arXiv subjects

Jose M. G. Vilar

Publications and source records attributed to Jose M. G. Vilar.

At least 19 recordsLinked to original sources

arXiv2026

Closed-form hard-sphere thermodynamics under nanoscale confinement: from equations of state to unmixing forces

Equations of state accurately describe hard-sphere thermodynamics but are generally considered to fail under nanoscale confinement. We show that this failure is geometric rather than thermodynamic: a closed-form, parameter-free mapping from apparent to effective packing fraction restores the accuracy of the Carnahan-Starling equation of state and, with it, the entire analytical thermodynamic framework, including free energies, wall pressures, chemical potentials, and the large-cavity surface response. Applied to overlapping anchored hard-sphere droplets, a geometry inherent to biomolecular force generation, it yields unmixing free energies and full distance-dependent force profiles in quantitative agreement with simulations from the dilute regime to above the bulk freezing transition.

cond-mat.soft↗

arXiv2026

Propensity Straight-Through Gradients for Discrete Stochastic Systems

Continuous-time Markov chains (CTMCs) provide the backbone for modeling discrete stochastic dynamics across applied, physical, and biological sciences. Their integration with modern gradient-based machine learning, however, is limited by the hard categorical event selection intrinsic to Gillespie-type simulation algorithms. We exploit the affine state update to obtain the exact one-step conditional-mean sensitivity by differentiating normalized reaction propensities. We pair this backward rule with exact forward trajectories to define the propensity straight-through (PST) estimator. At the trajectory level, we show that one-step sensitivities composed across events can depart from the exact multistep sensitivity. We derive the resulting per-step discrepancy in closed form and prove that it vanishes identically for affine downstream dependence. PST matches the accuracy of Gumbel-Softmax straight-through across all benchmarks: reversible dimerization (0.06% error), a genetic oscillator (1.7% error), a 50-task repressilator suite (0.17% median error), and patch-clamp ion-channel recordings ($R^2$ = 0.988). Under matched settings, PST converges 3.0-fold faster on the oscillator and 2.1-fold faster on the ion channel. At deep-learning scale, PST trains a 203,796-parameter stochastic reaction network with hard sampling, reaching 98.22% MNIST digit classification accuracy. By differentiating an exact conditional mean rather than a relaxed sample, PST offers a temperature- and Gumbel-free path to scalable gradient-based learning through exact stochastic trajectories.

q-bio.QM↗

arXiv2026

Scaling Laws and Paradoxical Metastable States in Nanofilament Entropic Separation

Entropic forces play a fundamental role in nanoscale phenomena, from colloidal self-assembly to biomolecular disaggregation. Here, we develop an exact analytical theory and find general scaling laws for the entropic separation of tether-mediated nanofilament bundles, revealing that a single dimensionless parameter--the ratio of the excluded-volume radius to the tether length--dictates whether filaments are pushed apart or, contrary to the usual expectation, pulled together. This unexpected regime challenges the view that entropic forces invariably promote disaggregation, instead uncovering conditions under which the bundles can remain in attractive metastable states. Brownian dynamics simulations confirm this paradoxical effect, offering predictive insights for applications in biophysics, soft matter physics, and nanotechnology.

cond-mat.soft↗

arXiv2026

Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization

Exact stochastic simulation of continuous-time Markov chains (CTMCs) is essential when discreteness and noise drive system behavior, but the hard categorical event selection in Gillespie-type algorithms blocks gradient-based learning. We eliminate this constraint by decoupling forward simulation from backward differentiation, with hard categorical sampling generating exact trajectories and gradients propagating through a continuous massively-parallel Gumbel-Softmax straight-through surrogate. Our approach enables accurate optimization at parameter scales over four orders of magnitude beyond existing simulators. We validate for accuracy, scalability, and reliability on a reversible dimerization model (0.09% error), a genetic oscillator (1.2% error), a 203,796-parameter gene regulatory network achieving 98.4% MNIST accuracy (a prototypical deep-learning multilayer perceptron benchmark), and experimental patch-clamp recordings of ion channel gating (R^2 = 0.987) in the single-channel regime. Our GPU implementation delivers 1.9 billion steps per second, matching the scale of non-differentiable simulators. By making exact stochastic simulation massively parallel and autodiff-compatible, our results enable high-dimensional parameter inference and inverse design across systems biology, chemical kinetics, physics, and related CTMC-governed domains.

q-bio.QM↗

arXiv2024

Quasi-average predictions and regression to the trend: an application the M6 financial forecasting competition

