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Del Olmo M, Legewie S, Brunner M, Höfer T, Kramer A, Blüthgen N, Herzel H. Network switches and their role in circadian clocks. J Biol Chem 2024; 300:107220. [PMID: 38522517 PMCID: PMC11044057 DOI: 10.1016/j.jbc.2024.107220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 03/07/2024] [Accepted: 03/18/2024] [Indexed: 03/26/2024] Open
Abstract
Circadian rhythms are generated by complex interactions among genes and proteins. Self-sustained ∼24 h oscillations require negative feedback loops and sufficiently strong nonlinearities that are the product of molecular and network switches. Here, we review common mechanisms to obtain switch-like behavior, including cooperativity, antagonistic enzymes, multisite phosphorylation, positive feedback, and sequestration. We discuss how network switches play a crucial role as essential components in cellular circadian clocks, serving as integral parts of transcription-translation feedback loops that form the basis of circadian rhythm generation. The design principles of network switches and circadian clocks are illustrated by representative mathematical models that include bistable systems and negative feedback loops combined with Hill functions. This work underscores the importance of negative feedback loops and network switches as essential design principles for biological oscillations, emphasizing how an understanding of theoretical concepts can provide insights into the mechanisms generating biological rhythms.
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Affiliation(s)
- Marta Del Olmo
- Institute for Theoretical Biology, Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Berlin, Germany.
| | - Stefan Legewie
- Department of Systems Biology, Institute for Biomedical Genetics (IBMG), University of Stuttgart, Stuttgart, Germany; Stuttgart Research Center for Systems Biology (SRCSB), University of Stuttgart, Stuttgart, Germany
| | - Michael Brunner
- Biochemistry Center, Universität Heidelberg, Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Universität Heidelberg, Heidelberg, Germany
| | - Achim Kramer
- Laboratory of Chronobiology, Institute for Medical Immunology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Nils Blüthgen
- Institute for Theoretical Biology, Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Berlin, Germany; Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Hanspeter Herzel
- Institute for Theoretical Biology, Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Berlin, Germany.
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Del Olmo M, Kalashnikov A, Schmal C, Kramer A, Herzel H. Coupling allows robust mammalian redox circadian rhythms despite heterogeneity and noise. Heliyon 2024; 10:e24773. [PMID: 38312577 PMCID: PMC10835301 DOI: 10.1016/j.heliyon.2024.e24773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 12/06/2023] [Accepted: 01/14/2024] [Indexed: 02/06/2024] Open
Abstract
Circadian clocks are endogenous oscillators present in almost all cells that drive daily rhythms in physiology and behavior. There are two mechanisms that have been proposed to explain how circadian rhythms are generated in mammalian cells: through a transcription-translation feedback loop (TTFL) and based on oxidation/reduction reactions, both of which are intrinsically stochastic and heterogeneous at the single cell level. In order to explore the emerging properties of stochastic and heterogeneous redox oscillators, we simplify a recently developed kinetic model of redox oscillations to an amplitude-phase oscillator with 'twist' (period-amplitude correlation) and subject to Gaussian noise. We show that noise and heterogeneity alone lead to fast desynchronization, and that coupling between noisy oscillators can establish robust and synchronized rhythms with amplitude expansions and tuning of the period due to twist. Coupling a network of redox oscillators to a simple model of the TTFL also contributes to synchronization, large amplitudes and fine-tuning of the period for appropriate interaction strengths. These results provide insights into how the circadian clock compensates randomness from intracellular sources and highlight the importance of noise, heterogeneity and coupling in the context of circadian oscillators.
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Affiliation(s)
- Marta Del Olmo
- Institute for Theoretical Biology - Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Philippstraße 13, 10115 Berlin, Germany
| | - Anton Kalashnikov
- Institute for Theoretical Biology - Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Philippstraße 13, 10115 Berlin, Germany
| | - Christoph Schmal
- Institute for Theoretical Biology - Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Philippstraße 13, 10115 Berlin, Germany
| | - Achim Kramer
- Institute for Medical Immunology - Laboratory of Chronobiology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Hanspeter Herzel
- Institute for Theoretical Biology - Humboldt Universität zu Berlin and Charité Universitätsmedizin Berlin, Philippstraße 13, 10115 Berlin, Germany
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Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. Bioinformatics 2023; 39:btad602. [PMID: 37769241 PMCID: PMC10576164 DOI: 10.1093/bioinformatics/btad602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 09/30/2023]
Abstract
MOTIVATION Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms (for instance in gene expression, eclosion, egg-laying, and feeding) tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets, and are also limited by their use of P-values in detecting oscillations. RESULTS We introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process regression and Bayesian inference to incorporate measurement errors, non-uniformly sampled data, and a recently developed non-stationary kernel to improve detection of oscillations. By using Bayes factors, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses, thus providing an advantage over P-values. Using synthetic datasets, we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary symmetric oscillations. Next, by analyzing existing qPCR datasets, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak and noisy oscillations. Finally, we generate new qPCR data on mouse embryonic stem cells. Surprisingly, we discover using ODeGP that increasing cell-density results in rapid generation of oscillations in the Bmal1 gene, thus highlighting our method's ability to discover unexpected and new patterns. In its current implementation, ODeGP is meant only for analyzing single or a few time-trajectories, not genome-wide datasets. AVAILABILITY AND IMPLEMENTATION ODeGP is available at https://github.com/Shaonlab/ODeGP.
