Biology subjects

Barahona, M.

Publications and source records attributed to Barahona, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Simulation-free approximate Bayesian computation for stochastic reaction networks

Uncovering stochastic reaction networks from data is central to single-cell biology. Simulation-based inference has evolved into a flexible paradigm for this purpose, circumventing the computational intractability of the likelihood. Notably, approximate Bayesian computation performs this inference by carrying out extensive Gillespie simulations, usually at a high computational cost. Here, we propose a simulation-free approximate Bayesian computation (SFABC) framework that avoids both simulation and explicit likelihood solutions of the network dynamics. Instead, SFABC exploits the Chemical Master Equation to formulate a tractable convex optimization with ensuing constraints, which are then used to accept sampled parameters consistent with the observed data. The resulting posterior approximation comes with theoretical guarantees: it can be tightened systematically, and it provably converges to the exact Bayesian posterior distributions. Using synthetic benchmarks, we show that SFABC matches the accuracy of standard simulation-based algorithms at a substantially reduced computational cost. We then showcase the applicability of SFABC to experimental systems biology by analyzing gene transcription bursting kinetics from snapshot, time-resolved, and cell-cycle-resolved single-cell data.

systems biology↗

bioRxiv2026

Arousal-informed neuronal avalanches operate near criticality

Cortical networks are thought to operate near a "critical point" that optimizes information processing, but this has been difficult to test at single-cell resolution across large brain areas, as narrow fields of view miss overlapping neural avalanches, causing subcritical estimates, Using mesoscopic two-photon calcium imaging in awake mouse visual cortex, we recorded thousands of neurons and applied convolutional non-negative matrix factorization to decompose population activity into distinct, overlapping spatiotemporal avalanches. Rather than occupying a single dynamical state, we find that the cortex hosts multiple overlapping subnetworks distributed across a phase diagram at varying distances from the critical point. Arousal-linked avalanches propagate widely and operate nearest the critical point, showing optimized dynamic range, minimized algorithmic complexity, and maximized temporal entropy. Our results reveal how the awake brain tunes parallel near-critical subnetworks to support robust sensory computation.

neuroscience↗

bioRxiv2025

Synergy mediates Long-Range Correlations in the Visual Cortex Near Criticality

Long-range correlations are a key signature of systems operating near criticality, indicating spatially-extended interactions across large distances. These extended dependencies underlie other emergent properties of critical dynamics, such as high susceptibility and multi-scale coordination. In the brain, along with other signatures of criticality, long-range correlations have been observed across various spatial scales, suggesting that the brain may operate near a critical point to optimise information processing and adaptability. However, the mechanisms underlying these long-range correlations remain poorly understood. Here, we investigate the role of synergistic interactions in mediating long-range correlations in the visual cortex of awake mice. We leverage recent advances in mesoscale two-photon calcium imaging to analyse the activity of thousands of neurons across a wide field of view, allowing us to confirm the presence of long-range correlations at the level of neuronal populations. By applying the Partial Information Decomposition (PID) framework, we decompose the correlations into synergistic and redundant information interactions. Our results reveal that the increase in long-range correlations during visual stimulation is accompanied by a significant increase in synergistic rather than redundant interactions among neurons. Furthermore, we analyse a combined network formed by the union of synergistic and redundant interaction networks, and find that both types of interactions complement each other to facilitate efficient information processing across long distances. This complementarity is further enhanced during the visual stimulation. These findings provide new insights into the computational mechanisms that give rise to long-range correlations in neural systems and highlight the importance of considering different types of information interactions in understanding correlations in the brain.

neuroscience↗

bioRxiv2025

Label-free biochemical imaging and timepoint analysis of neural organoids via deep learning-enhanced Raman microspectroscopy

Three-dimensional organoids have emerged as powerful models for studying human development, disease and drug response in vitro. Yet, their analysis remains constrained by standard imaging and characterisation techniques, which are invasive, require exogenous labelling and offer limited multiplexing. Here, we present a non-invasive, label-free imaging platform that integrates Raman microspectroscopy with deep learning-based hyperspectral unmixing for unsupervised, spatially resolved biochemical analysis of neural organoids. Our approach enables high-resolution mapping of cellular and subcellular structures in both cryosectioned and intact organoids, achieving improved imaging accuracy and robustness compared to conventional methods for hyperspectral analysis. Using our platform, we demonstrate volumetric imaging of a neural rosette within a neural organoid, and interrogate changes in biochemical composition during early developmental stages in intact neural organoids, revealing spatiotemporal variations in lipids, proteins and nucleic acids. This work establishes a versatile framework for high-content, label-free (bio)chemical phenotyping with broad applications in organoid research and beyond.

bioinformatics↗

bioRxiv2024

The exit of naïve pluripotency contains a lipid metabolism-induced checkpoint for genome integrity.

