Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Nonequilibrium associative retrieval of multiple stored self-assembly targets.

ABSTRACT: Many biological systems rely on the ability to self-assemble different target structures using the same set of components. Equilibrium self-assembly suffers from a limited capacity in such cases, due to an increasing number of decoy states that grows rapidly with the number of targets encoded. Moreover, improving the kinetic stability of a target at equilibrium carries the price of introducing kinetic traps, leading to slower assembly. Using a toy physical model of interacting particles, we demonstrate that local driving can improve both the assembly time and kinetic stability of multitarget self-assembly, as well as reduce fluctuations around the target configuration. We further show that the local drive can result in a steady-state probability distribution over target structures that deviates from the Boltzmann distribution in a way that depends on the types of interactions that stabilize the targets. Our results illustrate the role that nonequilibrium driving plays in overcoming tradeoffs that are inherent to equilibrium assemblies.

SUBMITTER: Bisker G

PROVIDER: S-EPMC6233095 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Design principles for nonequilibrium self-assembly.

Project description:We consider an important class of self-assembly problems, and using the formalism of stochastic thermodynamics, we derive a set of design principles for growing controlled assemblies far from equilibrium. The design principles constrain the set of configurations that can be obtained under nonequilibrium conditions. Our central result provides intuition for how equilibrium self-assembly landscapes are modified under finite nonequilibrium drive.

| S-EPMC5167149 | biostudies-literature

Multifarious assembly mixtures: systems allowing retrieval of diverse stored structures.

Project description:Self-assembly materials are traditionally designed so that molecular or mesoscale components form a single kind of large structure. Here, we propose a scheme to create "multifarious assembly mixtures," which self-assemble many different large structures from a set of shared components. We show that the number of multifarious structures stored in the solution of components increases rapidly with the number of different types of components. However, each stored structure can be retrieved by tuning only a few parameters, the number of which is only weakly dependent on the size of the assembled structure. Implications for artificial and biological self-assembly are discussed.

| S-EPMC4291664 | biostudies-literature

Nonequilibrium Self-Assembly Time Forecasting by the Stochastic Landscape Method.

Project description:Many biological systems rely on the ability to self-assemble target structures from different molecular building blocks using nonequilibrium drives, stemming, for example, from chemical potential gradients. The complex interactions between the different components give rise to a rugged energy landscape with a plethora of local minima on the dynamic pathway to the target assembly. Exploring a toy physical model of multicomponents nonequilibrium self-assembly, we demonstrate that a segmented description of the system dynamics can be used to provide predictions of the first assembly times. We show that for a wide range of values of the nonequilibrium drive, a log-normal distribution emerges for the first assembly time statistics. Based on data segmentation by a Bayesian estimator of abrupt changes (BEAST), we further present a general data-based algorithmic scheme, namely, the stochastic landscape method (SLM), for assembly time predictions. We demonstrate that this scheme can be implemented for the first assembly time forecast during a nonequilibrium self-assembly process, with improved prediction power compared to a naïve guess based on the mean remaining time to the first assembly. Our results can be used to establish a general quantitative framework for nonequilibrium systems and to improve control protocols of nonequilibrium self-assembly processes.

| S-EPMC10350916 | biostudies-literature

NERDSS: A Nonequilibrium Simulator for Multibody Self-Assembly at the Cellular Scale.

Project description:Currently, a significant barrier to building predictive models of cellular self-assembly processes is that molecular models cannot capture minutes-long dynamics that couple distinct components with active processes, whereas reaction-diffusion models cannot capture structures of molecular assembly. Here, we introduce the nonequilibrium reaction-diffusion self-assembly simulator (NERDSS), which addresses this spatiotemporal resolution gap. NERDSS integrates efficient reaction-diffusion algorithms into generalized software that operates on user-defined molecules through diffusion, binding and orientation, unbinding, chemical transformations, and spatial localization. By connecting the fast processes of binding with the slow timescales of large-scale assembly, NERDSS integrates molecular resolution with reversible formation of ordered, multisubunit complexes. NERDSS encodes models using rule-based formatting languages to facilitate model portability, usability, and reproducibility. Applying NERDSS to steps in clathrin-mediated endocytosis, we design multicomponent systems that can form lattices in solution or on the membrane, and we predict how stochastic but localized dephosphorylation of membrane lipids can drive lattice disassembly. The NERDSS simulations reveal the spatial constraints on lattice growth and the role of membrane localization and cooperativity in nucleating assembly. By modeling viral lattice assembly and recapitulating oscillations in protein expression levels for a circadian clock model, we illustrate the adaptability of NERDSS. NERDSS simulates user-defined assembly models that were previously inaccessible to existing software tools, with broad applications to predicting self-assembly in vivo and designing high-yield assemblies in vitro.

| S-EPMC7300348 | biostudies-literature

Nonequilibrium self-assembly dynamics of icosahedral viral capsids packaging genome or polyelectrolyte.

