1,721,009 research outputs found
Evolved interactions stabilize many coexisting phases in multicomponent liquids
No full textPhase separation has emerged as an essential concept for the spatial organization inside biological cells. However, despite the clear relevance to virtually all physiological functions, we understand surprisingly little about what phases form in a system of many interacting components, like in cells. Here we introduce a numerical method based on physical relaxation dynamics to study the coexisting phases in such systems. We use our approach to optimize interactions between components, similar to how evolution might have optimized the interactions of proteins. These evolved interactions robustly lead to a defined number of phases, despite substantial uncertainties in the initial composition, while random or designed interactions perform much worse. Moreover, the optimized interactions are robust to perturbations, and they allow fast adaption to new target phase counts. We thus show that genetically encoded interactions of proteins provide versatile control of phase behavior. The phases forming in our system are also a concrete example of a robust emergent property that does not rely on fine-tuning the parameters of individual constituents.BN/Liedewij Laan La
Influence of physical interactions on spatiotemporal patterns
Full text linkSpatiotemporal patterns are often modeled using reaction-diffusion equations, which combine complex reactions between constituents with ideal diffusive motion. Such descriptions neglect physical interactions between constituents, which might affect resulting patterns. To overcome this, we study how physical interactions affect cyclic dominant reactions, like the seminal rock-paper-scissors game, which exhibits spiral waves for ideal diffusion. Generalizing diffusion to incorporate physical interactions, we find that weak interactions change the length- and time scales of spiral waves, consistent with a mapping to the complex Ginzburg-Landau equation. In contrast, strong repulsive interactions typically generate oscillating lattices, and strong attraction leads to an interplay of phase separation and chemical oscillations, like droplets co-locating with cores of spiral waves. Our work suggests that physical interactions are relevant for forming spatiotemporal patterns in nature, and it might shed light on how biodiversity is maintained in ecological settings
Heterogeneous nucleation and growth of sessile chemically active droplets
Full text linkDroplets are essential for spatially controlling biomolecules in cells. To work properly, cells need to control the emergence and morphology of droplets. On the one hand, driven chemical reactions can affect droplets profoundly. For instance, reactions can control how droplets nucleate and how large they grow. On the other hand, droplets coexist with various organelles and other structures inside cells, which could affect their nucleation and morphology. To understand the interplay of these two aspects, we study a continuous field theory of active phase separation. Our numerical simulations reveal that reactions suppress nucleation while attractive walls enhance it. Intriguingly, these two effects are coupled, leading to shapes that deviate substantially from the spherical caps predicted for passive systems. These distortions result from anisotropic fluxes responding to the boundary conditions dictated by the Young–Dupré equation. Interestingly, an electrostatic analogy of chemical reactions confirms these effects. We thus demonstrate how driven chemical reactions affect the emergence and morphology of droplets, which could be crucial for understanding biological cells and improving technical applications, e.g., in chemical engineering.Deutsche Forschungsgemeinschaft 10.13039/501100001659European Research Council 10.13039/50110000078
Physics of droplet regulation in biological cells
No full textAbstract Droplet formation has emerged as an essential concept for the spatiotemporal organisation of biomolecules in cells. However, classical descriptions of droplet dynamics based on passive liquid-liquid phase separation cannot capture the complex situation inside cells. This review discusses three distinct aspects that are crucial in cells: (i) biomolecules are diverse and individually complex, implying that cellular droplets posses complex internal behaviour, e.g., in terms of their material properties; (ii) the cellular environment contains many solid-like structures that droplets can wet; (iii) cells are alive and use fuel to drive processes out of equilibrium. We illustrate how these principles control droplet nucleation, growth, position, and count to unveil possible regulatory mechanisms in biological cells and other applications of phase separation
Systematic Parameterization of Flory–Huggins Models from Molecular Dynamics Simulations for Ternary Lipid Mixtures
No full textMemory capacity of adaptive flow networks
Full text linkBiological flow networks adapt their network morphology to optimise flow
while being exposed to external stimuli from different spatial locations in
their environment. These adaptive flow networks retain a memory of the stimulus
location in the network morphology. Yet, what limits this memory and how many
stimuli can be stored is unknown. Here, we study a numerical model of adaptive
flow networks by applying multiple stimuli subsequently. We find strong memory
signals for stimuli imprinted for a long time into young networks.
Consequently, networks can store many stimuli for intermediate stimulus
duration, which balance imprinting and ageing.Comment: 7 pages, 4 figures, 9 pages of appendi
Interference length reveals regularity of crossover placement across species
Full text linkAbstract Crossover interference is a phenomenon that affects the number and positioning of crossovers in meiosis and thus affects genetic diversity and chromosome segregation. Yet, the underlying mechanism is not fully understood, partly because quantification is difficult. To overcome this challenge, we introduce the interference length L int that quantifies changes in crossover patterning due to interference. We show that it faithfully captures known aspects of crossover interference and provides superior statistical power over previous measures such as the interference distance and the gamma shape parameter. We apply our analysis to empirical data and unveil a similar behavior of L int across species, which hints at a common mechanism. A recently proposed coarsening model generally captures these aspects, providing a unified view of crossover interference. Consequently, L int facilitates model refinements and general comparisons between alternative models of crossover interference.Max-Planck-Gesellschaft 501100004189Max-Planck-Gesellschaft 50110000418
Memory Formation in Adaptive Networks
No full textThe continuous adaptation of networks like our vasculature ensures optimal network performance when challenged with changing loads. Here, we show that adaptation dynamics allow a network to memorize the position of an applied load within its network morphology. We identify that the irreversible dynamics of vanishing network links encode memory. Our analytical theory successfully predicts the role of all system parameters during memory formation, including parameter values which prevent memory formation. We thus provide analytical insight on the theory of memory formation in disordered systems
Physical interactions promote Turing patterns
Full text linkTuring's mechanism is often invoked to explain periodic patterns in nature,
although direct experimental support is scarce. Turing patterns form in
reaction-diffusion systems when the activating species diffuse much slower than
the inhibiting species, and the involved reactions are highly non-linear. Such
reactions can originate from co-operativity, whose physical interactions should
also affect diffusion. We here take direct interactions into account and show
that they strongly affect Turing patterns. We find that weak repulsion between
the activator and inhibitor can substantially lower the required differential
diffusivity and reaction non-linearity. In contrast, strong interactions can
induce phase separation, but the resulting length scale is still typically
governed by the fundamental reaction-diffusion length scale. Taken together,
our theory connects traditional Turing patterns with chemically active phase
separation, thus describing a wider range of systems. Moreover, we demonstrate
that even weak interactions affect patterns substantially, so they should be
incorporated when modeling realistic systems.Comment: 5 pages, 5 figures, and appendi
Nucleation of chemically active droplets
Full text linkDriven chemical reactions can control the macroscopic properties of droplets,
like their size. Such active droplets are critical in structuring the interior
of biological cells. Cells also need to control where and when droplets appear,
so they need to control droplet nucleation. Our numerical simulations
demonstrate that reactions generally suppress nucleation if they stabilize the
homogeneous state. An equilibrium surrogate model reveals that reactions
increase the effective energy barrier of nucleation, enabling quantitative
predictions of the increased nucleation times. Moreover, the surrogate model
allows us to construct a phase diagram, which summarizes how reactions affect
the stability of the homogeneous phase and the droplet state. This simple
picture provides accurate predictions of how driven reactions delay nucleation,
which is relevant for understanding droplets in biological cells and chemical
engineering.Comment: 7 pages, 3 figure
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