Pluripotent progenitors undergo dramatic cellular and biochemical transformations during peri-implantation development. These large-scale reprogramming events are fundamental for subsequent differentiation, but how they are integrated and co-ordinated with the preservation of genome integrity remain unknown. Here, we uncover a metabolism-induced telomere checkpoint that takes place in pluripotent progenitors as they form rosette-like epithelial structures. We show that the glycolytic switch at the exit of naive pluripotency is preceded by an acceleration of mitochondrial respiration and de novo lipogenesis, fuelling the accumulation of lipid droplets required for morphogenesis. We find that downstream of these CIDEA-promoted metabolic events is the induction of ZSCAN4, a key pluripotency-associated regulator of telomere stability. Surprisingly, the build-up of lipid droplets corresponds to a transient shortening of telomeres, which triggers the activation of an elongation mechanism via ZSCAN4. Thus, telomere homeostasis can be safeguarded as essential lipid metabolic reprogramming unfolds to drive developmental progression.

developmental biology↗

bioRxiv2023

ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning

The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a Convolutional Neural Network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of Normal Mode correlation maps derived from Elastic Network Models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the Normal Mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.

bioinformatics↗

bioRxiv2023

RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis

Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python.

bioinformatics↗

bioRxiv2023

The neuron mixer and its impact on human brain dynamics

A signal mixer made of a transistor or a diode facilitates rich computation, which has been the building block of modern telecommunications. The mixing produces new signals at the sum and difference frequencies of the input, thereby enabling powerful operations such as frequency conversion (aka heterodyning), phase detection, and multiplexing. Here, we report that a neural cell is also a signal mixer. We found through ex-vivo and in-vivo whole-cell measurements that neurons mix exogenous (controlled) and endogenous (spontaneous) subthreshold membrane potential oscillations, producing new oscillation frequencies. We show, using pharmacological manipulation, that the neural mixing originates in the voltage-gated ion channels. Furthermore, we demonstrate that the neural mixing dynamic is evident in human brain activity and is associated with cognitive functions. We found that the human electroencephalogram (EEG) displays distinct clusters of local and inter-region mixing interactions. By quantifying the strength of these interactions before a task, we show that converting the salient posterior alpha-beta oscillations into gamma-band oscillations regulates the visual attention state. Neural circuit oscillations have been observed in nearly every cognitive domain and species, and abnormal spectra of neural oscillations have been found in almost all brain disorders. Signal mixing enables individual neurons to actively sculpt the spectrum of their circuit oscillations and utilize them for computational operations, which have only been seen in modern telecommunication until now.

neuroscience↗

bioRxiv2022

Prediction of gene essentiality using machine learning and genome-scale metabolic models

The identification of essential genes, i.e. those that impair cell survival when deleted, requires large growth assays of knock-out strains. The complexity and cost of such experiments has triggered a growing interest in computational methods for gene essentiality prediction. In the case of metabolic genes, Flux Balance Analysis (FBA) is widely employed to predict essentiality under the assumption that cells maximize their growth rate. However, this approach implicitly assumes that knock-out strains optimize the same objectives as the wild-type, which excludes cases in which deletions cause large changes in cell physiology to meet other objectives for survival. Here we resolve this limitation with a novel machine learning approach that predicts essentiality directly from wild-type flux distributions. We first project the wild-type FBA solution onto a mass flow graph, a digraph with reactions as nodes and edge weights proportional to the mass transfer between reactions, and then train binary classifiers on the connectivity of graph nodes. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli, achieving near state-of-the art prediction accuracy for essential genes. Our approach suggests that wild-type FBA solutions contain enough information to predict essentiality, without the need to assume optimality of deletion strains.

systems biology↗

bioRxiv2021

Long- and short-term history effects in a spiking network model of statistical learning

The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Existing spiking network models implementing sampling lack the ability to learn the statistical structure from observed stimuli and instead often hard-code a dynamics. Here, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.

neuroscience↗

bioRxiv2021

Dimensionality reduction and data integration for scRNA-seq data based on integrative hierarchical Poisson factorisation

