Biology subjects

Suweis, S.

Publications and source records attributed to Suweis, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Towards a scalable effective connectivity estimation in fMRI

Characterizing directed connections between brain regions is fundamental for understanding the brains network organization and how it supports cognitive processes. However, a reliable methodology allowing assessment of effective connectivity (EC) at the whole-brain level and across high-resolution parcellations has remained elusive. This limitation reflects a trade-off between model detail and scalability. Mechanistic approaches such as Dynamic Causal Modeling (DCM) provide a principled description of directed interactions but become impractical for large parcellations, whereas scalable methods based on linear-system inversion often fail to reliably recover key features of the underlying effective connectivity. Here, using data from the Human Connectome Project S1200 release, we show that the multivariate Ornstein-Uhlenbeck (mOU) approach for EC estimation can be adapted to yield EC estimates that are in broad agreement with those of an advanced DCM, at least when the spatial variability of the hemodynamic response falls within the physiological range characteristic of the healthy adult brain. Furthermore, thanks to its low computational cost, mOU can be easily scaled up to large neuroimaging cohorts and high-resolution parcellations. High-resolution, mOU-based EC outperforms functional connectivity for single-subject connectome fingerprinting. In addition, it enables a high-resolution characterization of the brains hierarchical organization and modular architecture by identifying directed interactions within and between communities and revealing driver and receiver regions. Overall, our methodology provides a reliable and scalable EC estimation pipeline of wide applicability to cognitive and network neuroscience.

neuroscience↗

bioRxiv2026

A Whole-Brain Dynamical Framework Linking Resting-State Activity to TMS-Evoked Responses

A major challenge in systems neuroscience is understanding how external perturbations interact with ongoing brain activity. Transcranial magnetic stimulation (TMS), increasingly used in both basic and clinical neuroscience and often combined with electroencephalography (EEG), provides a unique opportunity to probe this interaction. However, how intrinsic dynamics constrain the propagation of TMS-evoked activity remains poorly understood. In particular, effective connectivity (EC)--capturing directed, state-dependent interactions between brain regions--is thought to critically shape perturbational spread, yet remains difficult to estimate at the whole-brain EEG level. Here we introduce an analytically tractable, generative whole-brain model that links spontaneous EEG activity to cortical responses under perturbation. By deriving a closed-form expression for the models cross-spectral density, we directly fit empirical resting-state EEG spectra and infer biophysically interpretable local dynamical parameters without time-domain simulations. We then estimate stimulation-site-specific EC using only a small fraction of the TMS-EEG trials. The resulting model accurately predicts the spatiotemporal structure of TMS-evoked potentials (TEPs) in unseen trials. Moreover, even without subject-specific refitting, group-level EC templates capture canonical site-specific propagation motifs underlying single-subject early TMS responses. Together, our results establish an analytical framework for individualized whole-brain modeling of TMS-EEG with potential applicability to model-based neuromodulation.

neuroscience↗

bioRxiv2026

Brain-tuning near criticality in newborns by prenatal experience with language

Language development starts early, possibly even in the womb. Recent results suggest that newborns neural responses to speech in the prenatally heard language are already different from those to unfamiliar languages. However, the neural dynamics of how these differential responses emerge remains little understood. Here, we hypothesize that they are supported by a functional tuning to criticality--a state that maximizes information transmission, dynamic range, and flexibility--, induced in newborns by the prenatally experienced language. To test this hypothesis, we study resting-state brain activity before and after stimulation with naturally spoken sentences in the prenatal language, French, in a rhythmically similar unfamiliar language, Spanish, as well as in a rhythmically different unfamiliar language, English. We show that the native language elicits brain tuning near criticality, balancing network activity and enhancing temporal correlations. Importantly, we find that the network state is not sensitive to the rhythmically different language, and only partially responds to the rhythmically similar language. These results indicate a stimulus-driven tuning to criticality in newborns, a potential foundation for early neuroplasticity, which relies on prenatal experience and may respond to early functional and developmental needs.

neuroscience↗

bioRxiv2025

Unraveling the temporal dependence of ecological interaction measures

Species interactions--ranging from direct predator-prey relationships to indirect effects mediated by the environment--are central to ecosystem balance and biodiversity. While empirical methods for measuring these interactions exist, their interpretability and limitations remain unclear. Here we examine the empirical matrix of pairwise interactions, a widely used tool, and analyze its temporal variability. We show that apparent fluctuations in interaction strength--and even shifts in interaction signs, often interpreted as transitions between competition and facilitation--can arise intrinsically from population dynamics with fixed ecological roles. Experimental protocols further shape these estimates: the duration of observation and the type of setup in microbial growth studies (e.g., chemostats, batch cultures, or resource conditions) systematically affect measured interactions. Considering interactions across timescales enhances interpretability: short-term measurements primarily capture direct species couplings, whereas long-term observations increasingly reflect indirect community feedback. Taken together, these results establish short-duration inferences, obtained either directly or extrapolated, as a principled way to disentangle direct from indirect interactions. Building on this insight, we propose a model-inference approach that leverages multiple short time series rather than extended longitudinal datasets.

