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Jaime Agudo-Canalejo

Publications and source records attributed to Jaime Agudo-Canalejo.

At least 19 recordsLinked to original sources
arXiv2026

Curvature-induced migration of small domains on lipid membranes

Membrane curvature has been shown to bias the location of lipid or protein domains, but the roles of bending rigidity, spontaneous curvature, Gaussian bending modulus, and line tension are difficult to separate in general. We develop a small-domain description for a membrane whose shape is held fixed, e.g. by strong adhesion to a curved substrate. Combining a local Helfrich energy with the small-area isoperimetric expansion gives a position-dependent energy determined by the local mean and Gaussian curvatures. We identify four generic regimes of curvature preference whose boundaries depend on only two dimensionless parameters. On specific membrane shapes with complex curvature, we show that this curvature preference determines the energy landscapes experienced by small domains, which are strongly dependent on the size of the domain through the line tension contribution. For example, on an oblate-shaped membrane, very small domains tend to localize at the poles, whereas larger domains prefer to localize at the equator. Whether this transition is continuous or discontinuous is found to strongly depend on the spontaneous curvature of the domain. This ordering suggests a mechanism by which small domains can collect and coalesce at one location before relocating as they grow.

cond-mat.soft↗
arXiv2026

An interdimensional funnel enhances topologically protected oscillations in high-dimensional stochastic systems

Biochemical systems such as protein complexes often occupy configuration spaces whose dimensionality grows with the number of molecular components. Although usually viewed as an obstacle to coherent dynamics, here we show that high dimensionality can enhance topologically protected oscillations. In the topological regime, a stochastic system with a $D$-dimensional configuration space develops a steady-state current confined to a one-dimensional edge cycle. The intervening boundary faces between the $D$-dimensional bulk and the one-dimensional cycle form an interdimensional funnel, sequentially pushing the system towards lower dimensional faces, down to the one-dimensional cycle. As a consequence, increasing $D$ enhances the funnel, leading to improved one-dimensional localization and oscillatory coherence. A quantized biorthogonal Zak phase identifies the transition to the topological regime, which additionally exhibits a highly structured non-Hermitian skin effect. In contrast with previous realizations of topological states in stochastic systems, the phenomena we uncover have no counterpart in quantum condensed matter or active matter systems.

cond-mat.soft↗
arXiv2026

Topological edge currents promote exploratory search in microtubule dynamic instability

Microtubules capture chromosomes during mitosis by stochastically switching between growth and shrinkage at catastrophe events. They display strikingly rich biochemistry and dynamics, regulated by a stabilizing cap with distinct conformational states. Microtubule lengths at catastrophe are observed to follow a peaked distribution, while their growth "stutters" briefly before catastrophe. Such complexity makes it hard to capture all these observations without a large number of tunable parameters. Here, we introduce a topological model of the microtubule cap that reproduces the features above through dynamical edge states, that provides a minimal description with just two free parameters. Our approach further provides an analytical description of catastrophes and allows the same features to persist over a wide range of tubulin concentration, consistent with experimental observations.

physics.bio-ph↗
arXiv2026

Robust Topologically Protected Edge Transport in Doubly Chiral Active Particles

Using theory, simulation, and experiment, we introduce a new class of active particle which we term doubly chiral active Brownian particles (dcABPs), which show robust topologically protected transport along boundaries without backscattering at corners. Their double chirality stems from the coexistence of an intrinsic angular velocity, which can cause rotation independently of translation, and a translation-rotation coupling inducing cross-alignment to the instantaneous velocity, which causes rotation only concomitantly with translation. A mechanically detailed model shows that the latter effect can arise from an asymmetric friction distribution in the direction perpendicular to the self-propulsion direction. We show that topologically protected modes emerge when the two sources of chirality have opposite sign and the intrinsic rotation is weaker than the translation-rotation coupling. In the deterministic limit, we characterize the emergence of these modes not only along straight boundaries, but also along curved boundaries and during interparticle interactions. We provide a proof-of-principle experimental realization by building a doubly chiral vibrobot. While setting the work into context, we moreover show that the topologically protected boundary-induced transport of dcABPs stands in contrast to the edge currents observed for simple chiral ABPs, which we demonstrate are not associated with boundary-induced transport, as well as to those observed for chiral active rods or self-aligning chiral ABPs, which we show to be associated with boundary-induced transport but to backscatter at corners, implying lack of topological protection.