Single-cell RNA sequencing (scRNA-seq) data sets consist of high-dimensional, sparse and noisy feature vectors, and pose a challenge for classic methods for dimensionality reduction. Such problems are compounded when dealing with composite data sets formed by different batches. We introduce Integrative Hierarchical Poisson Factorisation (IHPF), an extension of HPF that makes use of a noise ratio hyper-parameter to tune the variability attributed to batches vs. biological sources (cell phenotypes). We exemplify the application of IHPF under different data integration scenarios with varying alignments of batches and cell diversity, and show that IHPF produces latent factors that can be advantageously applied for cell clustering and visualisation. In addition, the extracted factors have a dual block structure in both cell and gene spaces with enhanced biological interpretability.

bioinformatics↗

bioRxiv2021

Similarity Measure for Sparse Time Course Data Based on Gaussian Processes

We propose a similarity measure for sparsely sampled time course data in the form of a loglikelihood ratio of Gaussian processes (GP). The proposed GP similarity is similar to a Bayes factor and provides enhanced robustness to noise in sparse time series, such as those found in various biological settings, e.g., gene transcriptomics. We show that the GP measure is equivalent to the Euclidean distance when the noise variance in the GP is negligible compared to the noise variance of the signal. Our numerical experiments on both synthetic and real data show improved performance of the GP similarity when used in conjunction with two distance-based clustering methods.

bioinformatics↗

bioRxiv2020

Allosteric hotspots in the main protease of SARS-CoV-2

AO_SCPLOWBSTRACTC_SCPLOWInhibiting the main protease of SARS-CoV-2 is of great interest in tackling the COVID-19 pandemic caused by the virus. Most efforts have been centred on inhibiting the binding site of the enzyme. However, considering allosteric sites, distant from the active or orthosteric site, broadens the search space for drug candidates and confers the advantages of allosteric drug targeting. Here, we report the allosteric communication pathways in the main protease dimer by using two novel fully atomistic graph theoretical methods: Bond-to-bond propensity analysis, which has been previously successful in identifying allosteric sites without a priori knowledge in benchmark data sets, and, Markov transient analysis, which has previously aided in finding novel drug targets in catalytic protein families. We further score the highest ranking sites against random sites in similar distances through statistical bootstrapping and identify four statistically significant putative allosteric sites as good candidates for alternative drug targeting.

bioinformatics↗

bioRxiv2020

Computation of single-cell metabolite distributions using mixture models

Metabolic heterogeneity is widely recognised as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

systems biology↗

bioRxiv2020

hcga: Highly Comparative Graph Analysis for network phenotyping

AO_SCPLOWBSTRACTC_SCPLOWNetworks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how HCGA can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. HCGA is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.

systems biology↗

bioRxiv2020

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

neuroscience↗

bioRxiv2020

Non-invasive Amelioration of Essential Tremor via Phase-Locked Disruption of its Temporal Coherence

Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be noninvasively suppressed via electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained after the end of the stimulation and can be phenomenologically predicted. Finally, using feature-based statistical-learning and neurophysiological-modelling we show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the oscillation via perturbation of the tremor generating a cascade of synchronous activity in the olivocerebellar loop. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may represent a powerful neuromodulatory strategy to treat brain disorders.

neuroscience↗

bioRxiv2019

Unsupervised graph-based learning predicts mutations that alter protein dynamics

AO_SCPLOWBSTRACTC_SCPLOWProteins exhibit complex dynamics across a vast range of time and length scales, from the atomistic to the conformational. Adenylate kinase (ADK) showcases the biological relevance of such inherently coupled dynamics across scales: single mutations can affect large-scale protein motions and enzymatic activity. Here we present a combined computational and experimental study of multiscale structure and dynamics in proteins, using ADK as our system of choice. We show how a computationally efficient method for unsupervised graph partitioning can be applied to atomistic graphs derived from protein structures to reveal intrinsic, biochemically relevant substructures at all scales, without re-parameterisation or a priori coarse-graining. We subsequently perform full alanine and arginine in silico mutagenesis scans of the protein, and score all mutations according to the disruption they induce on the large-scale organisation. We use our calculations to guide Forster Resonance Energy Transfer (FRET) experiments on ADK, and show that mutating residue D152 to alanine or residue V164 to arginine induce a large dynamical shift of the protein structure towards a closed state, in accordance with our predictions. Our computations also predict a graded effect of different mutations at the D152 site as a result of increased coherence between the core and binding domains, an effect confirmed quantitatively through a high correlation (R2 = 0.93) with the FRET ratio between closed and open populations measured on six mutants.

biophysics↗