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Academic literature on the topic 'Biomolecular oscillators'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Biomolecular oscillators"

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Agrawal, Deepak K., Elisa Franco, and Rebecca Schulman. "A self-regulating biomolecular comparator for processing oscillatory signals." Journal of The Royal Society Interface 12, no. 111 (October 2015): 20150586. http://dx.doi.org/10.1098/rsif.2015.0586.

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While many cellular processes are driven by biomolecular oscillators, precise control of a downstream on/off process by a biochemical oscillator signal can be difficult: over an oscillator's period, its output signal varies continuously between its amplitude limits and spends a significant fraction of the time at intermediate values between these limits. Further, the oscillator's output is often noisy, with particularly large variations in the amplitude. In electronic systems, an oscillating signal is generally processed by a downstream device such as a comparator that converts a potentially noisy oscillatory input into a square wave output that is predominantly in one of two well-defined on and off states. The comparator's output then controls downstream processes. We describe a method for constructing a synthetic biochemical device that likewise produces a square-wave-type biomolecular output for a variety of oscillatory inputs. The method relies on a separation of time scales between the slow rate of production of an oscillatory signal molecule and the fast rates of intermolecular binding and conformational changes. We show how to control the characteristics of the output by varying the concentrations of the species and the reaction rates. We then use this control to show how our approach could be applied to process different in vitro and in vivo biomolecular oscillators, including the p53-Mdm2 transcriptional oscillator and two types of in vitro transcriptional oscillators. These results demonstrate how modular biomolecular circuits could, in principle, be combined to build complex dynamical systems. The simplicity of our approach also suggests that natural molecular circuits may process some biomolecular oscillator outputs before they are applied downstream.

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Bokka, Venkat, Abhishek Dey, and Shaunak Sen. "Period–amplitude co-variation in biomolecular oscillators." IET Systems Biology 12, no. 4 (August 1, 2018): 190–98. http://dx.doi.org/10.1049/iet-syb.2018.0015.

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Banerjee, Soumyadip, Venkat Bokka, and Shaunak Sen. "Attenuation of Pulse Disturbances in Biomolecular Oscillators." IFAC-PapersOnLine 51, no. 1 (2018): 301–6. http://dx.doi.org/10.1016/j.ifacol.2018.05.031.

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Koeppl, H., M. Hafner, A. Ganguly, and A. Mehrotra. "Deterministic characterization of phase noise in biomolecular oscillators." Physical Biology 8, no. 5 (August 10, 2011): 055008. http://dx.doi.org/10.1088/1478-3975/8/5/055008.

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Zhou, Peipei, Shuiming Cai, Zengrong Liu, Luonan Chen, and Ruiqi Wang. "Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems." Chaos, Solitons & Fractals 50 (May 2013): 115–26. http://dx.doi.org/10.1016/j.chaos.2012.11.011.

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Tassinari, Riccardo, Claudia Cavallini, Elena Olivi, Federica Facchin, Valentina Taglioli, Chiara Zannini, Martina Marcuzzi, and Carlo Ventura. "Cell Responsiveness to Physical Energies: Paving the Way to Decipher a Morphogenetic Code." International Journal of Molecular Sciences 23, no. 6 (March 15, 2022): 3157. http://dx.doi.org/10.3390/ijms23063157.

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We discuss emerging views on the complexity of signals controlling the onset of biological shapes and functions, from the nanoarchitectonics arising from supramolecular interactions, to the cellular/multicellular tissue level, and up to the unfolding of complex anatomy. We highlight the fundamental role of physical forces in cellular decisions, stressing the intriguing similarities in early morphogenesis, tissue regeneration, and oncogenic drift. Compelling evidence is presented, showing that biological patterns are strongly embedded in the vibrational nature of the physical energies that permeate the entire universe. We describe biological dynamics as informational processes at which physics and chemistry converge, with nanomechanical motions, and electromagnetic waves, including light, forming an ensemble of vibrations, acting as a sort of control software for molecular patterning. Biomolecular recognition is approached within the establishment of coherent synchronizations among signaling players, whose physical nature can be equated to oscillators tending to the coherent synchronization of their vibrational modes. Cytoskeletal elements are now emerging as senders and receivers of physical signals, “shaping” biological identity from the cellular to the tissue/organ levels. We finally discuss the perspective of exploiting the diffusive features of physical energies to afford in situ stem/somatic cell reprogramming, and tissue regeneration, without stem cell transplantation.