cond-mat.soft↗
arXiv2026

Nonequilibrium protein complexes as molecular automata

Biology stores information and computes at the molecular scale, yet the ways in which it does so are often distinct from human-engineered computers. Mapping biological computation onto architectures familiar to computer science remains an outstanding challenge. Here, inspired by Crick's proposal for molecular memory, we analyse a thermodynamically-consistent model of a protein complex subject to driven, nonequilibrium enzymatic reactions. In the strongly driven limit, we find that the system maps onto a stochastic, asynchronous variant of cellular automata, where each rule corresponds to a different set of enzymes being present. We find a broad class of phenomena in these 'molecular automata' that can be exploited for molecular computation, including error-tolerant memory via multistable attractors, and long transients that can be used as molecular stopwatches. By systematically enumerating all possible dynamical rules, we identify those that allow molecular automata to implement simple computational architectures such as finite-state machines. Overall, our results provide a framework for engineering synthetic molecular automata, and offer a route to building protein-based computation in living cells.

physics.bio-ph↗
arXiv2025

Spatial self-organization of enzymes in complex reaction networks

Living systems contain intricate biochemical networks whose structure is closely related to their function and allows them to exhibit robust behavior in the presence of external stimuli. Such networks typically involve catalytic enzymes, which can have non-trivial transport properties, in particular chemotaxis-like directed motion along gradients of substrates and products. Here, we find that taking into account enzyme chemotaxis in models of catalyzed reaction networks can lead to their spatial self-organization in a process similar to biomolecular condensate formation. We develop a general theory for arbitrary reaction networks, and systematically study all closed unimolecular reaction networks involving up to six chemicals. Importantly, we find that network-wide propagation of concentration perturbations can be key to enabling self-organization. The ability to self-organize is highly dependent on the relative signs of the chemotactic mobilities of the enzymes to their substrate and product and on the global network structure. We find that spontaneous self-organization through chemotaxis can provide an avenue for the self-regulation of metabolic activity in complex catalyzed reaction networks. The network-induced interaction mechanism we uncover operates in the regime where the substrate molecules are diffusion-limited, suggesting that signaling molecules could take advantage of this scenario towards their functionality.

physics.bio-ph↗
arXiv2025

Topological phases in discrete stochastic systems

Topological invariants have proved useful for analyzing emergent function as they characterize a property of the entire system, and are insensitive to local details, disorder, and noise. They support boundary states, which reduce the system response to a lower dimensional space and, in 2D systems, offer a mechanism for the emergence of global cycles within a large phase space. Topological invariants have been heavily studied in quantum electronic systems and have been observed in other classical platforms such as mechanical lattices. However, this framework largely describes equilibrium systems within an ordered crystalline lattice, whereas biological systems are often strongly non-equilibrium with stochastic components. We review recent developments in topological states in discrete stochastic models in 1D and 2D systems, and initial progress in identifying testable signature of topological states in molecular systems and ecology. These models further provide simple principles for targeted dynamics in synthetic systems and in the engineering of reconfigurable materials. Lastly, we describe novel theoretical properties of these systems such as the necessity for non-Hermiticity in permitting edge states, as well as new analytical tools to reveal these properties. The emerging developments shed light on fundamental principles for non-equilibrium systems and topological protection enabling robust biological function.

cond-mat.stat-mech↗
arXiv2025

Mechanistic rules for de novo design of enzymes

Enzymes are nano-scale machines that have evolved to drive chemical reactions out of equilibrium in the right place at the right time. Given the complexity and specificity of enzymatic function, bottom-up design of enzymes presents a daunting task that is far more challenging than making passive molecules with specific binding affinities or building nano-scale mechanically active devices. We present a thermodynamically-consistent model for the operation of such a fuelled enzyme, which uses the energy from a favourable reaction to undergo non-equilibrium conformational changes that in turn catalyze a chemical reaction on an attached substrate molecule. We show that enzymatic function can emerge through a bifurcation upon appropriate implementation of momentum conservation on the effective reaction coordinates of the low dimensional description of the enzyme, and thanks to a generically present dissipative coupling. Our results can complement the recently developed strategies for de novo enzyme design based on machine learning approaches.

