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Paolo De Los Rios

Publications and source records attributed to Paolo De Los Rios.

At least 19 recordsLinked to original sources

Thermodynamic Space of Chemical Reaction Networks

Living systems operate out of equilibrium, continuously consuming energy to sustain organised, functional states. Their emergent behaviour usually relies on a set of interconnected chemical reaction networks (CRNs) driven by external fluxes that keep some species at fixed concentrations. Hence, uncovering the principles governing the functioning of these CRNs is crucial to understand how living systems generate and regulate complexity. While kinetics plays a key role in shaping detailed dynamical phenomena, the range of operations of a CRN is fundamentally constrained by thermodynamics. Here, we introduce and analytically derive the "thermodynamic space" of a CRN, i.e., the range of accessible stationary concentrations that can be realized under a given energetic budget. We establish analogous bounds for reaction affinities, shedding light on how global thermodynamic properties, such as the total non-equilibrium driving, can limit local non-equilibrium quantities, and further showing that these bounds can be used to test candidate network topologies and expose hidden multi-molecular pathways. We illustrate our results in various paradigmatic examples, demonstrating how the onset of complex behaviors is intimately tangled with the presence of non-equilibrium conditions. By providing a general tool for analysing CRNs, the presented framework constitutes a stepping stone to deepen our ability to predict complex out-of-equilibrium phenomena and design artificial chemical systems, starting from the sole knowledge of the underlying thermodynamic properties.

cond-mat.stat-mech↗

Thermodynamic Bounds on Symmetry Breaking in Linear and Catalytic Biochemical Systems

Living systems are maintained out-of-equilibrium by external driving forces. At stationarity, they exhibit emergent selection phenomena that break equilibrium symmetries and originate from the expansion of the accessible chemical space due to non-equilibrium conditions. Here, we use the matrix-tree theorem to derive upper and lower thermodynamic bounds on these symmetry-breaking features in linear and catalytic biochemical systems. Our bounds are independent of the kinetics and hold for both closed and open reaction networks. We also extend our results to master equations in the chemical space. Using our framework, we recover the thermodynamic constraints in kinetic proofreading. Finally, we show that the contrast of reaction-diffusion patterns can be bounded only by the non-equilibrium driving force. Our results provide a general framework for understanding the role of non-equilibrium conditions in shaping the steady-state properties of biochemical systems.

cond-mat.stat-mech↗

Entropic Clustering of Stickers Induces Aging in Biocondensates

Biomolecular condensates are cellular phase-separated droplets that usually exhibit a viscoelastic mechanical response. A behavior rationalized by modeling the complex molecules that make up a condensate as stickers and spacers, which assemble into a network-like structure. Condensates usually exhibit a solidification over a long period of time (days), a phenomenon described as aging.The emergence of such a long timescale of evolution from microscopic processes, as well as the associated microscopic reorganization leading to aging, remains mostly an open question. In this article, we explore the connection between the mechanical properties of the condensates and their microscopic structure. We propose a minimal model for the dynamic of stickers and spacers, and show that entropy maximization of spacers leads to an attractive force between stickers. Our system displays a surprisingly slow relaxation toward equilibrium, reminiscent of glassy systems and consistent with the liquid-to-solid transition observed. To explain this behavior, we study the clustering dynamic of stickers and successfully explain the origin of glassy relaxation.

cond-mat.soft↗

A Minimal Model for Carnot Efficiency at Maximum Power

Carnot efficiency sets a fundamental upper bound on the heat engine efficiency, attainable in the quasi-static limit, albeit at the cost of completely sacrificing power output. In this Letter, we present a minimal heat engine model that can attain Carnot efficiency while achieving maximum power output. We unveil the potential of intrinsic divergent physical quantities within the working substance, such as degeneracy, as promising thermodynamic resources to break through the universal power-efficiency trade-off imposed by nonequilibrium thermodynamics for conventional heat engines. Our findings provide novel insights into the collective advantage in harnessing energy of many-body interacting systems.