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Iacobelli, Peter. "Circadian dysregulation and Alzheimer’s disease: A comprehensive review." Brain Science Advances 8, no. 4 (November 30, 2022): 221–57. http://dx.doi.org/10.26599/bsa.2022.9050021.

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Alzheimer’s disease (AD), the foremost variant of dementia, has been associated with a menagerie of risk factors, many of which are considered to be modifiable. Among these modifiable risk factors is circadian rhythm, the chronobiological system that regulates sleep‐wake cycles, food consumption timing, hydration timing, and immune responses amongst many other necessary physiological processes. Circadian rhythm at the level of the suprachiasmatic nucleus (SCN), is tightly regulated in the human body by a host of biomolecular substances, principally the hormones melatonin, cortisol, and serotonin. In addition, photic information projected along afferent pathways to the SCN and peripheral oscillators regulates the synthesis of these hormones and mediates the manner in which they act on the SCN and its substructures. Dysregulation of this cycle, whether induced by environmental changes involving irregular exposure to light, or through endogenous pathology, will have a negative impact on immune system optimization and will heighten the deposition of Aβ and the hyperphosphorylation of the tau protein. Given these correlations, it appears that there is a physiologic association between circadian rhythm dysregulation and AD. This review will explore the physiology of circadian dysregulation in the AD brain, and will propose a basic model for its role in AD‐typical pathology, derived from the literature compiled and referenced throughout.

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Takinoue, Masahiro, Daisuke Kiga, Koh-ichiroh Shohda, and Akira Suyama. "RNA Oscillator: Limit Cycle Oscillations based on Artificial Biomolecular Reactions." New Generation Computing 27, no. 2 (February 2009): 107–27. http://dx.doi.org/10.1007/s00354-008-0057-5.

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Banerjee, Soumyadip, and Shaunak Sen. "Robustness of a biomolecular oscillator to pulse perturbations." IET Systems Biology 14, no. 3 (June 1, 2020): 127–32. http://dx.doi.org/10.1049/iet-syb.2019.0029.

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Poznanski, Roman, Eda Alemdar, Cacha Lleuvelyn, Valeriy Sbitnev, and Erkki Brandas. "Journal of Multiscale Neuroscience." Journal of Multiscale Neuroscience 1, no. 2 (October 28, 2022): 109–33. http://dx.doi.org/10.56280/1546792195.

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information based on an inter-cerebral superfast, spontaneous information pathway involving protein-protein interactions. Protons are convenient quantum objects for transferring bit units in a complex water medium like the brain. The phonon-polariton interaction in such a medium adds informational complexity involving complex protein interactions that are essential for the superfluid-like highway to enable the consciousness process to penetrate brain regions due to different regulated gene sets as opposed to single region-specific genes. Protein pathways in the cerebral cortices are connected in a single network of thousands of proteins. To understand the role of inter-cerebral communication, we postulate protonic currents in interfacial water crystal lattices result from phonon-polariton vibrations, which can lead in the presence of an electromagnetic field, to ultra-rapid communication where thermo-qubits, physical feelings, and protons that are convenient quantum objects for transferring bit units in a complex water medium. The relative equality between the frequencies of thermal oscillations due to the energy of the quasi-protonic movement about a closed loop and the frequencies of electromagnetic oscillations confirms the existence of quasi-polaritons. Phonon-polaritons are electromagnetic waves coupled to lattice vibrational modes. Still, when generated specifically by protons, they are referred to as phonon-coupled quasi-particles, i.e., providing a coupling with vibrational motions. We start from quasiparticles and move up the scale to biomolecular communication in subcellular, cellular and neuronal structures, leading to the negentropic entanglement of multiscale ‘bits’ of information. Espousing quantum potential chemistry, the interdependence of intrinsic information on the negative gain in the steady-state represents the mesoscopic aggregate of the microscopic random quantum-thermal fluctuations expressed through a negentropically derived, temperature-dependent, dissipative quantum potential energy. The latter depends on the time derivative of the spread function and temperature, which fundamentally explains the holonomic brain theory.