cond-mat.soft↗
arXiv2025

Topological phase locking in stochastic oscillators

The dynamics of many nanoscale biological and synthetic systems such as enzymes and molecular motors are activated by thermal noise, and driven out-of-equilibrium by local energy dissipation. Because the energies dissipated in these systems are comparable to the thermal energy, one would generally expect their dynamics to be highly stochastic. Here, by studying a thermodynamically-consistent model of two coupled noise-activated oscillators, we show that this is not always the case. Thanks to a novel phenomenon that we term topological phase locking (TPL), the coupled dynamics become quasi-deterministic, resulting in a greatly enhanced average speed of the oscillators. TPL is characterized by the emergence of a band of periodic orbits that form a torus knot in phase space, along which the two oscillators advance in rational multiples of each other. The effectively conservative dynamics along this band coexists with the basin of attraction of the dissipative fixed point. We further show that TPL arises as a result of a complex, infinite hierarchy of global bifurcations. Our results have implications for understanding the dynamics of a wide range of systems, from biological enzymes and molecular motors to engineered nanoscale electronic, optical, or mechanical oscillators.

cond-mat.stat-mech↗
arXiv2024

Nonlinear response theory of molecular machines

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.

cond-mat.soft↗
arXiv2024

Phase Coexistence in Nonreciprocal Quorum-Sensing Active Matter

Motility and nonreciprocity are two primary mechanisms for self-organization in active matter. In a recent study [Phys. Rev. Lett. 131, 148301 (2023)], we explored their joint influence in a minimal model of two-species quorum-sensing active particles interacting via mutual motility regulation. Our results notably revealed a highly dynamic phase of chaotic chasing bands that is absent when either nonreciprocity or self-propulsion is missing. Here, we examine further the phase behavior of nonreciprocal quorum-sensing active particles, distinguishing between the regimes of weak and strong nonreciprocity. In the weakly nonreciprocal regime, this system exhibits multi-component motility-induced phase separation. We establish an analytical criterion for the associated phase coexistence, enabling a quantitative prediction of the phase diagram. For strong nonreciprocity, where the dynamics is chase-and-run-like, we numerically determine the phase behavior and show that it strongly depends on the scale of observation. In small systems, our numerical simulations reveal a phenomenology consistent with phenomenological models, comprising traveling phase-separated domains and spiral-like defect patterns. However, we show that these structures are generically unstable in large systems, where they are superseded by bulk phase coexistence between domains that are either homogeneous or populated by mesoscopic chasing bands. Crucially, this implies that collective motion totally vanishes at large scales, while the breakdown of our analytical criterion for this phase coexistence with multi-scale structures prevents us from predicting the corresponding phase diagram.

cond-mat.soft↗
arXiv2024

Thermodynamic inference of correlations in nonequilibrium collective dynamics

The theory of stochastic thermodynamics has revealed many useful fluctuation relations, with the thermodynamic uncertainty relation (TUR) being a theorem of major interest. When many nonequilibrium currents interact with each other, a naive application of the TUR to an individual current can result in an apparent violation of the TUR bound. Here, we explore how such an apparent violation can be used to put a lower bound on the strength of correlations $C$ as well as the number $N$ of interacting currents in collective dynamics. This lower bound is a combined bound on $C(N-1)$ if only one current is measured, or a bound on $N$ if two currents are measured. Our proposed protocol allows for the inference of hidden correlations in experiment, for example when a team of molecular motors pulls on the same cargo but only one or a subset of them is fluorescently tagged. By solving analytically and numerically several models of many-body nonequilibrium dynamics, we ascertain under which conditions this strategy can be applied and the inferred bound on correlations becomes tight.

cond-mat.stat-mech↗
arXiv2023

Role of shape in particle-lipid membrane interactions: from surfing to full engulfment

Understanding and manipulating the interactions between foreign bodies and cell membranes during endo- and phagocytosis is of paramount importance, not only for the fate of living cells but also for numerous biomedical applications. This study aims to elucidate the role of variables such as anisotropic particle shape, curvature, orientation, membrane tension, and adhesive strength in this essential process, using a minimal experimental biomimetic system comprising giant unilamellar vesicles and rod-like particles with different curvatures and aspect ratios. We find that the particle wrapping process is dictated by the balance between the elastic energy penalty and adhesion energy gain, leading to two distinct engulfment pathways, tip-first and side-first, emphasizing the significance of the particle orientation in determining the pathway. Moreover, our experimental results are consistent with theoretical predictions in a state diagram, showcasing how to control the wrapping pathway from surfing to partial to complete wrapping by the interplay between membrane tension and adhesive strength. At moderate particle concentrations, we observed the formation of rod clusters, which exhibited cooperative and sequential wrapping. Our study not only contributes to a comprehensive understanding of the mechanistic intricacies of endocytosis by highlighting how the interplay between the anisotropic particle shape, curvature, orientation, membrane tension, and adhesive strength can influence the engulfment pathway but also provides a foundational base for future research in the field.

