Search results for "Physics - Biological Physics"

showing 10 items of 61 documents

On the thermodynamic origin of metabolic scaling

2018

The origin and shape of metabolic scaling has been controversial since Kleiber found that basal metabolic rate of animals seemed to vary as a power law of their body mass with exponent 3/4, instead of 2/3, as a surface-to-volume argument predicts. The universality of exponent 3/4 -claimed in terms of the fractal properties of the nutrient network- has recently been challenged according to empirical evidence that observed a wealth of robust exponents deviating from 3/4. Here we present a conceptually simple thermodynamic framework, where the dependence of metabolic rate with body mass emerges from a trade-off between the energy dissipated as heat and the energy efficiently used by the organi…

0106 biological sciences0301 basic medicineFOS: Physical scienceslcsh:Medicine92B05010603 evolutionary biology01 natural sciencesPower lawArticle03 medical and health sciencesFractalPhysics - Biological PhysicsStatistical physicslcsh:ScienceQuantitative Biology - Populations and EvolutionAdditive modelScalingMathematicsMultidisciplinarylcsh:RPopulations and Evolution (q-bio.PE)Universality (dynamical systems)030104 developmental biologyBiological Physics (physics.bio-ph)13. Climate actionFOS: Biological sciencesEctothermBasal metabolic rateExponentlcsh:QScientific Reports
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Collective behavior of quorum-sensing run-and-tumble particles in confinement

2016

We study a generic model for quorum-sensing bacteria in circular confinement. Every bacterium produces signaling molecules, the local concentration of which triggers a response when a certain threshold is reached. If this response lowers the motility then an aggregation of bacteria occurs, which differs fundamentally from standard motility-induced phase separation due to the long-ranged nature of the concentration of signal molecules. We analyze this phenomenon analytically and by numerical simulations employing two different protocols leading to stationary cluster and ring morphologies, respectively.

0301 basic medicineCollective behaviorGeneral Physics and AstronomyFOS: Physical sciencesNanotechnologyCondensed Matter - Soft Condensed MatterBacterial Physiological Phenomena01 natural sciencesSignalModels BiologicalQuantitative Biology::Cell BehaviorQuantitative Biology::Subcellular Processes03 medical and health sciences0103 physical sciencesCell Behavior (q-bio.CB)Cluster (physics)Physics - Biological Physics010306 general physicsCondensed Matter - Statistical MechanicsPhysicsStatistical Mechanics (cond-mat.stat-mech)ChemotaxisQuorum SensingQuorum sensing030104 developmental biologyChemical physicsBiological Physics (physics.bio-ph)FOS: Biological sciencesQuantitative Biology - Cell BehaviorSoft Condensed Matter (cond-mat.soft)
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Dynamic coarse-graining fills the gap between atomistic simulations and experimental investigations of mechanical unfolding

2017

We present a dynamic coarse-graining technique that allows to simulate the mechanical unfolding of biomolecules or molecular complexes on experimentally relevant time scales. It is based on Markov state models (MSM), which we construct from molecular dynamics simulations using the pulling coordinate as an order parameter. We obtain a sequence of MSMs as a function of the discretized pulling coordinate, and the pulling process is modeled by switching among the MSMs according to the protocol applied to unfold the complex. This way we cover seven orders of magnitude in pulling speed. In the region of rapid pulling we additionally perform steered molecular dynamics simulations and find excellen…

0301 basic medicineDiscretizationGeneral Physics and AstronomyMarkov processFOS: Physical sciencesCondensed Matter - Soft Condensed Matter01 natural sciences03 medical and health sciencesMolecular dynamicssymbols.namesake0103 physical sciencesPhysics - Biological PhysicsStatistical physicsPhysical and Theoretical Chemistry010306 general physicsPhysicsQuantitative Biology::BiomoleculesMarkov chainMolecular biophysicsBiomolecules (q-bio.BM)Function (mathematics)030104 developmental biologyQuantitative Biology - BiomoleculesOrders of magnitude (time)Biological Physics (physics.bio-ph)FOS: Biological sciencessymbolsSoft Condensed Matter (cond-mat.soft)Granularity
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Conformational dynamics of a single protein monitored for 24 hours at video rate