cond-mat.stat-mech↗

Phase boundaries promote chemical reactions through localized fluxes

One of the hypothesized functions of biomolecular condensates is to act as chemical reactors, where chemical reactions can be modulated, i.e. accelerated or slowed down, while substrate molecules enter and products exit from the condensate. Likewise, the components themselves that take part in the architectural integrity of condensates might be modified by active (energy consuming, non-equilibrium) processes, e.g. by ATPase chaperones or by kinases and phosphatases. In this work, we study how the presence of spatial inhomogeneities, such as in the case of liquid-liquid phase separation, affects active chemical reactions and results in the presence of directional flows of matter, which are one of the hallmarks of non-equilibirum processes. We establish the minimal conditions for the existence of such spatial currents, and we furthermore find that these fluxes are maximal at the condensate interface. These results propose that some condensates might be most efficient as chemical factories due to their interfaces rather than their volumes, and could suggest a possible biological reason for the the observed abundance of small non-fusing condensates inside the cell, thus maximizing their surface and the associated fluxes.

physics.bio-ph↗

Dissipation-driven selection under finite diffusion: hints from equilibrium and separation of time-scales

When exposed to a thermal gradient, reaction networks can convert thermal energy into the chemical selection of states that would be unfavourable at equilibrium. The kinetics of reaction paths, and thus how fast they dissipate available energy, might be dominant in dictating the stationary populations of all chemical states out-of-equilibrium. This phenomenology has been theoretically explored mainly in the infinite diffusion limit. Here, we show that the regime in which the diffusion rate is finite, and also slower than some chemical reactions, might give birth to interesting features, as the maximization of selection, or the switch of the selected state at stationarity. We introduce a framework, rooted in a time-scale separation analysis, which is able to capture leading non-equilibrium features using only equilibrium arguments under well-defined conditions. In particular, it is possible to identify fast-dissipation subnetworks of reactions whose Boltzmann equilibrium dominates the steady-state of the entire system as a whole. Finally, we also show that the dissipated heat (and so the entropy production) can be estimated, under some approximations, through the heat capacity of fast-dissipation subnetworks. This work provides a tool to develop an intuitive equilibrium-based grasp on complex non-isothermal reaction networks, which are important paradigms to understand the emergence of complex structures from basic building blocks.

cond-mat.stat-mech↗

Interpretable Neural Networks based classifiers for categorical inputs

Because of the pervasive usage of Neural Networks in human sensitive applications, their interpretability is becoming an increasingly important topic in machine learning. In this work we introduce a simple way to interpret the output function of a neural network classifier that take as input categorical variables. By exploiting a mapping between a neural network classifier and a physical energy model, we show that in these cases each layer of the network, and the logits layer in particular, can be expanded as a sum of terms that account for the contribution to the classification of each input pattern. For instance, at the first order, the expansion considers just the linear relation between input features and output while at the second order pairwise dependencies between input features are also accounted for. The analysis of the contributions of each pattern, after an appropriate gauge transformation, is presented in two cases where the effectiveness of the method can be appreciated.

cs.LG↗

The intrinsic non-equilibrium nature of thermophoresis

Exposing a solution to a temperature gradient can lead to the accumulation of particles on either the cold or warm side. This phenomenon, known as thermophoresis, has been discovered more than a century ago, and yet its microscopic origin is still debated. Here, we show that thermophoresis can be observed in any system such that the transitions between different internal states are modulated by temperature and such that different internal states have different transport properties. We establish thermophoresis as a genuine non-equilibrium effect, whereby a system of currents in real and internal space that is consistent with the thermodynamic necessity of transporting heat from warm to cold regions. Our approach also provides an expression for the Soret coefficient, which decides whether particles accumulate on the cold or on the warm side, that is associated with the correlation between the energies of the internal states and their transport properties, that instead remain system-specific quantities. Finally, we connect our results to previous approaches based on close-to-equilibrium energetics. Our thermodynamically consistent approach thus encompasses and generalizes previous findings.

cond-mat.stat-mech↗

Equilibrium and non-equilibrium furanose selection in the ribose isomerisation network