ecology↗

bioRxiv2025

The somatosensory barrel cortex controls the spindlethalamocortical oscillation by frequency locking

The sleep spindle is a characteristic oscillation typically observed in NREM sleep and anesthesia. It is generated by a closed-loop thalamocortical circuit that is allegedly contributing to thalamocortical gating, sensory processing and memory consolidation. Yet, the circuit intricacy in terms of electrophysiological neuronal properties and connectivity has so far contributed to hinder a clear understanding of its regulation and function. In this study, we experimentally demonstrate that, when driven by the somatosensory cortex, the spindle circuit behaves as a macroscopic single-frequency self-sustained oscillator. We frequency-modulated cortical inputs to the thalamocortical spindle circuit by periodic microstimulation of the barrel cortex in the anesthetized rat. Cortical spindles exhibited synchronization by frequency locking and not resonance, displaying a characteristic Arnold tongue, a hallmark of the self-sustained oscillator. With a rate model of the barrel cortex-thalamus circuit reproducing the oscillator behavior we show that frequency-locking can govern synchronization under whisking. Significance statementTemporal coordination across neurons (neural synchronization) is believed to be a fundamental mechanism underlying brain information processing. Neural synchornization generates oscillatory signals such as waxing and waning, approximately 10 Hz oscillations occurring during sleep and anaesthesia and known as brain spindles. Spindles are generated by a closed-loop circuit between thalamus and cortex but their function remains unknown. We demonstrate that, similarly to a methronome that adjusts its frequency to the one of an external forcing oscillator, the spindle circuit is controlled by the sensory cortex according to a frequency-locking mechanism. We suggest that frequency-locking within the thalmocortical circuitry represents a flexible frequency-adjustable mechanism tuning the processing of sensory inputs by neural synchronization.

neuroscience↗

bioRxiv2025

Structural Connectome Dimension Shapes Brain Dynamics in Health and Disease

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to high-cognitive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

neuroscience↗

bioRxiv2025

Whisker stimulation reinforces a resting-state network in the barrel cortex: nested oscillations and avalanches

The cerebral cortex operates in a state of restless activity, even in the absence of external stimuli. Collective neuronal activities, such as neural avalanches and synchronized oscillations, are also found under rest conditions, and these features have been suggested to support sensory processing, brain readiness for rapid responses, and computational efficiency. The rat barrel cortex and thalamus circuit, with its somatotopic organization for processing sensory inputs from the whiskers, provides a powerful system to explore such interplay. To characterize these resting state circuits, we perform simultaneous multi-electrode recordings in rats barrel cortex and thalamus. During spontaneous activity, oscillations with frequencies centered around 11 Hz are detected concomitantly with slow oscillations below 4 Hz, as well as power-law distributed avalanches. The phase of the lower-frequency oscillation appears to modulate the higher-frequency amplitude, and it has a role in gating avalanche occurrences. We then record neural activity during controlled whisker movements and observe that the 11 Hz barrel circuit active at rest is indeed the one involved in response to whisker stimulation. We finally show how a thalamic-driven firing-rate model can describe the entire phenomenology observed at resting state and predict the response of the barrel cortex to controlled whisker movement, suggesting that the same intrinsic dynamics underlying resting-state activity also shape sensory responses. Author SummaryThe brain is active even in the absence of external stimuli, generating complex patterns of activity that are thought to prepare it for processing incoming information. Two prominent features of this spontaneous activity are rhythmic oscillations and neuronal avalanches--bursts of activity that span a wide range of sizes and durations. However, how these patterns relate to actual sensory processing remains unclear. In this study, we investigated the rat barrel cortex, a well-characterized system for processing tactile information from whiskers. By combining electrophysiological recordings and computational modeling, we found that specific transient oscillations centered around 11 Hz are present during rest and become significantly stronger when the whiskers are stimulated. At the same time, spontaneous activity displays a rich dynamical regime with neuronal avalanches, whose timing is modulated by slower brain rhythms. Importantly, we show that a simple thalamus-driven computational model can reproduce these observations. We thus provide a minimal yet powerful model that suggests that the same rich, intrinsic dynamics underlying resting-state activity also shape sensory responses. Our results support the idea that spontaneous brain activity is not idle, but instead reflects an organized dynamical regime that facilitates efficient processing of sensory inputs.

