Biological Physics
- [1] arXiv:2405.14542 [pdf, ps, html, other]
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Title: Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularitySubjects: Biological Physics (physics.bio-ph); Adaptation and Self-Organizing Systems (nlin.AO)
Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation with empirical functional connectivity dynamics. Here, we propose and show that the brain's hierarchically modular mesoscale structure alone can give rise to robust metastable dynamics and (metastable) chimera states in the presence of phase frustration. We construct unweighted $3$-layer hierarchical networks of identical Kuramoto-Sakaguchi oscillators, parameterized by the average degree of the network and a structural parameter determining the ratio of connections between and within blocks in the upper two layers. Together, these parameters affect the characteristic timescales of the system. Away from the critical synchronization point, we detect the emergence of metastable states in the lowest hierarchical layer coexisting with chimera and metastable states in the upper layers. Using the Laplacian renormalization group flow approach, we uncover two distinct pathways towards achieving the metastable regimes detected in these distinct layers. In the upper layers, we show how the symmetry-breaking states depend on the slow eigenmodes of the system. In the lowest layer instead, metastable dynamics can be achieved as the separation of timescales between layers reaches a critical threshold. Our results show an explicit relationship between metastability, chimera states, and the eigenmodes of the system, bridging the gap between harmonic based studies of empirical data and oscillatory models.
New submissions for Friday, 24 May 2024 (showing 1 of 1 entries )
- [2] arXiv:2405.12998 (cross-list from q-bio.OT) [pdf, ps, other]
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Title: The miscalibration of the honeybee odometerComments: 16 pagesSubjects: Other Quantitative Biology (q-bio.OT); Biological Physics (physics.bio-ph)
We examine a series of articles on honeybee odometry and navigation published between 1996 and 2010, and find inconsistencies in results, duplicated figures, indications of data manipulation, and incorrect calculations. This suggests that redoing the experiments in question is warranted.
- [3] arXiv:2405.13424 (cross-list from cond-mat.stat-mech) [pdf, ps, html, other]
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Title: Emergent behaviour and phase transitions in spatially distributed multi-cellular metabolic networksK. Narayanankutty, J. A. Pereiro-Morejon, A. Ferrero, V. Onesto, S. Forciniti, L. L. del Mercato, R. Mulet, A. De Martino, D. S. Tourigny, D. De MartinoComments: Main(14 pages)+ supporting information(14 pages). Comments are welcomeSubjects: Statistical Mechanics (cond-mat.stat-mech); Biological Physics (physics.bio-ph); Cell Behavior (q-bio.CB)
Overflow metabolism is a ubiquitous phenomenon whereby cells in aerobic conditions excrete byproducts of glycolysis, such as lactate or acetate, into the medium in a seemingly wasteful and polluting fashion. Whilst overflow may confer microbes a fitness advantage by allowing them to overcome a finite oxidative capacity, its occurrence in higher organisms is harder to assess. Important insight was however obtained in recent experiments conducted at single-cell resolution, which revealed that accumulation of overflow products in tumor cell cultures known as the Warburg effect arises from imbalances in the dynamic and heterogeneous inter-cellular exchange network through which cells collectively regulate the microenvironment. Here we provide a quantitative characterization of this scenario by integrating metabolic network modeling with diffusion constraints, statistical physics theory and single-cell experimental flux data. On the theoretical side, we clarify how diffusion-limited exchanges shape the space of viable metabolic states of a multi-cellular system. Specifically, a phase transition from a balanced network of exchanges to an unbalanced overflow regime occurs as the mean cellular glucose and oxygen uptakes vary while single-cell metabolic phenotypes are highly heterogeneous around this transition. We then show that time-resolved data from human tumor-stroma cell co-cultures consistently map to this crossover region, supporting the idea that environmental deterioration reflects a failure of coordination among recurrently interacting cells. In summary, our findings suggest that, rather than deriving from multiple independent cell-autonomous processes, environmental control is an emergent feature of multi-cellular systems.
- [4] arXiv:2405.13508 (cross-list from cond-mat.dis-nn) [pdf, ps, html, other]
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Title: Furutsu-Novikov--like cross-correlation--response relations for systems driven by shot noiseComments: 12 pages, 9 figuresSubjects: Disordered Systems and Neural Networks (cond-mat.dis-nn); Biological Physics (physics.bio-ph)
We consider a dynamic system that is driven by an intensity-modulated Poisson process with intensity $\Lambda(t)=\lambda(t)+\varepsilon\nu(t)$. We derive an exact relation between the input-output cross-correlation in the spontaneous state ($\varepsilon=0$) and the linear response to the modulation ($\varepsilon>0$). This can be regarded as a variant of the Furutsu-Novikov theorem for the case of shot noise. As we show, the relation is still valid in the presence of additional independent noise. Furthermore, we derive an extension to Cox-process input, i.e. to colored shot noise. We discuss applications to particle detection and to neuroscience. Using the new relation, we obtain a fluctuation-response-relation for a leaky integrate-and-fire neuron. We also show how the new relation can be used in a remote control problem in a recurrent neural network. The relations are numerically tested for both stationary and non-stationary dynamics. Lastly, extensions to marked Poisson processes and to higher-order statistics are presented.
