A mathematical model of the Drosophila circadian clock with emphasis on posttranslational mechanisms

Leise, Tanya; Moin, Emily E.

doi:10.1016/j.jtbi.2007.04.013

Cited by 18 publications

(18 citation statements)

References 41 publications

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“…The model presented by Kuczenski et al (2007) does not address circadian phase dynamics or light-induced phase response. The model presented by Leise and Moin (2007) does not include appropriate light-entrained dynamics; our simulations of their model subject to regular 12-h light/dark cycles show 48-h harmonic effects. The model we found that most accurately depicts phase dynamics is presented by Smolen et al (2004) and incorporates all 5 clock components.…”

Section: Discussionmentioning

confidence: 91%

Modeling the Drosophila melanogaster Circadian Oscillator via Phase Optimization

et al. 2008

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The circadian clock, which coordinates daily physiological behaviors of most organisms, maintains endogenous (approximately 24 h) cycles and simultaneously synchronizes to the 24-h environment due to its inherent robustness to environmental perturbations coupled with a sensitivity to specific environmental stimuli. In this study, the authors develop a detailed mathematical model that characterizes the Drosophila melanogaster circadian network. This model incorporates the transcriptional regulation of period, time-less, vrille, PAR-domain protein 1, and clock gene and protein counterparts. The interlocked positive and negative feedback loops that arise from these clock components are described primarily through mass-action kinetics (with the exception of regulated gene expression) and without the use of explicit time delays. System parameters are estimated via a genetic algorithm-based optimization of a cost function that relies specifically on circadian phase behavior since amplitude measurements are often noisy and do not account for the unique entrainment features that define circadian oscillations. Resulting simulations of this 29-state ordinary differential equation model comply with fitted wild-type experimental data, demonstrating accurate free-running (23.24-h periodic) and entrained (24-h periodic) circadian dynamics. This model also predicts unfitted mutant phenotype behavior by illustrating short and long periodicity, robust oscillations, and arrhythmicity. This mechanistic model also predicts light-induced circadian phase resetting (as described by the phase-response curve) that are in line with experimental observations.

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Section: Discussionmentioning

confidence: 91%

Modeling the Drosophila melanogaster Circadian Oscillator via Phase Optimization

et al. 2008

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“…2007; F athallah -S haykh et al . 2009), and one did both (L eise and M oin 2007). In addition, one study re-used parameters from one of the three studies mentioned above.…”

Section: Resultsmentioning

confidence: 99%

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

Scheuer

Hanlon

Dresel

et al. 2017

Preprint

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General Article SummarySubstantial biological model curation was essential for identifying differences in peak 23 time variance of the clock-proteins 'PERIOD' and 'TIMELESS', which probably stem 24 from differences in phosphorylation-network complexity. 25 We repeatedly encountered systemic limitations of contemporary data analysis 26 strategies in our work on circadian clocks. Thus, we used it as an opportunity for 27 composing a panoramic view of the broader challenges in biological model curation, 28 which are likely to increase as biologists aim to integrate all existing expertise in order to 29 address diverse grand challenges. We developed and tested a trans-disciplinary 30 research workflow, which enables biologists and compiler-architects to define biology-31 friendly compilers for efficiently constructing and maintaining Versioned Biological 32 Information Resources (VBIRs). We report insights gleaned from our practical clock 33 research that are essential for defining a VBIRs infrastructure, which improves the 34 efficiency of biological model curation to the point where it can be democratized. . CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It ...

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“…The parameters in these models have a physical interpretation: they denote synthesis or degradation rates, binding affinities, transport rates, etc. Detailed models for circadian clocks are now available for mammals [47][48][49][50], Neurospora [51,52], Drosophila [53][54][55][56][57], and Arabidopsis [58][59][60][61].…”

Section: From Scheme To Equationsmentioning

confidence: 99%

Modeling circadian clocks: From equations to oscillations

Gonze¹

2011

Open Life Sciences

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Circadian rhythms are generated at the cellular level by a small but tightly regulated genetic network. In higher eukaryotes, interlocked transcriptional-translational feedback loops form the core of this network, which ensures the activation of the right genes (proteins) at the right time of the day. Understanding how such a complex molecular network can generate robust, self-sustained oscillations and accurately responds to signals from the environment (such as light and temperature) is greatly helped by mathematical modeling. In the present paper we review some mathematical models for circadian clocks, ranging from abstract, phenomenological models to the most detailed molecular models. We explain how the equations are derived, highlighting the challenges for the modelers, and how the models are analyzed. We show how to compute bifurcation diagrams, entrainment, and phase response curves. In the subsequent paper, we discuss, through a selection of examples, how modeling efforts have contributed to a better understanding of the dynamics of the circadian regulatory network.

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A mathematical model of the Drosophila circadian clock with emphasis on posttranslational mechanisms

Cited by 18 publications

References 41 publications

Modeling the Drosophila melanogaster Circadian Oscillator via Phase Optimization

Modeling the Drosophila melanogaster Circadian Oscillator via Phase Optimization

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

Modeling circadian clocks: From equations to oscillations

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