neuroscience↗

bioRxiv2024

Unveiling Gene Perturbation Effects through Gene Regulatory Networks Inference from single-cell transcriptomic data

1Physiological and pathological processes are governed by a network of genes called gene regulatory networks (GRNs). By reconstructing GRNs, we can accurately model how cells behave in their natural state and predict how genetic changes will affect them. Transcriptomic data of single cells are now available for a wide range of cellular processes in multiple species. Thus, a method building predictive GRNs from single-cell RNA sequencing (scRNA-seq) data, without any additional prior knowledge, could have a great impact on our understanding of biological processes and the genes playing a key role in them. To this aim, we developed IGNITE (Inference of Gene Networks using Inverse kinetic Theory and Experiments), an unsupervised machine learning framework designed to infer directed, weighted, and signed GRNs directly from unperturbed single-cell RNA sequencing data. IGNITE uses the GRNs to generate gene expression data upon single and multiple genetic perturbations. IGNITE is based on the inverse problem for a kinetic Ising model, a model from statistical physics that has been successfully applied to biological networks. We tested IGNITE on murine pluripotent stem cells (PSCs) transitioning from the naive to formative states. Using as input only scRNA-seq data of unperturbed PSCs, IGNITE simulated single and triple gene knockouts. Comparison with experimental data revealed high accuracy, up to 74%, outperforming currently available methods. In sum, IGNITE identifies predictive GRNs from scRNA-seq data without additional prior knowledge and faithfully simulates single and multiple gene perturbations. Applications of IGNITE range from studying cell differentiation to identifying genes specifically active under pathological conditions.

systems biology↗

bioRxiv2023

EEG microstate transition cost correlates with task demands

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the frameworks effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain. Author SummaryIn our daily lives, our brains manage various tasks with different mental demands. Yet, quantifying how much mental effort each task demands is not always straightforward. To tackle this challenge, we developed a way to measure how much cognitive effort our brains use during tasks directly from electroencephalography (EEG) data, which is one of the most used tools to non-invasively measure brain activity. Our approach involved the identification of distinct patterns of synchronized neural activity across the brain, named EEG microstates. By employing optimal transport theory, we established a framework to quantify the cost associated with cognitive transitions based on modifications in EEG microstates. This allowed us to link changes in brain activity patterns to the cognitive effort required for task performance. To validate our framework, we applied it to EEG data collected during a commonly employed cognitive task known as the Stroop task. This task is recognized for challenging us with varying levels of cognitive demand. Our analysis revealed that as the task became more demanding, there were discernible shifts in the EEG microstates. Importantly, these shifts in neural activity patterns corresponded to higher costs associated with cognitive transitions. Our approach offers a promising methodology to assess cognitive effort using neural data, contributing to our comprehension of how the brain manages and adapts to varying cognitive challenges.

neuroscience↗

bioRxiv2023

Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease

Recent advancements in next-generation sequencing have revolutionized our understanding of the human microbiome. Despite this progress, challenges persist in comprehending the microbiomes influence on disease, hindered by technical complexities in species classification, abundance estimation, and data compositionality. At the same time, recently the existence of macroecological laws describing the variation and diversity in microbial communities irrespective of their environment has been proposed using 16s data and explained by a simple phenomenological model of population dynamics. We here investigate the relationship between dysbiosis, i.e. in unhealthy individuals there are deviations from the "regular" composition of the gut microbial community, and the existence of macro-ecological emergent law in microbial communities. We first quantitatively reconstruct these patterns at the species level using shotgun data, offering a more biologically interpretable approach, and addressing the consequences of sampling effects and false positives on ecological patterns. We then ask if such patterns can discriminate between healthy and unhealthy cohorts. Concomitantly, we evaluate the efficacy of different population models, which incorporate sampling and different ecological and statistical principles (e.g., the Taylors law and environmental noise) to describe such patterns. A critical aspect of our analysis is understanding the relationship between model parameters, which have clear ecological interpretations, and the state of the gut microbiome, thereby enabling the generation of synthetic compositional data that distinctively represent healthy and unhealthy individuals. Our approach, grounded in theoretical ecology and statistical physics, allows for a robust comparison of these models with empirical data, enhancing our understanding of the strengths and limitations of simple microbial models of population dynamics. Author summaryIn this study, we explore emerging ecological properties in gut microbiomes. Our aim here is to determine whether these patterns can be informative of the gut microbiome (healthy or diseased) and unveil essential ingredients driving its population dynamics. Leveraging on phenomenological models of species abundance fluctuations and metagenomics data, we highlight the pivotal role of Taylors law, a straightforward mathematical relation, in constructing theoretical models for the human gut microbiome. We thus explore such a general theoretical framework for investigating microbiome composition and show that not all ecological patterns are informative to characterize its states, while few are (e.g., species diversity). Eventually, thanks to the ecological interpretability of the inferred models parameters, our analysis provides insights into the role of environmental fluctuations and carrying capacities of the gut microbiomes in both health and disease. This study offers valuable knowledge, bridging theoretical concepts with practical implications for human health.