2018

We use plasmon rulers to follow the conformational dynamics of a single protein for up to 24 h at a video rate. The plasmon ruler consists of two gold nanospheres connected by a single protein linker. In our experiment, we follow the dynamics of the molecular chaperone heat shock protein 90 (Hsp90), which is known to show “open” and “closed” conformations. Our measurements confirm the previously known conformational dynamics with transition times in the second to minute time scale and reveals new dynamics on the time scale of minutes to hours. Plasmon rulers thus extend the observation bandwidth 3–4 orders of magnitude with respect to single-molecule fluorescence resonance energy transfer a…

0301 basic medicineLetterProtein ConformationMolecular ConformationFOS: Physical sciencesHsp90Bioengineeringsingle molecule02 engineering and technology7. Clean energyQuantitative Biology - Quantitative Methods03 medical and health sciencesMolecular dynamicsFluorescence Resonance Energy TransferNanotechnologyGeneral Materials ScienceHSP90 Heat-Shock ProteinsPhysics - Biological PhysicsQuantitative Methods (q-bio.QM)PlasmonPhysicsVideo rateMechanical EngineeringProtein dynamics92Biomolecules (q-bio.BM)General ChemistrySurface Plasmon Resonance021001 nanoscience & nanotechnologyCondensed Matter PhysicsGold nanospheres030104 developmental biologyFörster resonance energy transferQuantitative Biology - BiomoleculesBiological Physics (physics.bio-ph)Chemical physicsFOS: Biological sciencesprotein dynamicsPlasmon rulernonergodicityGold0210 nano-technologyLinker
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Mapping brain activity with flexible graphene micro-transistors

2016

arXiv:1611.05693v1.-- et al.

0301 basic medicineMaterials scienceFOS: Physical sciences02 engineering and technologylaw.invention03 medical and health scienceslawGeneral Materials ScienceElectronicsPhysics - Biological PhysicsNeural implantsBioelectronicsBioelectronicsbusiness.industryGrapheneSensorsMechanical EngineeringTransistorGeneral Chemistry021001 nanoscience & nanotechnologyCondensed Matter PhysicsField-effect transistorsMicroelectrodeBrain implant030104 developmental biologyBiological Physics (physics.bio-ph)Mechanics of MaterialsFOS: Biological sciencesQuantitative Biology - Neurons and CognitionOptoelectronicsNeurons and Cognition (q-bio.NC)Charge carrierField-effect transistorGraphene0210 nano-technologybusiness2D Materials
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Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states

2017

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on: (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according with local rules that are modulated by a parameter $\kappa$. This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate e…

0301 basic medicineNormalization (statistics)Majority ruleTime FactorsFOS: Physical sciencesAbnormal cellPattern Formation and Solitons (nlin.PS)Models BiologicalCell Physiological PhenomenaCombinatorics03 medical and health sciences0302 clinical medicineCell Behavior (q-bio.CB)Physics - Biological PhysicsGame of lifeMathematicsCellular Automata and Lattice Gases (nlin.CG)Artificial networksNonlinear Sciences - Pattern Formation and SolitonsCellular automatonMulticellular organism030104 developmental biologyBiological Physics (physics.bio-ph)030220 oncology & carcinogenesisFOS: Biological sciencesQuantitative Biology - Cell BehaviorBiological systemNonlinear Sciences - Cellular Automata and Lattice GasesCoupled map lattice
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Wavelength selection of rippling patterns in myxobacteria

2016

Rippling patterns of myxobacteria appear in starving colonies before they aggregate to form fruiting bodies. These periodic traveling cell density waves arise from the coordination of individual cell reversals, resulting from an internal clock regulating them, and from contact signaling during bacterial collisions. Here we revisit a mathematical model of rippling in myxobacteria due to Igoshin et al.\ [Proc. Natl. Acad. Sci. USA {\bf 98}, 14913 (2001) and Phys. Rev. E {\bf 70}, 041911 (2004)]. Bacteria in this model are phase oscillators with an extra internal phase through which they are coupled to a mean-field of oppositely moving bacteria. Previously, patterns for this model were obtaine…

