Asymmetry in Signal Oscillations Contributes to Efficiency of Periodic Systems

Bae, Seul-A; Acevedo, Alison; Androulakis, Ioannis P.

doi:10.1615/critrevbiomedeng.2017019658

Cited by 3 publications

(2 citation statements)

References 65 publications

(104 reference statements)

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“…Therefore, self‐sustained oscillators, as a fundamental building block, introduce great flexibility and plasticity to a biological system. Furthermore, oscillating systems were shown, computationally, to achieve better resource utilization in a resource‐constrained environment (Bae, Acevedo, & Androulakis, 2016), whereas oscillations were also shown to optimize response to acute disturbances. Therefore, rhythmic homeostatic dynamics contributed to the development of emergent systemic properties.…”

Section: Insights From Mathematical Modelsmentioning

confidence: 99%

Circadian rhythms and the HPA axis: A systems view

Androulakis

2021

WIREs Mechanisms of Disease

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The circadian timing system comprises a network of time‐keeping clocks distributed across a living host whose responsibility is to allocate resources and distribute functions temporally to optimize fitness. The molecular structures generating these rhythms have evolved to accommodate the rotation of the earth in an attempt to primarily match the light/dark periods during the 24‐hr day. To maintain synchrony of timing across and within tissues, information from the central clock, located in the suprachiasmatic nucleus, is conveyed using systemic signals. Leading among those signals are endocrine hormones, and while the hypothalamic–pituitary–adrenal axis through the release of glucocorticoids is a major pacesetter. Interestingly, the fundamental units at the molecular and physiological scales that generate local and systemic signals share critical structural properties. These properties enable time‐keeping systems to generate rhythmic signals and allow them to adopt specific properties as they interact with each other and the external environment. The purpose of this review is to provide a broad overview of these structures, discuss their functional characteristics, and describe some of their fundamental properties as these related to health and disease. This article is categorized under: Immune System Diseases > Computational Models Immune System Diseases > Biomedical Engineering

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Section: Insights From Mathematical Modelsmentioning

confidence: 99%

Circadian rhythms and the HPA axis: A systems view

Androulakis

2021

WIREs Mechanisms of Disease

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“…Oscillatory behavior is critical to many life sustaining processes. Examples include: cell cycles [ 1 ], circadian rhythms [ 2 ], notch signaling in the development of the nervous system [ 3 ], tissue development [ 4 ], gene transcription [ 5 ], and efficient signaling [ 6 ]. Biological oscillators are also important elements in building applications in synthetic biology [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

An oscillating reaction network with an exact closed form solution in the time domain

Hellerstein

2023

BMC Bioinformatics

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Background Oscillatory behavior is critical to many life sustaining processes such as cell cycles, circadian rhythms, and notch signaling. Important biological functions depend on the characteristics of these oscillations (hereafter, oscillation characteristics or OCs): frequency (e.g., event timings), amplitude (e.g., signal strength), and phase (e.g., event sequencing). Numerous oscillating reaction networks have been documented or proposed. Some investigators claim that oscillations in reaction networks require nonlinear dynamics in that at least one rate law is a nonlinear function of species concentrations. No one has shown that oscillations can be produced for a reaction network with linear dynamics. Further, no one has obtained closed form solutions for the frequency, amplitude and phase of any oscillating reaction network. Finally, no one has published an algorithm for constructing oscillating reaction networks with desired OCs. Results This is a theoretical study that analyzes reaction networks in terms of their representation as systems of ordinary differential equations. Our contributions are: (a) construction of an oscillating, two species reaction network [two species harmonic oscillator (2SHO)] that has no nonlinearity; (b) obtaining closed form formulas that calculate frequency, amplitude, and phase in terms of the parameters of the 2SHO reaction network, something that has not been done for any published oscillating reaction network; and (c) development of an algorithm that parameterizes the 2SHO to achieve desired oscillation, a capability that has not been produced for any published oscillating reaction network. Conclusions Our 2SHO demonstrates the feasibility of creating an oscillating reaction network whose dynamics are described by a system of linear differential equations. Because it is a linear system, we can derive closed form expressions for the frequency, amplitude, and phase of oscillations, something that has not been done for other published reaction networks. With these formulas, we can design 2SHO reaction networks to have desired oscillation characteristics. Finally, our sensitivity analysis suggests an approach to constructing a 2SHO for a biochemical system.

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