Mauricio Barahona

Simulation-free approximate Bayesian computation for stochastic reaction networks

This paper introduces SFABC, a simulation-free method for Bayesian inference of stochastic reaction networks, showing it matches simulation-based accuracy at lower computational cost.

Arousal-informed neuronal avalanches operate near criticality

The paper uses calcium imaging and computational methods to show that cortical networks operate near criticality through arousal-linked neuronal avalanches.

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

This paper develops a label-free imaging platform using Raman microspectroscopy and deep learning to analyze neural organoids, revealing spatiotemporal biochemical changes.

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

The paper investigates synergistic interactions mediating long-range correlations in the visual cortex of awake mice during visual stimulation.

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

The paper identifies a lipid metabolism-induced checkpoint during the exit of naïve pluripotency that ensures genome integrity through telomere regulation.

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

ANTIPASTI predicts antibody binding affinity using Normal Mode correlation maps and a Convolutional Neural Network, revealing structural and energetic patterns that influence binding.

The neuron mixer and its impact on human brain dynamics

The paper identifies neurons as signal mixers that generate new oscillation frequencies, showing their role in cognitive functions and brain disorders.

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

RamanSPy provides an open-source Python package to streamline Raman spectroscopy analysis, offering tools for integrative data workflows and algorithm development.

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

This paper presents a spiking network model that learns and recollects statistical structure from stimuli, showing sensory history effects through unsupervised learning.

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

The paper identifies four statistically significant putative allosteric sites in the SARS-CoV-2 main protease using graph theoretical methods.

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

This paper predicts gene essentiality using machine learning on wild-type flux distributions, achieving near state-of-the-art accuracy for Escherichia coli.

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

This paper introduces IHPF, a method for dimensionality reduction and data integration in scRNA-seq, showing improved clustering and visualization through batch-aware latent factors.

hcga: Highly Comparative Graph Analysis for network phenotyping

The paper introduces HCGA, a framework for analyzing graph data by computing thousands of graph features and using statistical learning tools to improve classification and discovery.

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

This paper introduces a hierarchical spiking neuron network model for sequence learning across multiple time scales, showing faster learning and better relearning compared to serial models.

Similarity Measure for Sparse Time Course Data Based on Gaussian Processes

The paper introduces a similarity measure for sparse time course data using Gaussian processes, showing improved clustering performance on synthetic and real data.

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

This paper demonstrates a method to suppress essential tremor by disrupting neural oscillation coherence through phase-locked electrical stimulation of the cerebellum.

Computation of single-cell metabolite distributions using mixture models

The paper introduces a method to predict single-cell metabolite distributions using Gaussian mixture models, bypassing stochastic simulations by leveraging enzyme kinetics and expression data.

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

The paper introduces a spiking recurrent network model that learns spatiotemporal sequences using biologically-plausible learning rules and demonstrates its ability to replay learned sequences.

Inferring high-dimensional pathways of trait acquisition in evolution and disease

This paper introduces a statistical method to infer dynamic pathways of trait acquisition in evolution and disease using HyperTraPS, applied to ovarian cancer and tuberculosis resistance.

Stochastic modelling reveals mechanisms of metabolic heterogeneity

This study uses a stochastic model to show that metabolic heterogeneity arises from enzyme expression and catalysis fluctuations, leading to metabolite distributions that differ from deterministic models.

Flux-dependent graphs for metabolic networks

The paper introduces flux-dependent graphs to represent metabolic networks, showing how they change under different environmental and genetic conditions.

Computational Re-Design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation

This work computationally redesigns synthetic genetic oscillators to enable independent amplitude and frequency modulation using tuneable degradation and additional input signals.

Modelling the Dynamics of Biological Systems with the Geometric Hidden Markov Model

This paper introduces the geometric hidden Markov model (GHMM) to capture low-dimensional dynamics of biological processes from time series data, showing it can predict oscillatory behavior in a synthetic biomolecular circuit.

GlnK facilitates the dynamic regulation of bacterial nitrogen assimilation

The paper investigates how GlnK and GlnB regulate glutamine synthetase activity in E. coli during nitrogen starvation and upshift using experiments and a biochemical model.

Long-term, layer-specific reverberant activity in the mouse somatosensory cortex following sensory stimulation

This study shows that multi-whisker stimulation of mouse vibrissae increases long-term, layer-specific spontaneous activity in the somatosensory cortex, with effects specific to layers IV and Vb.

SC3 - consensus clustering of single-cell RNA-Seq data

SC3 improves scRNA-seq clustering by integrating multiple methods, outperforming existing tools on twelve datasets and analyzing 44,808 cells.

Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

The paper introduces a graph-theoretical method to predict allosteric sites and their mediating interactions by analyzing bond-to-bond propensities in proteins.