0301 basic medicinePeriodicityPhase transitionPhase (waves)FOS: Physical sciencesModels BiologicalMotion03 medical and health sciencesQuantum mechanicsWavenumberComputer SimulationMyxococcalesPhysics - Biological PhysicsCondensed Matter - Statistical MechanicsPhysicsStatistical Mechanics (cond-mat.stat-mech)Kuramoto modelNonlinear systemWavelength030104 developmental biologyClassical mechanicsNonlinear DynamicsMean field theoryBiological Physics (physics.bio-ph)RipplingLinear Models
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Physical mechanisms of micro- and nanodomain formation in multicomponent lipid membranes.

2016

This article summarizes a variety of physical mechanisms proposed in the literature, which can generate micro- and nanodomains in multicomponent lipid bilayers and biomembranes. It mainly focusses on lipid-driven mechanisms that do not involve direct protein-protein interactions. Specifically, it considers (i) equilibrium mechanisms based on lipid-lipid phase separation such as critical cluster formation close to critical points, and multiple domain formation in curved geometries, (ii) equilibrium mechanisms that stabilize two-dimensional microemulsions, such as the effect of linactants and the effect of curvature-composition coupling in bilayers and monolayers, and (iii) non-equilibrium me…

0301 basic medicinePhase transitionCytoplasmCritical phenomenaLipid BilayersBiophysicsFOS: Physical sciencesCondensed Matter - Soft Condensed MatterMolecular Dynamics SimulationBiochemistryPhase TransitionQuantitative Biology::Subcellular Processes03 medical and health sciencesSurface-Active AgentsMembrane MicrodomainsMonolayerCluster (physics)AnimalsHumansMicroemulsionPhysics - Biological PhysicsLipid bilayerPhysics::Biological PhysicsBacteriaChemistryBiological membraneCell BiologyCrystallographyActin CytoskeletonKinetics030104 developmental biologyMembraneBiological Physics (physics.bio-ph)Chemical physicsSoft Condensed Matter (cond-mat.soft)ThermodynamicsEmulsionsSignal TransductionBiochimica et biophysica acta. Biomembranes
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Retrieving infinite numbers of patterns in a spin-glass model of immune networks

2013

The similarity between neural and immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies {\em in parallel}. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with `coordinator branches' (T-cells) and `effector branches' (B-cells), a…

0301 basic medicineSimilarity (geometry)Spin glassComputer sciencestatistical mechanicFOS: Physical sciencesGeneral Physics and AstronomyNetwork topologyTopology01 natural sciencesQuantitative Biology::Cell Behavior03 medical and health sciencesCell Behavior (q-bio.CB)0103 physical sciencesattractor neural-networks; statistical mechanics; brain networks; Physics and Astronomy (all)Physics - Biological Physics010306 general physicsAssociative propertybrain networkArtificial neural networkMechanism (biology)ErgodicityDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksAcquired immune system030104 developmental biologyBiological Physics (physics.bio-ph)FOS: Biological sciencesattractor neural-networkQuantitative Biology - Cell Behavior
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Counting individual 41Ca atoms with a magneto-optical trap

2003

Atom Trap Trace Analysis (ATTA), a novel method based upon laser trapping and cooling, is used to count individual atoms of 41Ca present in biomedical samples with isotopic abundance levels between 10^-8 and 10^-10. ATTA is calibrated against Resonance Ionization Mass Spectrometry, demonstrating a good agreement between the two methods. The present ATTA system has a counting efficiency of 2x10^-7. Within one hour of observation time, its 3-sigma detection limit on the isotopic abundance of 41Ca reaches 4.5x10^-10.

Atomic Physics (physics.atom-ph)Biological Physics (physics.bio-ph)FOS: Physical sciencesPhysics - Biological PhysicsPhysics - Atomic Physics
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