ecology↗

bioRxiv2023

Synthetic Eco-Evolutionary Dynamics in Simple Molecular Environment

The understanding of eco-evolutionary dynamics, and in particular the mechanism of coexistence of species, is still fragmentary and in need of test bench model systems. To this aim we developed a variant of SELEX in-vitro selection to study the evolution of a population of [~] 1015 single-strand DNA oligonucleotide individuals. We begin with a seed of random sequences which we select via affinity capture from [~] 1012 DNA oligomers of fixed sequence ( resources) over which they compete. At each cycle ( generation), the ecosystem is replenished via PCR amplification of survivors. Massive parallel sequencing indicates that across generations the variety of sequences ( species) drastically decreases, while some of them become populous and dominate the ecosystem. The simplicity of our approach, in which survival is granted by hybridization, enables a quantitative investigation of fitness through a statistical analysis of binding energies. We find that the strength of individual-resource binding dominates the selection in the first generations, while inter and intra-individual interactions become important in later stages, in parallel with the emergence of prototypical forms of mutualism and parasitism.

ecology↗

bioRxiv2023

Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inaccurate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

neuroscience↗

bioRxiv2023

Prenatal experience with language shapes the brain

Human infants acquire language with striking ease compared to adults, but the neural basis of their remarkable brain plasticity for language remains little understood. Applying a scaling analysis of neural oscillations for the first time to address this question, we show that newborns electrophysiological activity exhibits increased long-range temporal correlations after stimulation with speech, particularly in the prenatally heard language, indicating the early emergence of brain specialization for the native language.

neuroscience↗

bioRxiv2023

A network-based method for extracting the organization of brain-wide circuits from reconstructed connectome datasets

Functional brain activity is supported by specific circuit wiring diagrams. When the synaptic annotation is available, the analysis of high-resolution connectomes allows for unraveling the circuit architectures. Despite the continuous technological and computational improvements, obtaining a whole-brain bauplan based on synaptic annotation remains a demanding effort. As an alternative, we present here an approach to extract an approximated brain connectome starting from libraries of single cell anatomical reconstructions aligned on the same anatomical reference brain. Our approach relies on the identification of neurite terminal nodes based on Strahler numbering of the cell morphology, and the adoption of a proximity range criterion, so as to infer, in the absence of synapse information, approximated brain network architectures. As an initial benchmark we used information theory metrics to confront our approach against a synaptically-annotated EM dataset of Drosophila melanogaster hemibrain. The comparison with our approach revealed a general agreement in the organization of the extracted connectivity modules measured in terms of Normalized Mutual Information, Adjusted Rand Index and Pearson correlation. Moreover, we show that the modules identified, along with their organization, can capture known circuit motives. We then applied this approach to a light microscopy dataset of the zebrafish larval brain composed of about 3000 neuronal skeletonizations. We show that the approximated connectome and the resulting modular organization is capable of capturing specific and topographically organized connection patterns as well as known functional circuit architectures. In conclusion, we present a scalable, from-circuit-to-brain range, approach to reveal approximated neuronal architectures supporting brain mechanisms, potentially suitable for hypothesis generation and for guiding the exploration of and integration with EM connectomes. Author SummaryUnderstanding how the brain works requires detailed maps of its neural connections, known as connectomes. While advanced techniques like electron microscopy (EM) can map these connections at the level of individual synapses, they are time consuming and resource intensive, limiting their use. As an alternative, we developed a computational method that approximates brain connectivity using existing datasets of neuron shapes (morphologies) without requiring synaptic annotations. Our approach identifies potential connections between neurons based on the proximity of their terminal branches regions where synapses are likely to form within a shared 3D reference brain. We validated our method using a high-resolution EM connectome of the fruit fly brain, demonstrating that it captures broad organizational patterns, such as clusters of densely interconnected neurons, despite moderate agreement at the synaptic level. Applying the method to a light microscopy dataset of the zebrafish larva brain ([~]3,000 neurons), we successfully reconstructed large-scale networks that recapitulated known functional circuits. This approach offers a scalable way to extract brain-wide connectivity principles from existing datasets, bridging the gap between cellular anatomy and circuit function. It can guide targeted experiments and complement future EM studies, making neuroscience data more accessible for hypothesis generation.

