrinsically photosensitive retinal ganglion cells (ipRGCs) mediate both image-forming vision and non-image-forming visual responses such as pupillary constriction and circadian photoentrainment. Five types of ipRGCs, named M1-M5, have been discovered in rodents. To further investigate their photoresponse properties, we made multielectrode array spike recordings from rat ipRGCs, classified them into M1, M2/M4, and M3/M5 clusters, and measured their intrinsic, melanopsin-based responses to single and flickering light pulses. Results showed that ipRGC spiking can track flickers up to ∼0.2 Hz in frequency and that flicker intervals between 5 and 14 s evoke the most spikes. We also learned that melanopsin's integration time is intensity and cluster dependent. Using these data, we constructed a mathematical model for each cluster's intrinsic photoresponse. We found that the data for the M1 cluster are best fit by a model that assumes a large photoresponse, causing the cell to enter depolarization block. Our models also led us to hypothesize that the M2/M4 and M3/M5 clusters experience comparable photoexcitation but that the M3/M5 cascade decays significantly faster than the M2/M4 cascade, resulting in different response waveforms between these clusters. These mathematical models will help predict how each ipRGC cluster might respond to stimuli of any waveform and could inform the invention of lighting technologies that promote health through melanopsin stimulation.
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http://dx.doi.org/10.1152/jn.00544.2015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737408PMC
November 2015

Collective phase response curves for heterogeneous coupled oscillators.

Phys Rev E Stat Nonlin Soft Matter Phys 2015 Aug 24;92(2):022923. Epub 2015 Aug 24.

Department of Mathematics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Phase response curves (PRCs) have become an indispensable tool in understanding the entrainment and synchronization of biological oscillators. However, biological oscillators are often found in large coupled heterogeneous systems and the variable of physiological importance is the collective rhythm resulting from an aggregation of the individual oscillations. To study this phenomena we consider phase resetting of the collective rhythm for large ensembles of globally coupled Sakaguchi-Kuramoto oscillators. Making use of Ott-Antonsen theory we derive an asymptotically valid analytic formula for the collective PRC. A result of this analysis is a characteristic scaling for the change in the amplitude and entrainment points for the collective PRC compared to the individual oscillator PRC. We support the analytical findings with numerical evidence and demonstrate the applicability of the theory to large ensembles of coupled neuronal oscillators.
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http://dx.doi.org/10.1103/PhysRevE.92.022923DOI Listing
August 2015

Distinct roles for GABA across multiple timescales in mammalian circadian timekeeping.

Proc Natl Acad Sci U S A 2015 Jul 30;112(29):E3911-9. Epub 2015 Jun 30.

Department of Mathematics, University of Michigan, Ann Arbor, MI 48109; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109

The suprachiasmatic nuclei (SCN), the central circadian pacemakers in mammals, comprise a multiscale neuronal system that times daily events. We use recent advances in graphics processing unit computing to generate a multiscale model for the SCN that resolves cellular electrical activity down to the timescale of individual action potentials and the intracellular molecular events that generate circadian rhythms. We use the model to study the role of the neurotransmitter GABA in synchronizing circadian rhythms among individual SCN neurons, a topic of much debate in the circadian community. The model predicts that GABA signaling has two components: phasic (fast) and tonic (slow). Phasic GABA postsynaptic currents are released after action potentials, and can both increase or decrease firing rate, depending on their timing in the interspike interval, a modeling hypothesis we experimentally validate; this allows flexibility in the timing of circadian output signals. Phasic GABA, however, does not significantly affect molecular timekeeping. The tonic GABA signal is released when cells become very excited and depolarized; it changes the excitability of neurons in the network, can shift molecular rhythms, and affects SCN synchrony. We measure which neurons are excited or inhibited by GABA across the day and find GABA-excited neurons are synchronized by-and GABA-inhibited neurons repelled from-this tonic GABA signal, which modulates the synchrony in the SCN provided by other signaling molecules. Our mathematical model also provides an important tool for circadian research, and a model computational system for the many multiscale projects currently studying brain function.
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http://dx.doi.org/10.1073/pnas.1420753112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517259PMC
July 2015

GABA-mediated repulsive coupling between circadian clock neurons in the SCN encodes seasonal time.

Proc Natl Acad Sci U S A 2015 Jul 30;112(29):E3920-9. Epub 2015 Jun 30.

RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Graduate School of Biomedical Sciences, Hiroshima University, Minami, Hiroshima 734-8553, Japan; Japan Science and Technology Agency, Core Research for Evolutional Science and Technology, Chiyoda-ku, Tokyo 102-0076, Japan

The mammalian suprachiasmatic nucleus (SCN) forms not only the master circadian clock but also a seasonal clock. This neural network of ∼10,000 circadian oscillators encodes season-dependent day-length changes through a largely unknown mechanism. We show that region-intrinsic changes in the SCN fine-tune the degree of network synchrony and reorganize the phase relationship among circadian oscillators to represent day length. We measure oscillations of the clock gene Bmal1, at single-cell and regional levels in cultured SCN explanted from animals raised under short or long days. Coupling estimation using the Kuramoto framework reveals that the network has couplings that can be both phase-attractive (synchronizing) and -repulsive (desynchronizing). The phase gap between the dorsal and ventral regions increases and the overall period of the SCN shortens with longer day length. We find that one of the underlying physiological mechanisms is the modulation of the intracellular chloride concentration, which can adjust the strength and polarity of the ionotropic GABAA-mediated synaptic input. We show that increasing day-length changes the pattern of chloride transporter expression, yielding more excitatory GABA synaptic input, and that blocking GABAA signaling or the chloride transporter disrupts the unique phase and period organization induced by the day length. We test the consequences of this tunable GABA coupling in the context of excitation-inhibition balance through detailed realistic modeling. These results indicate that the network encoding of seasonal time is controlled by modulation of intracellular chloride, which determines the phase relationship among and period difference between the dorsal and ventral SCN.
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http://dx.doi.org/10.1073/pnas.1421200112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517217PMC
July 2015
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