- [5] arXiv:2405.13521 (cross-list from cond-mat.stat-mech) [pdf, ps, html, other]
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Title: Run-and-tumble particle with saturating ratesSubjects: Statistical Mechanics (cond-mat.stat-mech); Biological Physics (physics.bio-ph); Cell Behavior (q-bio.CB)
We consider a run-and-tumble particle whose speed and tumbling rate are space-dependent on an infinite line. Unlike most of the previous work on such models, here we make the physical assumption that at large distances, these rates saturate to a constant. For our choice of rate functions, we show that a stationary state exists, and the exact steady state distribution decays exponentially or faster and can be unimodal or bimodal. The effect of boundedness of rates is seen in the mean-squared displacement of the particle that displays qualitative features different from those observed in the previous studies where it approaches the stationary state value monotonically in time; in contrast, here we find that if the initial position of the particle is sufficiently far from the origin, the variance in its position either varies nonmonotonically or plateaus before reaching the stationary state. These results are captured quantitatively by the exact solution of the Green's function when the particle has uniform speed but the tumbling rates change as a step-function in space; the insights provided by this limiting case are found to be consistent with the numerical results for the general model.
- [6] arXiv:2405.13905 (cross-list from cs.CE) [pdf, ps, html, other]
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Title: Calibration of stochastic, agent-based neuron growth models with Approximate Bayesian ComputationComments: 32 pages, 12 FiguresSubjects: Computational Engineering, Finance, and Science (cs.CE); Biological Physics (physics.bio-ph)
Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components -- the so-called agents -- discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.
- [7] arXiv:2405.13963 (cross-list from cond-mat.soft) [pdf, ps, html, other]
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Title: Active dynamics of charged macromoleculesComments: 13 pages, 1 figureSubjects: Soft Condensed Matter (cond-mat.soft); Biological Physics (physics.bio-ph); Chemical Physics (physics.chem-ph)
We study the role of active coupling on the transport properties of homogeneously charged macromolecules in an infinitely dilute solution. An enzyme becomes actively bound to a segment of the macromolecule, exerting an electrostatic force on it. Eventually, thermal fluctuations cause it to become unbound, introducing active coupling into the system. We study the mean-squared displacement (MSD) and find a new scaling regime compared to the thermal counterpart in the presence of hydrodynamic and segment-segment electrostatic interactions. Furthermore, the study of segment-segment equal-time correlation reveals the swelling of the macromolecule. Further, we derive the concentration equation of the macromolecule with active binding and study how the cooperative diffusivity of the macromolecules get modified by its environment, including the macromolecules itself. It turns out that these active fluctuations enhance the effective diffusivity of the macromolecules. The derived closed-form expression for diffusion constant is pertinent to the accurate interpretation of light scattering data in multi-component systems with binding-unbinding equilibria.
- [8] arXiv:2405.14011 (cross-list from cond-mat.soft) [pdf, ps, html, other]
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Title: Nonlinear Response Theory of Molecular MachinesSubjects: Soft Condensed Matter (cond-mat.soft); Statistical Mechanics (cond-mat.stat-mech); Biological Physics (physics.bio-ph)
Chemical affinities are responsible for driving active matter systems out of equilibrium. At the nano-scale, molecular machines interact with the surrounding environment and are subjected to external forces. The mechano-chemical coupling which arises naturally in these systems reveals a complex interplay between chemical and mechanical degrees of freedom with strong impact on their active mechanism. By considering various models far from equilibrium, we show that the tuning of applied forces give rise to a nonlinear response that causes a non-monotonic behaviour in the machines' activity. Our findings have implications in understanding, designing, and triggering such processes by controlled application of external fields, including the collective dynamics of larger non-equilibrium systems where the total dissipation and performance might be affected by internal and inter-particle interactions.
Cross submissions for Friday, 24 May 2024 (showing 7 of 7 entries )
- [9] arXiv:2402.17660 (replaced) [pdf, ps, html, other]
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Title: TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular SimulationsRaul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr, Philipp Thölke, Thomas E. Markland, Gianni De FabritiisComments: Version accepted in Journal of Chemical Theory and ComputationSubjects: Machine Learning (cs.LG); Biological Physics (physics.bio-ph); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2-fold to 10-fold over previous iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and the smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at this https URL.