Biology subjects

Garcia-Ojalvo, J.

Publications and source records attributed to Garcia-Ojalvo, J..

7 recordsLinked to original sources

bioRxiv2018

Electrical-charge accumulation enables integrative quality control during B. subtilis sporulation

Quality control of offspring is important for the survival of cells. However, the mechanism by which quality of offspring cells may be monitored while running genetic programs of cellular differentiation remains largely unclear. Here we investigated a quality control system during Bacillus subtilis spore formation by combining single-cell time-lapse microscopy, molecular biology and mathematical modelling. Our results revealed that the quality-control system via premature germination is coupled with the accumulation of cations on the surface of developing forespores. Specifically, the forespores accumulating less cations on their surface are more likely to be aborted. This charge accumulation system enables the projection of multidimensional information about the external environment and morphological development of the forespore onto a one-dimensional information of cation accumulation. Based on the insight we gain, we propose a novel use of Nernstian chemicals for reducing the yield and quality of Bacillus endospores.

biophysics↗

bioRxiv2018

Bistable emergence of oscillations in structured cell populations

Biofilm communities of Bacillus subtilis bacteria have recently been shown to exhibit collective growth-rate oscillations mediated by electrochemical signaling to cope with nutrient starvation. These oscillations emerge once the colony reaches a large enough number of cells. However, it remains unclear whether the amplitude of the oscillations, and thus their effectiveness, builds up over time gradually, or if they can emerge instantly with a non-zero amplitude. Here we address this question by combining microfluidics-based time-lapse microscopy experiments with a minimal theoretical description of the system in the form of a delay-differential equation model. Analytical and numerical methods reveal that oscillations arise through a subcritical Hopf bifurcation, which enables instant high amplitude oscillations. Consequently, the model predicts a bistable regime where an oscillating and a non-oscillating attractor coexist in phase space. We experimentally validate this prediction by showing that oscillations can be triggered by perturbing the media conditions, provided the biofilm size lies within an appropriate range. The model also predicts that the minimum size at which oscillations start decreases with stress, a fact that we also verify experimentally. Taken together, our results show that collective oscillations in cell populations can emerge suddenly with non-zero amplitude via a discontinuous transition.

systems biology↗

bioRxiv2018

Hypoarousal non-stationary ADHD biomarker based on echo-state networks

Attention-Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity and impulsivity. It is one of the most commonly diagnosed neurodevelopmental and psychiatric disorders of childhood and therefore presents a very high prevalence rate. However the high rate of ADHD misdiagnosis makes the discovery of neurophysiological ADHD biomarkers an important clinical challenge. This study proposes a novel non-stationary ADHD biomarker based on Echo State Networks to quantify EEG dynamical changes between low attention/arousal states (resting with eyes closed, or EC) and normal attention/arousal states (resting with eyes open, or EO). Traditionally, EEG biomarkers have revealed an increase in stationary power in the theta band along with a decrease in beta, with these frequencies largely accepted to be altered in the ADHD population. We successfully verify the hypothesis that measured differences between these two conditions are altered in the ADHD population. Statistically significant differences between a group of ADHD subjects and an aged-matched control population were obtained in theta and beta rhythms. Our network discriminates between EO/EC EEG regimes in the ADHDs better than in controls, suggesting that differences in EEG patterns between low and normal arousal/attention states are larger in the ADHD population.