neuroscience↗

bioRxiv2022

Criticality and network structure drive emergent oscillations in a stochastic whole-brain model

Understanding the relation between the structure of brain networks and its functions is a fundamental open question. Simple models of neural activity based on real anatomical networks have proven to be effective in describing features of whole-brain spontaneous activity when tuned at their critical point. In this work, we show that indeed structural networks are a crucial ingredient in the emergence of collective oscillations in a whole-brain stochastic model at criticality. We study analytically a stochastic Greenberg-Hastings cellular automaton in the mean-field limit, showing that it undergoes an abrupt phase transition with a bistable region. In particular, no global oscillations emerge in this limit. Then, we show that by introducing a network structure in the homeostatic normalization regime, the bistability may be disrupted, and the transition may become smooth. Concomitantly, through an interplay between the network topology and weights, a large peak in the power spectrum appears around the transition point, signaling the emergence of collective oscillations. Hence, both the structure of brain networks and criticality are fundamental in driving the collective responses of whole-brain stochastic models.

neuroscience↗

bioRxiv2021

Beyond resting state neuronal avalanches in the somatosensory barrel cortex

Since its first experimental signatures, the so called critical brain hypothesis has been extensively studied. Yet, its actual foundations remain elusive. According to a widely accepted teleological reasoning, the brain would be poised to a critical state to optimize the mapping of the noisy and ever changing real-world inputs, thus suggesting that primary sensory cortical areas should be critical. We investigated whether a single barrel column of the somatosensory cortex of the anesthetized rat displays a critical behavior. Neuronal avalanches were recorded across all cortical layers in terms of both spikes and population local field potentials, and their behavior during spontaneous activity compared to the one evoked by a controlled single whisker deflection. By applying a maximum likelihood statistical method based on timeseries undersampling to fit the avalanches distributions, we show that neuronal avalanches are power law distributed for both spikes and local field potentials during spontaneous activity, with exponents that are spread along a scaling line. Instead, after the tactile stimulus, activity switches to an across-layers synchronization mode that appears to dominate during cortical representation of the single sensory input.

neuroscience↗

bioRxiv2020

Recovery of neural dynamics criticality in personalized whole brain models of stroke

The critical brain hypothesis states that biological neuronal networks, because of their structural and functional architecture, work near phase transitions for optimal response to internal and external inputs. Criticality thus provides optimal function and behavioral capabilities. We test this hypothesis by examining the influence of brain injury (strokes) on the criticality of neural dynamics estimated at the level of single participants using directly measured individual structural connectomes and whole-brain models. Lesions engender a sub-critical state that recovers over time in parallel with behavior. The improvement of criticality is associated with the re-modeling of specific white matter connections. We show that personalized whole-brain dynamical models poised at criticality track neural dynamics, alteration post-stroke, and behavior at the level of single participants.

neuroscience↗

bioRxiv2020

Statistical Physics of DNA melting: Unveiling the artificial corrections for self-complementary sequences

DNA hybridization is at the heart of countless biological and biotechnological processes. Its theoretical modeling played a crucial role, since it has enabled extracting the relevant thermodynamic parameters from systematic measurements of DNA melting curves. However, in its current state, hybridization modelling requires introducing an extra entropic contribution in self-complementary sequences that lacks any biophysical meaning. In this article, we propose a framework based on statistical physics to describe DNA hybridization and melting in an arbitrary mixture of DNA strands. In particular, we are able to analytically derive closed expressions of the system partition functions for any number N of strings, and explicitly calculate them in two paradigmatic situations: (i) a system made of self-complementary sequences and (ii) a system comprising two mutually complementary sequences. We derive the melting curve in the thermodynamic limit (N [->] {infty}) of our description, which differs from the expression commonly used to evaluate the melting of self-complementary systems in that it does not require correcting terms. We provide a thorough study comprising limit cases and alternative approaches showing how our framework can give a comprehensive view of hybridization and melting phenomena. SIGNIFICANCEIn this study, we provide a transparent derivation of the melting curves of DNA duplexes using basic tools of statistical mechanics. We find that in the case of self-complementary sequences, our expression differs from the one used in literature, which is generally amended by the introduction of a phenomenological correction which in our approach becomes unnecessary. By offering a clean formal description of DNA hybridization, our approach sharpens our understanding of DNA interactions and opens the way to study the pairing of DNA oligomers away from any thermodynamic limit.

biophysics↗