arXiv subjects

Trevor GrandPre

Publications and source records attributed to Trevor GrandPre.

12 recordsLinked to original sources

arXiv2026

Memory-Driven Self-Propulsion and Flocking of Chemically Active Droplets

Biomolecular condensates are continually remodeled by biochemical reactions that can exhibit non-Markovian, history-dependent dynamics. We develop a theory of active phase separation with non-Markovian reactions and show that delayed reaction feedback destabilizes stationary droplets: when the memory time becomes comparable to the reaction turnover time, condensates deform and spontaneously acquire a polar, self-propelled state. In multidroplet systems, persistent memory wakes mediate alignment, producing polar flocks and, at higher concentrations, traveling labyrinths. These results establish reaction memory as a control parameter of active phase separation, linking condensate remodeling, autonomous motility, and collective organization, and suggest a possible route to flocking-like behavior within cells.

cond-mat.soft↗

arXiv2026

Principles of Client Enrichment in Multicomponent Biomolecular Condensates

Biomolecular condensates are commonly organized by a small number of scaffold molecules that drive phase separation together with client molecules that do not condense on their own but become selectively recruited into the dense phase. A central open question is how client recruitment feeds back on scaffold interactions to determine condensate composition. Here we address this problem in a reconstituted focal adhesion system composed of focal adhesion kinase (FAK) and phosphorylated p130Cas (Cas) as scaffolds and the adaptor protein paxillin (PXN) as a client. We show that both FAK phosphorylation and PXN recruitment produce a common compositional response in which FAK becomes enriched while Cas is depleted within the condensate. To interpret these observations, we develop two complementary theoretical descriptions. First, within a two-component Flory-Huggins framework, we show that phosphorylation can be captured by either strengthening heterotypic FAK-Cas interactions or increasing the effective number of interaction-relevant segments on FAK, both of which bias partitioning toward FAK-rich condensates. Second, we introduce a minimal three-component Flory-Huggins theory without an explicit solvent and map it onto an effective two-component description, demonstrating that client recruitment renormalizes homotypic and heterotypic scaffold interactions. Analytical predictions for the location of the critical point are tested in reconstituted multicomponent systems through PXN addition, showing that client recruitment alone tunes proximity to criticality and reshapes condensate composition. Together, our results reveal distinct yet convergent physical routes by which post-translational modification and client recruitment control scaffold composition in multicomponent condensates.

q-bio.BM↗

arXiv2025

Coarse-Graining via Lumping: Exact Calculations and Fundamental Limitations

Detecting broken time-reversibility at micro- and nanoscale is often difficult when experiments offer limited state resolution. We introduce a lumping method that builds an effective semi-Markov model able to reproduce exactly the full entropy-production statistics of the microscopic dynamics. The mean entropy production stays accurate even when hidden current-carrying cycles are merged, though higher-order information can be unavoidably lost. In these cases, we capture violations of fluctuation theorems consistent with experiments, opening a path to novel inference strategies out of equilibrium.

cond-mat.stat-mech↗

arXiv2025

Extremal events dictate population growth rate inference

Recent methods have been developed to map single-cell lineage statistics to population growth. Because population growth selects for exponentially rare phenotypes, these methods inherently depend on sampling large deviations from finite data, which introduces systematic errors. A comprehensive understanding of these errors in the context of finite data remains elusive. To address this gap, we study the error in growth rate estimates across different models. We show that under the usual bias-variance decomposition, the bias can be decomposed into a finite-time bias and nonlinear averaging bias. We demonstrate that finite-time bias, which dominates at short times, can be mitigated by fitting its monotonic behavior. In contrast, at longer times, nonlinear averaging bias becomes the predominant source of error, leading to a phase transition. This transition can be understood through the Random Energy Model, a mean-field model of disordered systems, where a few lineages dominate the estimator. Applying these methods to experimental data demonstrates that correcting for biases in lineage-based approaches yields consistent results for the long-term growth rate across multiple methods and enables the reverse-engineering of dynamic models. This new framework provides a quantitative understanding of growth rate estimators, clarifies the conditions under which they can be effectively applied to finite data, and introduces model-free approaches for studying the connections between physiology and cell growth.

cond-mat.stat-mech↗

arXiv2024

Direct estimates of irreversibility from time series

The arrow of time can be quantified through the Kullback-Leibler divergence ($D_{KL}$) between the distributions of forward and reverse trajectories in a system. Many approaches to estimate this rely on specific models, but the use of incorrect models can introduce uncontrolled errors. Here, we describe a model-free method that uses trajectory data directly to estimate the evidence for irreversibility over finite windows of time. To do this we build on previous work to identify and correct for errors that arise from limited sample size. Importantly, our approach accurately recovers $D_{KL} = 0$ in systems that adhere to detailed balance, and the correct nonzero $D_{KL}$ for data generated by well understood models of nonequilibrium systems. We apply our method to trajectories of neural activity in the retina as it responds to naturalistic inputs, and find evidence of irreversibility in single neurons, emphasizing the non-Markovian character of these data. These results open new avenues for investigating how the brain represents the arrow of time.

cond-mat.stat-mech↗

arXiv2024

Maximum entropy models for patterns of gene expression

New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We notice that the probability distribution of cell states has many local maxima. By labeling cell states according to the associated maximum, we obtain a cell classification that agrees well with previous results that use traditional clustering techniques. Our results provide quantitative descriptions of gene expression statistics and interpretable criteria for defining cell classes, supporting the hypothesis that cell classes emerge from the collective interaction of gene expression levels.