bioengineering↗

bioRxiv2018

Echo State Networks Ensemble for SSVEP Dynamical Online Detection

BackgroundRecent years have witnessed an increased interest in the use of steady state visual evoked potentials (SSVEPs) in brain computer interfaces (BCI), SSVEP is considered a stationary brain process that appears when gazing at a stimulation light source.\n\nNew MethodsThe complex nature of brain processes advocates for non-linear EEG analysis techniques. In this work we explore the use of an Echo State Networks (ESN) based architecture for dynamical SSVEP detection.\n\nResultsWhen simulating a 6-degrees of freedom BCI system, an information transfer rate of 49bits/min was achieved. Detection accuracy proved to be similar for observation windows ranging from 0.5 to 4 seconds.\n\nComparison with existing methodsSSVEP detection performance has been compared to standard canonical correlation analysis (CCA). CCA achieved a maximum information transfer rate of 21 bits/minute. In this case detection accuracy increased along with the observation window length\n\nConclusionsAccording to here presented results ESN outperforms standard canonical correlation and has proved to require shorter observation time windows. However ESN and CCA approaches delivered diverse classification accuracies at subject level for various stimulation frequencies, proving to be complementary methods. A possible explanation of these results may be the occurrence of evoked responses of different nature, which are then detected by different approaches. While reservoir computing methods are able to detect complex dynamical patterns and/or complex synchronization among EEG channels, CCA exclusively captures stationary patterns. Therefore, the ESN-based approach may be used to extend the definition of steady-state response, considered so far a stationary process.\n\nHighlightsO_LIWe present a novel SSVEP dynamical detection approach based on ESN.\nC_LIO_LIThis is the first time ESNs are applied to SSVEP based BCI systems.\nC_LIO_LIWe provide experimental validation of proposed methodology.\nC_LIO_LIExperimental results indicate non-stationarity in SSVEP patterns.\nC_LI

bioengineering↗

bioRxiv2017

Extracranial estimation of neural mass model parameters using the Unscented Kalman Filter

Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Arys model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method.\n\nAuthor SummaryTo completely understand brain function, we will need to integrate experimental information into a consistent theoretical framework. Invasive techniques as EcoG recordings, together with models that describe the brain at the mesoscale, provide valuable information about the brain state and its dynamical evolution when combined with techniques coming from control theory, such as the Kalman filter. This method, which is specifically designed to deal with systems with noisy or imperfect data, combines experimental data with theoretical models assuming Bayesian inference. So far, implementations of the Kalman filter have not been suited for non-invasive measures like EEG. Here we attempt to overcome this situation by introducing a model of the head that allows to transfer the intracranial signals produced by a mesoscopic model to the scalp in the form of EEG recordings. Our results show the advantages of using multichannel EEG recordings, which are extended in space and allow to discriminate signals produced by the interaction of coupled columns. The extension of the Kalman method presented here can be expected to expand the applicability of the technique to all situations where EEG recordings are used, including the routine monitoring of illnesses or rehabilitation tasks, brain-computer interface protocols, and transcranial stimulation.

neuroscience↗

bioRxiv2017

Collective excitability in a mesoscopic neuronal model of epileptic activity

The brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we study the joint dynamics of two cortical columns described by Jansen-Rit neural mass models, and show that coupling between the columns gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.

neuroscience↗

bioRxiv2016

Impact of Zygosity in Bimodal Phenotype Distributions

Allele number, or zygosity, is a clear determinant of gene expression in diploid cells. But the relationship between the number of copies of a gene and its expression can be hard to anticipate, especially when the gene in question is embedded in a regulatory circuit that contains feedbacks. Here we study this question making use of the natural genetic variability of human populations, which allows us to compare the expression profiles of a receptor protein in natural killer cells between donors infected with human cytomegalovirus (HCMV) with one or two copies of the allele. Crucially, the distribution of gene expression in many of the donors is bimodal, indicative of the presence of a positive feedback somewhere in the regulatory environment of the gene. Three separate gene-circuit models differing in the location of the positive feedback with respect to the gene can all reproduce well the homozygous data. However, when the resulting fitted models are applied to the hemizygous donors, only one model (the one with the positive feedback located at the level of gene transcription) reproduces the experimentally observed gene-expression profile. In that way, our work shows that zygosity can help us relate structure and function of gene regulatory networks.\n\nAuthor SummaryNearly all mammalian cells, including human cells, have two copies of each chromosome, and thus possess two potentially different copies of each gene (which might be in some cases non-functional or even absent). Naiively one might expect that two identical copies of the gene would lead to the protein being expressed at twice the rate, but many factors can alter this simple calculation. One of these factors is the existence of feedback mechanisms affecting in one way or another the regulatory circuit in which our gene of interest is embedded. Here we study the relationship between the number of gene copies and the expression of a receptor protein that plays a crucial role in the recognition of pathogens by natural killer cells, which are important elements of the innate immune system. Experimental data of virus-infected donors reveals a bimodal expression profile of this receptor, typical of a positive feedback, and a clear difference between donors with one or two copies of the gene. Mathematical modeling allows us to find the likely location of the feedback loop within the genes regulatory circuit, by requiring the correct model to reproduce the expression profiles of both types of donors.

systems biology↗