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Books on the topic "Biomolecular oscillators"

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Chen, Luonan. Modeling biomolecular networks in cells: Structures and dynamics. London: Springer, 2010.

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Book chapters on the topic "Biomolecular oscillators"

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Baker, C. M., E. Darian, and A. D. MacKerell Jr. "Chapter 3. Towards Biomolecular Simulations with Explicit Inclusion of Polarizability: Development of a CHARMM Polarizable Force Field based on the Classical Drude Oscillator Model." In RSC Biomolecular Sciences, 23–50. Cambridge: Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849735049-00023.

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Doster, W. "Brownian Oscillator Analysis of Molecular Motions in Biomolecules." In Neutron Scattering in Biology, 461–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-29111-3_20.

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Del Vecchio, Domitilla, and Richard M. Murray. "Analysis of Dynamic Behavior." In Biomolecular Feedback Systems. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691161532.003.0003.

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This chapter turns to some of the tools from dynamical systems and feedback control theory that will be used in the rest of the text to analyze and design biological circuits. It first models the dynamics of a system using the input/output modeling formalism described in Chapter 1 and then studies the “robustness” of the system of a given function of the circuit. The chapter then discusses some of the underlying ideas for how to model biological oscillatory behavior, focusing on those types of oscillations that are most common in biomolecular systems. Hereafter, the chapter explores how the location of equilibrium points, their stability, their regions of attraction, and other dynamic phenomena vary based on the values of the parameters in a model. Finally, methods for reducing the complexity of the models that are introduced in this chapter are reviewed.

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Murray, Richard M. "Biological Circuit Components." In Biomolecular Feedback Systems. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691161532.003.0005.

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This chapter describes some simple circuit components that have been constructed in E. coli cells using the technology of synthetic biology and then considers a more complicated circuit that already appears in natural systems to implement adaptation. It first analyzes the negatively autoregulated gene fabricated in E. coli bacteria, before turning to the toggle switch, which is composed of two genes that mutually repress each other. The chapter next illustrates a dynamical model of a “repressilator”—an oscillatory genetic circuit consisting of three repressors arranged in a ring fashion. The activator–repressor clock is then considered, alongside an incoherent feedforward loop (IFFL). Finally, the chapter examines bacterial chemotaxis, which E. coli use to move in the direction of increasing nutrients.

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Chikishev, A. Yu, A. V. Netrebko, and Yu M. Romanovsky. "On the damping of cluster oscillations in protein molecules." In Stochastic Dynamics of Reacting Biomolecules, 263–83. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812795434_0009.

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"Explicit Inclusion of Induced Polarization in Atomistic Force Fields Based on the Classical Drude Oscillator Model." In Many-Body Effects and Electrostatics in Biomolecules, 209–50. Jenny Stanford Publishing, 2016. http://dx.doi.org/10.1201/b21343-10.

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Savelyev, Alexey, Benoît Roux, and Alexander MacKerell. "Explicit Inclusion of Induced Polarization in Atomistic Force Fields Based on the Classical Drude Oscillator Model." In Many-Body Effects and Electrostatics in Biomolecules, 191–232. Pan Stanford, 2016. http://dx.doi.org/10.1201/b21343-9.

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Conference papers on the topic "Biomolecular oscillators"

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Samaniego, Christian Cuba, Elisa Franco, and Giulia Giordano. "Design and analysis of a biomolecular positive-feedback oscillator." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619738.