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Affiliation(s)
- Shabnam Sahay
- Department of Computer Science, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, Karnataka 560065, India
| | - Shishir Adhikari
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, United States
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, United States
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, United States
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, United States
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Shaon Chakrabarti
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, Karnataka 560065, India
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Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. bioRxiv 2023:2023.03.21.533651. [PMID: 36993318 PMCID: PMC10055182 DOI: 10.1101/2023.03.21.533651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method, ODeGP ( O scillation De tection using G aussian P rocesses), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.
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Affiliation(s)
- Shabnam Sahay
- Department of Computer Science, Indian Institute of Technology Bombay
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore
| | - Shishir Adhikari
- Department of Systems Biology, Harvard Medical School, Boston
- Department of Data Science, Dana-Farber Cancer Institute, Boston
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston
- Department of Data Science, Dana-Farber Cancer Institute, Boston
- Broad Institute of MIT and Harvard, Cambridge
| | - Shaon Chakrabarti
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore
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Bartholomai BM, Gladfelter AS, Loros JJ, Dunlap JC. PRD-2 mediates clock-regulated perinuclear localization of clock gene RNAs within the circadian cycle of Neurospora. Proc Natl Acad Sci U S A 2022; 119:e2203078119. [PMID: 35881801 PMCID: PMC9351534 DOI: 10.1073/pnas.2203078119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 06/24/2022] [Indexed: 02/02/2023] Open
Abstract
The transcription-translation negative feedback loops underlying animal and fungal circadian clocks are remarkably similar in their molecular regulatory architecture and, although much is understood about their central mechanism, little is known about the spatiotemporal dynamics of the gene products involved. A common feature of these circadian oscillators is a significant temporal delay between rhythmic accumulation of clock messenger RNAs (mRNAs) encoding negative arm proteins, for example, frq in Neurospora and Per1-3 in mammals, and the appearance of the clock protein complexes assembled from the proteins they encode. Here, we report use of single-molecule RNA fluorescence in situ hybridization (smFISH) to show that the fraction of nuclei actively transcribing the clock gene frq changes in a circadian manner, and that these mRNAs cycle in abundance with fewer than five transcripts per nucleus at any time. Spatial point patterning statistics reveal that frq is spatially clustered near nuclei in a time of day-dependent manner and that clustering requires an RNA-binding protein, PRD-2 (PERIOD-2), recently shown also to bind to mRNA encoding another core clock component, casein kinase 1. An intrinsically disordered protein, PRD-2 displays behavior in vivo and in vitro consistent with participation in biomolecular condensates. These data are consistent with a role for phase-separating RNA-binding proteins in spatiotemporally organizing clock mRNAs to facilitate local translation and assembly of clock protein complexes.
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Affiliation(s)
- Bradley M. Bartholomai
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755
| | - Amy S. Gladfelter
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Jennifer J. Loros
- Department of Biochemistry and Cell Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755
| | - Jay C. Dunlap
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755
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Sukys A, Öcal K, Grima R. Approximating Solutions of the Chemical Master Equation using Neural Networks. iScience 2022; 25:105010. [PMID: 36117994 PMCID: PMC9474291 DOI: 10.1016/j.isci.2022.105010] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/13/2022] [Accepted: 08/18/2022] [Indexed: 10/27/2022] Open
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Öcal K, Gutmann MU, Sanguinetti G, Grima R. Inference and uncertainty quantification of stochastic gene expression via synthetic models. J R Soc Interface 2022; 19:20220153. [PMID: 35858045 PMCID: PMC9277240 DOI: 10.1098/rsif.2022.0153] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
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Affiliation(s)
- Kaan Öcal
- School of Informatics, University of Edinburgh, Edinburgh EH9 3JH, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
| | - Michael U Gutmann
- School of Informatics, University of Edinburgh, Edinburgh EH9 3JH, UK
| | - Guido Sanguinetti
- Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy
| | - Ramon Grima
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK
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Breitenbach T, Helfrich-Förster C, Dandekar T. An effective model of endogenous clocks and external stimuli determining circadian rhythms. Sci Rep 2021; 11:16165. [PMID: 34373483 PMCID: PMC8352901 DOI: 10.1038/s41598-021-95391-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/23/2021] [Indexed: 02/07/2023] Open
Abstract
Circadian endogenous clocks of eukaryotic organisms are an established and rapidly developing research field. To investigate and simulate in an effective model the effect of external stimuli on such clocks and their components we developed a software framework for download and simulation. The application is useful to understand the different involved effects in a mathematical simple and effective model. This concerns the effects of Zeitgebers, feedback loops and further modifying components. We start from a known mathematical oscillator model, which is based on experimental molecular findings. This is extended with an effective framework that includes the impact of external stimuli on the circadian oscillations including high dose pharmacological treatment. In particular, the external stimuli framework defines a systematic procedure by input-output-interfaces to couple different oscillators. The framework is validated by providing phase response curves and ranges of entrainment. Furthermore, Aschoffs rule is computationally investigated. It is shown how the external stimuli framework can be used to study biological effects like points of singularity or oscillators integrating different signals at once. The mathematical framework and formalism is generic and allows to study in general the effect of external stimuli on oscillators and other biological processes. For an easy replication of each numerical experiment presented in this work and an easy implementation of the framework the corresponding Mathematica files are fully made available. They can be downloaded at the following link: https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/circadian/ .
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Affiliation(s)
- Tim Breitenbach
- grid.8379.50000 0001 1958 8658Institut für Mathematik, Universität Würzburg, Emil-Fischer-Strasse 30, 97074 Würzburg, Germany
| | | | - Thomas Dandekar
- grid.8379.50000 0001 1958 8658Biozentrum, Universität Würzburg, Am Hubland, 97074 Würzburg, Germany
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