The exclusive presence of $β$-D-ribofuranose in nucleic acids is still a conundrum in prebiotic chemistry, given that pyranose species are substantially more stable at equilibrium. However, a precise characterisation of the relative furanose/pyranose fraction at temperatures higher than about 50$^{\,\rm o}$C is still lacking. Here, we employ a combination of NMR measurements and statistical mechanics modelling to predict a population inversion between furanose and pyranose at equilibrium at high temperatures. More importantly, we show that a steady temperature gradient may steer an open isomerisation network into a non-equilibrium steady state where furanose is boosted beyond the limits set by equilibrium thermodynamics. Moreover, we demonstrate that nonequilibrium selection of furanose is maximum at optimal dissipation, as gauged by the temperature gradient and energy barriers for isomerisation. The predicted optimum is compatible with temperature drops found in hydrothermal vents associated with extremely fresh lava flows on the seafloor.

cond-mat.stat-mech↗

Nonequilibrium theory of enzyme chemotaxis and enhanced diffusion

Enhanced diffusion and anti-chemotaxis of enzymes have been reported in several experiments in the last decade, opening up entirely new avenues of research in the bio-nanosciences both at the applied and fundamental level. Here, we introduce a novel theoretical framework, rooted in non-equilibrium effects characteristic of catalytic cycles, that explains all observations made so far in this field. In addition, our theory predicts entirely novel effects, such as dissipation-induced switch between anti-chemotactic and chemotactic behavior.

cond-mat.stat-mech↗

Dissipation-driven selection in non-equilibrium chemical networks

Life has most likely originated as a consequence of processes taking place in non-equilibrium conditions (\textit{e.g.} in the proximity of deep-sea thermal vents) selecting states of matter that would have been otherwise unfavorable at equilibrium. Here we present a simple chemical network in which the selection of states is driven by the thermodynamic necessity of dissipating heat as rapidly as possible in the presence of a thermal gradient: states participating to faster reactions contribute the most to the dissipation rate, and are the most populated ones in non-equilibrium steady-state conditions. Building upon these results, we show that, as the complexity of the chemical network increases, the \textit{velocity} of the reaction path leading to a given state determines its selection, giving rise to non-trivial localization phenomena in state space. A byproduct of our studies is that, in the presence of a temperature gradient, thermophoresis-like behavior inevitably appears depending on the transport properties of each individual state, thus hinting at a possible microscopic explanation of this intriguing yet still not fully understood phenomenon.

cond-mat.stat-mech↗

Efficient conversion of chemical energy into mechanical work by Hsp70 chaperones

Hsp70 molecular chaperones are abundant ATP-dependent nanomachines that actively reshape non-native, misfolded proteins and assist a wide variety of essential cellular processes. Here we combine complementary computational/theoretical approaches to elucidate the structural and thermodynamic details of the chaperone-induced expansion of a substrate protein, with a particular emphasis on the critical role played by ATP hydrolysis. We first determine the conformational free-energy cost of the substrate expansion due to the binding of multiple chaperones using coarse-grained molecular simulations. We then exploit this result to implement a non-equilibrium rate model which estimates the degree of expansion as a function of the free energy provided by ATP hydrolysis. Our results are in quantitative agreement with recent single-molecule FRET experiments and highlight the stark non-equilibrium nature of the process, showing that Hsp70s are optimized to convert effectively chemical energy into mechanical work close to physiological conditions.

q-bio.BM↗

Entropic selection of concepts unveils hidden topics in documents corpora

The organization and evolution of science has recently become itself an object of scientific quantitative investigation, thanks to the wealth of information that can be extracted from scientific documents, such as citations between papers and co-authorship between researchers. However, only few studies have focused on the concepts that characterize full documents and that can be extracted and analyzed, revealing the deeper organization of scientific knowledge. Unfortunately, several concepts can be so common across documents that they hinder the emergence of the underlying topical structure of the document corpus, because they give rise to a large amount of spurious and trivial relations among documents. To identify and remove common concepts, we introduce a method to gauge their relevance according to an objective information-theoretic measure related to the statistics of their occurrence across the document corpus. After progressively removing concepts that, according to this metric, can be considered as generic, we find that the topic organization displays a correspondingly more refined structure.