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Dey, Supravat, and Abhyudai Singh. "Genomic decoy sites enhance the oscillatory regime of a biomolecular clock." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147998.

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Bastatas, Lyndon D., Jinky B. Bornales, Christopher C. Bernido, and M. Victoria Carpio-Bernido. "White Noise Path Integral Treatment of a Two-dimensional Dirac Oscillator in a Uniform Magnetic Field." In STOCHASTIC AND QUANTUM DYNAMICS OF BIOMOLECULAR SYSTEMS: Proceedings of the 5th Jagna International Workshop. AIP, 2008. http://dx.doi.org/10.1063/1.2956803.

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Poursina, Mohammad, Kishor Bhalerao, and Kurt Anderson. "Divide-and-Conquer Based Adaptive Coarse Grained Simulation of RNA." In ASME 2010 First Global Congress on NanoEngineering for Medicine and Biology. ASMEDC, 2010. http://dx.doi.org/10.1115/nemb2010-13123.

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Molecular modeling has gained increasing importance in recent years for predicting important structural properties of large biomolecular systems such as RNA which play a critical role in various biological processes. Given the complexity of biopolymers and their interactions within living organisms, efficient and adaptive multi-scale modeling approaches are necessary if one is to reasonably perform computational studies of interest. These studies nominally involve multiple important physical phenomena occurring at different spatial and temporal scales. These systems are typically characterized by large number of degrees of freedom O(103) – O(107). The temporal domains range from sub-femto seconds (O(10−16)) associated with the small high frequency oscillations of individual tightly bonded atoms to milliseconds (O(10−3)) or greater for the larger scale conformational motion. The traditional approach for molecular modeling involved fully atomistic models which results in fully decoupled equations of motion. The problems with this approach are well documented in literature.

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Yeo, Leslie Y., and James R. Friend. "Surface Acoustic Waves: A New Paradigm for Driving Ultrafast Biomicrofluidics." In ASME 2009 Second International Conference on Micro/Nanoscale Heat and Mass Transfer. ASMEDC, 2009. http://dx.doi.org/10.1115/mnhmt2009-18517.

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Surface acoustic waves (SAWs), which are 10 MHz order surface waves roughly 10 nm in amplitude propagating on the surface of a piezoelectric substrate, can offer a powerful method for driving fast microfluidic actuation and microparticle or biomolecule manipulation. We demonstrate that sessile drops can be linearly translated on planar substrates or fluid can be pumped through microchannels at typically one to two orders of magnitude faster than that achievable through current microfluidic technologies. Micromixing can be induced in the same microchannel in which fluid is pumped using the SAW simply by changing the SAW frequency to superimpose a chaotic oscillatory flow onto the uniform through flow. Strong inertial microcentrifugation for micromixing and particle concentration or separation can also be induced via symmetry-breaking. At low SAW amplitudes below that at which flow commences, the transverse standing wave that arises across the microchannel afford particle aggregation and hence sorting on nodal lines. Other microfluidic manipulations are also possible with the SAW. For example, capillary waves excited on a sessile drop by the SAW can be exploited for microparticle or nanoparticle collection and sorting. At higher amplitudes, the large substrate accelerations drives rapid destabilization of the drop interface giving rise to inertial liquid jets or atomization to produce 1–10 μm monodispersed aerosol droplets. These have significant implications for microfluidic chip mass spectrometry interfacing or pulmonary drug delivery. The atomization also provides a convenient means for the synthesis of 150–200 nm polymer or protein particles or to encapsulate proteins, peptides and other therapeutic molecules within biodegradable polymeric shells for controlled release drug delivery. The atomization of thin films containing polymer solutions, in addition, gives produces a unique regular, long-range spatial polymer spot patterning effect whose size and spacing are dependent on the SAW frequency, thus offering a simple and powerful method for surface patterning without requiring physical or chemical templating.

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