Project description:The survival of viruses partly relies on their ability to self-assemble inside host cells. Although coarse-grained simulations have identified different pathways leading to assembled virions from their components, experimental evidence is severely lacking. Here, we use time-resolved small-angle X-ray scattering to uncover the nonequilibrium self-assembly dynamics of icosahedral viral capsids packaging their full RNA genome. We reveal the formation of amorphous complexes via an en masse pathway and their relaxation into virions via a synchronous pathway. The binding energy of capsid subunits on the genome is moderate (~7kBT0, with kB the Boltzmann constant and T0 = 298 K, the room temperature), while the energy barrier separating the complexes and the virions is high (~ 20kBT0). A synthetic polyelectrolyte can lower this barrier so that filled capsids are formed in conditions where virions cannot build up. We propose a representation of the dynamics on a free energy landscape.

| S-EPMC6078970 | biostudies-literature

Sleep improves prospective remembering by facilitating spontaneous-associative retrieval processes.

Project description:Memories are of the past but for the future, enabling individuals to implement intended plans and actions at the appropriate time. Prospective memory is the specific ability to remember and execute an intended behavior at some designated point in the future. Although sleep is well-known to benefit the consolidation of memories for past events, its role for prospective memory is still not well understood. Here, we show that sleep as compared to wakefulness after prospective memory instruction enhanced the successful execution of prospective memories two days later. We further show that sleep benefited both components of prospective memory, i.e. to remember that something has to be done (prospective component) and to remember what has to be done (retrospective component). Finally, sleep enhanced prospective remembering particularly when attentional resources were reduced during task execution, suggesting that subjects after sleep were able to recruit additional spontaneous-associative retrieval processes to remember intentions successfully. Our findings indicate that sleep supports the maintenance of prospective memory over time by strengthening intentional memory representations, thus favoring the spontaneous retrieval of the intended action at the appropriate time.

| S-EPMC3797070 | biostudies-literature

Robust nonequilibrium pathways to microcompartment assembly.

Project description:Cyanobacteria sequester photosynthetic enzymes into microcompartments which facilitate the conversion of carbon dioxide into sugars. Geometric similarities between these structures and self-assembling viral capsids have inspired models that posit microcompartments as stable equilibrium arrangements of the constituent proteins. Here we describe a different mechanism for microcompartment assembly, one that is fundamentally nonequilibrium and yet highly reliable. This pathway is revealed by simulations of a molecular model resolving the size and shape of a cargo droplet and the extent and topography of an elastic shell. The resulting metastable microcompartment structures closely resemble those of carboxysomes, with a narrow size distribution and faceted shells. The essence of their assembly dynamics can be understood from a simpler mathematical model that combines elements of classical nucleation theory with continuum elasticity. These results highlight important control variables for achieving nanoscale encapsulation in general and for modulating the size and shape of carboxysomes in particular.

| S-EPMC6016822 | biostudies-other

Hippocampal GABA levels correlate with retrieval performance in an associative learning paradigm.

Project description:Neural plasticity is a complex process dependent on neurochemical underpinnings. Next to the glutamatergic system which contributes to memory formation via long-term potentiation (LTP) and long-term depression (LTD), the main inhibitory neurotransmitter, GABA is crucially involved in neuroplastic processes. Hence, we investigated changes in glutamate and GABA levels in the brain in healthy participants performing an associative learning paradigm. Twenty healthy participants (10 female, 25 ± 5 years) underwent paired multi-voxel magnetic resonance spectroscopy imaging before and after completing 21 days of a facial associative learning paradigm in a longitudinal study design. Changes of GABA and glutamate were compared to retrieval success in the hippocampus, insula and thalamus. No changes in GABA and glutamate concentration were found after 21 days of associative learning. However, baseline hippocampal GABA levels were significantly correlated with initial retrieval success (pcor = 0.013, r = 0.690). In contrast to the thalamus and insula (pcor>0.1), higher baseline GABA levels in the hippocampus were associated with better retrieval performance in an associative learning paradigm. Therefore, our findings support the importance of hippocampal GABA levels in memory formation in the human brain in vivo.

| S-EPMC7610791 | biostudies-literature

Activation of perineuronal net-expressing excitatory neurons during associative memory encoding and retrieval.

Project description:Perineuronal nets (PNNs), proteoglycan-rich extracellular matrix structures, are thought to be expressed around inhibitory neurons and contribute to critical periods of brain function and synaptic plasticity. However, in some specific brain regions such as the amygdala, PNNs were predominantly expressed around excitatory neurons. These neurons were recruited during auditory fear conditioning and memory retrieval. Indeed, the activation of PNN-expressing excitatory neurons predicted cognitive performance.

| S-EPMC5380958 | biostudies-literature

Redox-promoted associative assembly of metal-organic materials.

Project description:We develop an associative synthesis of metal-organic materials that combines solid-state metal oxidation and coordination-driven self-assembly into a one-step, waste-free transformation. The methodology hinges on the unique reactivity of ortho-quinones, which we introduce as versatile oxidants for mechanochemical synthesis. Our strategy opens a previously unexplored route to paramagnetic metal-organic materials from elementary metals.

| S-EPMC5530005 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data