cond-mat.soft↗
arXiv2023

Enhanced diffusion of tracer particles in nonreciprocal mixtures

We study the diffusivity of a tagged particle in a binary mixture of Brownian particles with non-reciprocal interactions. Numerical simulations reveal that, for a broad class of interaction potentials, non-reciprocity can significantly increase the effective diffusion coefficient of tracer particles, and that this diffusion enhancement is associated with a breakdown of the Einstein relation. These observations are quantified and confirmed via two different and complementary analytical approaches: (i) a linearized stochastic density field theory, which is particularly accurate in the limit of soft interactions; (ii) a reduced two-body description, which is exact at leading order in the density of particles. The latter reveals that diffusion enhancement can be attributed to the formation of transiently propelled dimers of particles, whose cohesion and speed are controlled by the non-reciprocal interactions.

cond-mat.soft↗
arXiv2023

Entropy production and thermodynamic inference for stochastic microswimmers

The question of characterization of the degree of non-equilibrium activity in active matter systems is studied in the context of a stochastic microswimmer model driven by a chemical cycle. The resulting dynamical properties and entropy production rate unravel a complex interplay between the chemical and the hydrodynamic degrees of freedom beyond linear response, which is not captured by conventional phenomenological approaches. By studying the precision-dissipation trade-off, a new protocol is proposed in which microscopic chemical driving forces can be inferred experimentally. Our findings highlight subtleties associated with the stochastic thermodynamics of autonomous microswimmers.

cond-mat.stat-mech↗
arXiv2023

Dynamical pattern formation without self-attraction in quorum-sensing active matter: the interplay between nonreciprocity and motility

We study a minimal model involving two species of particles interacting via quorum-sensing rules. Combining simulations of the microscopic model and linear stability analysis of the associated coarse-grained field theory, we identify a mechanism for dynamical pattern formation that does not rely on the standard route of intra-species effective attractive interactions. Instead, our results reveal a highly dynamical phase of chasing bands induced only by the combined effects of self-propulsion and nonreciprocity in the inter-species couplings. Turning on self-attraction, we find that the system may phase separate into a macroscopic domain of such chaotic chasing bands coexisting with a dilute gas. We show that the chaotic dynamics of bands at the interfaces of this phase-separated phase results in anomalously slow coarsening.

cond-mat.stat-mech↗
arXiv2023

Interaction-motif-based classification of self-organizing metabolic cycles

Particles that are catalytically-active and chemotactic can interact through the concentration fields upon which they act, which in turn may lead to wide-scale spatial self-organization. When these active particles interact through several fields, these interactions gain an additional structure, which can result in new forms of collective behaviour. Here, we study a mixture of active species which catalyze the conversion of a substrate chemical into a product chemical, and chemotax in concentration gradients of both substrate and product. Such species develop non-reciprocal, specific interactions that we coarse-grain into attractive and repulsive, which can lead to a potentially complex interaction network. We consider the particular case of a metabolic cycle of three species, each of which interacts with itself and both other species in the cycle. We find that the stability of a cycle of species that only chemotax in gradients of their substrate is piloted by a set of two parameter-free conditions, which we use to classify the low number of corresponding interaction networks. In the more general case of substrate- and product-chemotactic species, we can derive a set of two high-dimensional stability conditions, which can be used to classify the stability of all the possible interaction networks based on the self- and pair-interaction motifs they contain. The classification scheme that we introduce can help guide future studies on the dynamics of complex interaction networks and explorations of the corresponding large parameter spaces in such metabolically active complex systems.

cond-mat.soft↗
arXiv2023

Network effects lead to self-organization in metabolic cycles of self-repelling catalysts

Mixtures of particles that interact through phoretic effects are known to aggregate if they belong to species that exhibit attractive self-interactions. We study self-organization in a model metabolic cycle composed of three species of catalytically-active particles that are chemotactic towards the chemicals that define their connectivity network. We find that the self-organization can be controlled by the network properties, as exemplified by a case where a collapse instability is achieved by design for self-repelling species. Our findings highlight a possibility for controlling the intricate functions of metabolic networks by taking advantage of the physics of phoretic active matter.

cond-mat.soft↗