physics.bio-ph↗

arXiv2023

Noise driven phase transitions in eco-evolutionary systems

In complex ecosystems such as microbial communities, there is constant ecological and evolutionary feedback between the residing species and the environment occurring on concurrent timescales. Species respond and adapt to their surroundings by modifying their phenotypic traits, which in turn alters their environment and the resources available. To study this interplay between ecological and evolutionary mechanisms, we develop a consumer-resource model that incorporates phenotypic mutations. In the absence of noise, we find that phase transitions require finely-tuned interaction kernels. Additionally, we quantify the effects of noise on frequency dependent selection by defining a time-integrated mutation current, which accounts for the rate at which mutations and speciation occurs. We find three distinct phases: homogeneous, patterned, and patterned traveling waves. The last phase represents one way in which co-evolution of species can happen in a fluctuating environment. Our results highlight the principal roles that noise and non-reciprocal interactions between resources and consumers play in phase transitions within eco-evolutionary systems.

q-bio.PE↗

arXiv2023

Tuning Nonequilibrium Phase Transitions with Inertia

In striking contrast to equilibrium systems, inertia can profoundly alter the structure of active systems. Here, we demonstrate that driven systems can exhibit effective equilibrium-like states with increasing particle inertia, despite rigorously violating the fluctuation-dissipation theorem. Increasing inertia progressively eliminates motility-induced phase separation and restores equilibrium crystallization for active Brownian spheres. This effect appears to be general for a wide class of active systems, including those driven by deterministic time-dependent external fields, whose nonequilibrium patterns ultimately disappear with increasing inertia. The path to this effective equilibrium limit can be complex, with finite inertia sometimes acting to accentuate nonequilibrium transitions. The restoration of near equilibrium statistics can be understood through the conversion of active momentum sources to passive-like stresses. Unlike truly equilibrium systems, the effective temperature is now density dependent, the only remnant of the nonequilibrium dynamics. This density-dependent temperature can in principle introduce departures from equilibrium expectations, particularly in response to strong gradients. Our results provide additional insight into the effective temperature ansatz while revealing a mechanism to tune nonequilibrium phase transitions.

cond-mat.soft↗

arXiv2021

Phase Diagram of Active Brownian Spheres: Crystallization and the Metastability of Motility-Induced Phase Separation

Motility-induced phase separation (MIPS), the phenomenon in which purely repulsive active particles undergo a liquid-gas phase separation, is among the simplest and most widely studied examples of a nonequilibrium phase transition. Here, we show that states of MIPS coexistence are in fact only metastable for three-dimensional active Brownian particles over a very broad range of conditions, decaying at long times through an ordering transition we call active crystallization. At an activity just above the MIPS critical point, the liquid-gas binodal is superseded by the crystal-fluid coexistence curve, with solid, liquid, and gas all coexisting at the triple point where the two curves intersect. Nucleating an active crystal from a disordered fluid, however, requires a rare fluctuation that exhibits the nearly close-packed density of the solid phase. The corresponding barrier to crystallization is surmountable on a feasible timescale only at high activity, and only at fluid densities near maximal packing. The glassiness expected for such dense liquids at equilibrium is strongly mitigated by active forces, so that the lifetime of liquid-gas coexistence declines steadily with increasing activity, manifesting in simulations as a facile spontaneous crystallization at extremely high activity.

cond-mat.soft↗

arXiv2020

Entropy production fluctuations encode collective behavior in active matter

We derive a general lower bound on distributions of entropy production in interacting active matter systems. The bound is tight in the limit that interparticle correlations are small and short-ranged, which we explore in four canonical active matter models. In all models studied, the bound is weak where collective fluctuations result in long-ranged correlations, which subsequently links the locations of phase transitions to enhanced entropy production fluctuations. We develop a theory for the onset of enhanced fluctuations and relate it to specific phase transitions in active Brownian particles. We also derive optimal control forces that realize the dynamics necessary to tune dissipation and manipulate the system between phases. In so doing, we uncover a general relationship between entropy production and pattern formation in active matter, as well as ways of controlling it.

cond-mat.stat-mech↗

arXiv2020

A large deviation principle linking lineage statistics to fitness in microbial populations

In exponentially proliferating populations of microbes, the population typically doubles at a rate less than the average doubling time of a single-cell due to variability at the single-cell level. It is known that the distribution of generation times obtained from a single lineage is, in general, insufficient to determine a population's growth rate. Is there an explicit relationship between observables obtained from a single lineage and the population growth rate? We show that a population's growth rate can be represented in terms of averages over isolated lineages. This lineage representation is related to a large deviation principle that is a generic feature of exponentially proliferating populations. Due to the large deviation structure of growing populations, the number of lineages needed to obtain an accurate estimate of the growth rate depends exponentially on the duration of the lineages, leading to a non-monotonic convergence of the estimate, which we verify in both synthetic and experimental data sets.

q-bio.PE↗

arXiv2018

Current fluctuations of interacting active Brownian particles

We derive the distribution of particle currents for a system of interacting active Brownian particles in the long time limit using large deviation theory and a weighted many body expansion. We find the distribution is non-Gaussian, except in the limit of passive particles. The non-Gaussian fluctuations can be understood from the effective potential the particles experience when conditioned on a given current. This potential suppresses fluctuations of the particles orientations and surrounding density, aligning particles and reducing their effective drag. From the distribution of currents, we compute the diffusion coefficient, which is in excellent agreement with molecular dynamics simulations over a range of self-propulsion velocities and densities. We show that mass transport is Fickian in that the diffusion constant determines the response of a small density gradient, and that nonlinear responses are similarly computable from the density dependence of the current distribution.

cond-mat.stat-mech↗