The efficient market hypothesis considers all available information already reflected in asset prices and limits the possibility of consistently achieving above-average returns by trading on publicly available data. We analyzed low dispersion prediction methods and their application to the M6 financial forecasting competition. Predictive averages and regression to the trend offer slight but potentially consistent advantages over the reference indexes. We put these results in the context of high variability approaches, which, if not accompanied by high information content, are bound to underperform the benchmark index as they are prone to overfit the past. In general, predicting the expected values under high uncertainty conditions, such as those assumed by the efficient market hypothesis, is more effective on average than trying to predict actual values.

stat.AP↗

arXiv2024

Actionable forecasting as a determinant of function in noisy biological systems

Continuous adaptation to variable environments is crucial for the survival of living organisms. Here, we analyze how adaptation, forecasting, and resource mobilization towards a target state, termed actionability, interact to determine biological function. We develop a general theory and show that it is possible for organisms to continuously track their optimal state in a dynamic environment by adapting towards an actionable target that incorporates just current information on the optimal state and its rate of change. If the environmental information is precise and readily actionable, it is possible to implement perfect tracking without anticipatory mechanisms, irrespective of the adaptation rate. In contrast, predictive functions, like those of circadian rhythms, are beneficial if sensing the environment is slow or unreliable, as they allow better adaptation with fewer resources. To explore potential actionable forecasting mechanisms, we develop a general approach that implements the adaptation dynamics with forecasting through a dynamics-informed neural network.

q-bio.CB↗

arXiv2022

Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

Inferring the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is as relevant as challenging. It is a requirement for current approaches to overcome the need to know the details of the perturbations to proceed with the analyses. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions with 0.93-day accuracy over the time span of a year.

cs.LG↗

arXiv2019

Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations

This paper provides a description of the approach and methodology I used in winning the European Union Big Data Technologies Horizon Prize on data-driven prediction of electricity grid traffic. The methodology relies on identifying typical short-term recurrent fluctuations, which is subsequently refined through a regression-of-fluctuations approach. The key points and strategic considerations that led to selecting or discarding different methodological aspects are also discussed. The criteria include adaptability to changing conditions, reliability with outliers and missing data, robustness to noise, and efficiency in implementation.

stat.AP↗

arXiv2014

Suppression and enhancement of transcriptional noise by DNA looping

DNA looping has been observed to enhance and suppress transcriptional noise but it is uncertain which of these two opposite effects is to be expected for given conditions. Here, we derive analytical expressions for the main quantifiers of transcriptional noise in terms of the molecular parameters and elucidate the role of DNA looping. Our results rationalize paradoxical experimental observations and provide the first quantitative explanation of landmark individual-cell measurements at the single molecule level on the classical lac operon genetic system [Choi et al., Science 322, 442-446 (2008)].

q-bio.SC↗

arXiv2014

Entropy of leukemia on multidimensional morphological and molecular landscapes

Leukemia epitomizes the class of highly complex diseases that new technologies aim to tackle by using large sets of single-cell level information. Achieving such goal depends critically not only on experimental techniques but also on approaches to interpret the data. A most pressing issue is to identify the salient quantitative features of the disease from the resulting massive amounts of information. Here, I show that the entropies of cell-population distributions on specific multidimensional molecular and morphological landscapes provide a set of measures for the precise characterization of normal and pathological states, such as those corresponding to healthy individuals and acute myeloid leukemia (AML) patients. I provide a systematic procedure to identify the specific landscapes and illustrate how, applied to cell samples from peripheral blood and bone marrow aspirates, this characterization accurately diagnoses AML from just flow cytometry data. The methodology can generally be applied to other types of cell-populations and establishes a straightforward link between the traditional statistical thermodynamics methodology and biomedical applications.

q-bio.QM↗

arXiv2014

System-size scaling of Boltzmann and alternate Gibbs entropies

It has recurrently been proposed that the Boltzmann textbook definition of entropy $S(E)=k\ln Ω(E)$ in terms of the number of microstates $Ω(E)$ with energy $E$ should be replaced by the expression $S_G(E)=k\ln \sum_{E^\prime <E}{Ω(E^\prime )}$ examined by Gibbs. Here, we show that $S_G$ either is equivalent to $S$ in the macroscopic limit or becomes independent of the energy exponentially fast as the system size increases. The resulting exponential scaling makes the realistic use of $S_G$ unfeasible and leads in general to temperatures that are inconsistent with the notions of hot and cold.

cond-mat.stat-mech↗

arXiv2013

Systems Biophysics of Gene Expression

Gene expression is a central process to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges among traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses.

q-bio.MN↗

arXiv2011

Trafficking Coordinate Description of Intracellular Transport Control of Signaling Networks