physics.soc-ph↗

Interplay between cost and benefits triggers nontrivial vaccination uptake

The containment of epidemic spreading is a major challenge in science. Vaccination, whenever available, is the best way to prevent the spreading, because it eventually immunizes individuals. However, vaccines are not perfect, and total immunization is not guaranteed. Imperfect immunization has driven the emergence of anti-vaccine movements that totally alter the predictions about the epidemic incidence. Here, we propose a mathematically solvable mean-field vaccination model to mimic the spontaneous adoption of vaccines against influenza-like diseases, and the expected epidemic incidence. The results are in agreement with extensive Monte Carlo simulations of the epidemics and vaccination co-evolutionary processes. Interestingly, the results reveal a non-monotonic behavior on the vaccination coverage, that increases with the imperfection of the vaccine and after decreases. This apparent counterintuitive behavior is analyzed and understood from stability principles of the proposed mathematical model.

physics.soc-ph↗

Thermodynamic bounds on the ultra- and infra-affinity of Hsp70 for its substrates

The 70 kDa Heat Shock Proteins Hsp70 have several essential functions in living systems, such as protecting cells against protein aggregation, assisting protein folding, remodeling protein complexes and driving the translocation into organelles. These functions require high affinity for non-specific amino-acid sequences that are ubiquitous in proteins. It has been recently shown that this high affinity, called ultra-affinity, depends on a process driven out of equilibrium by ATP hydrolysis. Here we establish the thermodynamic bounds for ultra-affinity, and further show that the same reaction scheme can in principle be used both to strengthen and to weaken affinities (leading in this case to infra-affinity). We show that cofactors are essential to achieve affinity beyond the equilibrium range. Finally, biological implications are discussed.

q-bio.BM↗

ScienceWISE: Topic Modeling over Scientific Literature Networks

We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics. So far, SW has been proven to be an effective platform for managing large volumes of technical articles by means of ontological concept-based browsing. However, as the publication of research articles accelerates, the expressivity and the richness of the SW ontology turns into a double-edged sword: a more fine-grained characterization of articles is possible, but at the cost of introducing more spurious relations among them. In this context, the challenge of continuously recommending relevant articles to users lies in tackling a network partitioning problem, where nodes represent articles and co-occurring concepts create edges between them. In this paper, we discuss the three research directions we have taken for solving this issue: i) the identification of generic concepts to reinforce inter-article similarities; ii) the adoption of a bipartite network representation to improve scalability; iii) the design of a clustering algorithm to identify concepts for cross-disciplinary articles and obtain fine-grained topics for all articles.

cs.DL↗

The Shape of a Stretched Polymer

The shape of a polymer plays an important role in determining its interactions with other molecules and with the environment, and is in turn affected by both of them. As a consequence, in the literature the shape properties of a chain in many different conditions have been investigated. Here, we characterize the shape and orientational properties of a polymer chain under tension, a physical condition typically realized both in single-molecule experiments and in vivo. By means of analytical calculations and Monte Carlo simulations, we develop a theoretical framework which quantitatively describes these properties, highlighting the interplay between external force and chain size in determining the spatial distribution of a stretched chain.

cond-mat.soft↗

Quantifying the role of chaperones in protein translocation by computational modelling

The molecular chaperone Hsp70 plays a central role in the import of cytoplasmic proteins into organelles, driving their translocation by binding them from the organellar interior. Starting from the experimentally-determined structure of the \textit{E. coli} Hsp70, we computed, by means of molecular simulations, the effective free-energy profile for substrate translocation upon chaperone binding. We then used the resulting free energy to quantitatively characterize the kinetics of the import process, whose comparison with unassisted translocation highlights the essential role played by Hsp70 in importing cytoplasmic proteins.

q-bio.SC↗