Many cellular networks rely on the regulated transport of their components to transduce extracellular information into precise intracellular signals. The dynamics of these networks is typically described in terms of compartmentalized chemical reactions. There are many important situations, however, in which the properties of the compartments change continuously in a way that cannot naturally be described by chemical reactions. Here, we develop an approach based on transport along a trafficking coordinate to precisely describe these processes and we apply it explicitly to the TGF-β signal transduction network, which plays a fundamental role in many diseases and cellular processes. The results of this newly introduced approach accurately capture for the first time the distinct TGF-β signaling dynamics of cells with and without cancerous backgrounds and provide an avenue to predict the effects of chemical perturbations in a way that closely recapitulates the observed cellular behavior.

q-bio.SC↗

arXiv2011

Far-from-equilibrium processes without net thermal exchange via energy sorting

Many important processes at the microscale require far-from-equilibrium conditions to occur, as in the functioning of mesoscopic bioreactors, nanoscopic rotors, and nanoscale mass conveyors. Achieving such conditions, however, is typically based on energy inputs that strongly affect the thermal properties of the environment and the controllability of the system itself. Here, we present a general class of far-from-equilibrium processes that suppress the net thermal exchange with the environment by maintaining the Maxwell-Boltzmann velocity distribution intact. This new phenomenon, referred to as ghost equilibrium, results from the statistical cancellation of superheated and subcooled nonequilibrated degrees of freedom that are autonomously generated through a microscale energy sorting process. We provide general conditions to observe this phenomenon and study its implications for manipulating energy at the microscale. The results are applied explicitly to two mechanistically different cases, an ensemble of rotational dipoles and a gas of trapped particles, which encompass a great variety of common situations involving both rotational and translational degrees of freedom.

cond-mat.stat-mech↗

arXiv2011

Control of gene expression by modulated self-assembly

Numerous transcription factors self-assemble into different order oligomeric species in a way that is actively regulated by the cell. Until now, no general functional role has been identified for this widespread process. Here we capture the effects of modulated self-assembly in gene expression with a novel quantitative framework. We show that this mechanism provides precision and flexibility, two seemingly antagonistic properties, to the sensing of diverse cellular signals by systems that share common elements present in transcription factors like p53, NF-kappaB, STATs, Oct, and RXR. Applied to the nuclear hormone receptor RXR, this framework accurately reproduces a broad range of classical, previously unexplained, sets of gene expression data and corroborates the existence of a precise functional regime with flexible properties that can be controlled both at a genome-wide scale and at the individual promoter level.

q-bio.MN↗

arXiv2010

Accurate prediction of gene expression by integration of DNA sequence statistics with detailed modeling of transcription regulation

Gene regulation involves a hierarchy of events that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. The effects of DNA sequence on these processes have typically been studied based either on its quantitative connection with single-domain binding free energies or on empirical rules that combine different DNA motifs to predict gene expression trends on a genomic scale. The middle-point approach that quantitatively bridges these two extremes, however, remains largely unexplored. Here, we provide an integrated approach to accurately predict gene expression from statistical sequence information in combination with detailed biophysical modeling of transcription regulation by multidomain binding on multiple DNA sites. For the regulation of the prototypical lac operon, this approach predicts within 0.3-fold accuracy transcriptional activity over a 10,000-fold range from DNA sequence statistics for different intracellular conditions.

q-bio.MN↗

arXiv2010

Noisy-threshold control of cell death

Cellular responses to death-promoting stimuli typically proceed through a differentiated multistage process, involving a lag phase, extensive death, and potential adaptation. Deregulation of this chain of events is at the root of many diseases. Improper adaptation is particularly important because it allows cell sub-populations to survive even in the continuous presence of death conditions, which results, among others, in the eventual failure of many targeted anticancer therapies. Here, I show that these typical responses arise naturally from the interplay of intracellular variability with a threshold-based control mechanism that detects cellular changes in addition to just the cellular state itself. Implementation of this mechanism in a quantitative model for T-cell apoptosis, a prototypical example of programmed cell death, captures with exceptional accuracy experimental observations for different expression levels of the oncogene Bcl-xL and directly links adaptation with noise in an ATP threshold below which cells die. These results indicate that oncogenes like Bcl-xL, besides regulating absolute death values, can have a novel role as active controllers of cell-cell variability and the extent of adaptation.

q-bio.CB↗

arXiv2007

Inconsistency of the non-standard definition of work

We show that the recently postulated non-standard definition of work proportional to force variation rather than to displacement [A. Imparato and L. Peliti, cond-mat arXiv:0706.1134v1] is thermodynamically inconsistent at both microscopic and macroscopic scales and leads to non-physical results, including free energy changes that depend on arbitrary parameters.

cond-mat.stat-mech↗