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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2026

Biophysical modeling of excitation/inhibition imbalance in Alzheimer's disease

INTRODUCTION: Alzheimer's disease (AD) involves early sensory and perceptual changes that can precede memory decline. We developed a two-layer rate-based neural model of early and higher visual areas with feedforward, feedback, and recurrent connections to examine how excitatory/inhibitory (E/I) balance shapes visual motion perception in AD. METHODS: Using Motion-Induced Position Shift and Altered Motion Repulsion stimuli, we factorially manipulated excitatory (E) and inhibitory (I) subfield power and connectivity to examine their effects on illusion representation across visual layers. RESULTS: Increased E, decreased I, and increased E/I reduced illusion representation, consistent with reduced illusion susceptibility in AD, whereas the converse increased illusion representation, reflecting neurotypical processing. Higher-level layer 2 representations were more resistant to E/I perturbations than lower-level layer 1. Illusion representation was less affected by E/I increases in intra-areal (recurrent) connectivity than in inter-areal (feedforward/feedback) connectivity. DISCUSSION: This mechanistic framework links E/I balance and connectivity to altered visual motion illusion representations in AD and suggests that E/I and connectivity measures may provide probes of early AD neurobiology. The framework may also generalize beyond vision.

neuroscience↗
bioRxiv2026

Effective mean-field variables explain how biophysical parameters control firing rate and regularity in a spiking network

Parameter sweeps of recurrent spiking networks are often interpreted as evidence that each nominal parameter controls activity through a distinct mechanism. We tested this interpretation in a 10,000 neuron Brunel type network using eight one factor sweeps, local sensitivity analysis, and a global Sobol design with 51,200 simulations. Under the standard calibration, seven of eight nominal parameters influenced the stationary state through effective quantities describing drive and recurrent coupling relative to threshold, input noise, and temporal scale ratios. Without fitting to simulation output, stationary Siegert theory predicted firing rates across the sweeps with a median error of 1%, while a renewal based approximation reproduced spike train regularity with R2 = 0.99 for the log coefficient of variation of interspike intervals. Applied without refitting to 10,240 parameter combinations, the theory explained 95% of firing rate variance and 90% of log regularity variance and reproduced the global sensitivity structure. Most apparent effects on rate and regularity therefore reflected the calibration. Synaptic delay contributed little to firing rate variance but was the leading total order factor for synchrony and bursting. Linear stability analysis predicted synchrony onset at 6.0 ms, within the observed transition from 5 to 9 ms, and reproduced oscillation frequencies. In a pre specified generality test, the theory also predicted firing rates in a fluctuation driven regime and reversals of threshold and synaptic time constant effects under alternative calibrations. These results support interpreting parameter dependence through effective drive, coupling, noise, and collective timing rather than nominal parameters alone.

physiology↗
bioRxiv2018

AmyCo: the Amyloidoses Collection

AmyCo: the Amyloidoses CollectionAmyloid fibrils are formed when soluble proteins misfold into highly ordered insoluble fibrillar aggregates and affect various organs and tissues. The deposition of amyloid fibrils is the main hallmark of a group of disorders, called amyloidoses. Curiously, fibril deposition has been also recorded as a complication in a number of other pathological conditions, including well-known neurodegenerative or endocrine diseases. To date, amyloidoses are roughly classified, owing to their tremendous heterogeneity. In this work, we introduce AmyCo, a freely available collection of amyloidoses and clinical disorders related to amyloid deposition. AmyCo classifies 74 diseases associated with amyloid deposition into two distinct categories, namely 1) amyloidosis and 2) clinical conditions associated with amyloidosis. Each database entry is annotated with the major protein component (causative protein), other components of amyloid deposits and affected tissues or organs. Database entries are also supplemented with appropriate detailed annotation and are referenced to ICD-10, MeSH, OMIM, PubMed, AmyPro and UniProtKB databases. To our knowledge, AmyCo is the largest repository containing information about amyloidoses and diseases related to amyloid deposition. The AmyCo web interface is available at http://bioinformatics.biol.uoa.gr/amyco.\n\nKaterina C. Nastou is a Ph.D. student in Bioinformatics, at the Department of Biology of the National and Kapodistrian University of Athens. She is currently working on computational analysis of membrane and amyloidogenic proteins as part of her Ph.D. thesis. Her research focuses on the study of protein-protein interactions and the visualization and analysis of biological networks for these protein families, on the computational prediction of protein structure and function and the design and development of biological databases.\n\nGeorgia I. Nasi is a Ph.D. student in Biophysics, at the Department of Biology of the National and Kapodistrian University of Athens and a second year student in the Bioinformatics Masters Program, at the same department. She is currently conducting her Masters thesis on the computational analysis and visualization of the interaction network of amyloidoses and proteins associated with these disorders. Her research for her Ph.D. focuses on biophysical and computational analysis of amyloidogenic proteins and peptide-analogues associated with amyloidoses.\n\nDr. Paraskevi L. Tsiolaki is a Biologist with an MSc in Bioinformatics and a PhD in Molecular Biophysics. She is currently working as a postdoctoral fellow in Dr V. Iconomidous group, Assist. Prof. at the National and Kapodistrian University of Athens and her research interests focus on Molecular Biophysics and Structural Biology. Her current research efforts have been directed towards identifying the structural characteristics that underlie the self-assembly mechanisms,governing amyloidogenicity. More specifically she works on the structure and self-assembly of different amyloidogenic proteins or amyloidogenic peptide-analogues, implicated with amyloidoses, utilizing biophysical and biochemical techniques. She is also working on the computational and structural analysis of the anomalous type of protein-protein interactions in protein aggregation, with particular focus on the development of novel therapeutic intervention strategies.\n\nDr.Zoi Litou works as a Special Laboratory Teaching Staff in \"Bioinformatics-Biophysics\" at the Section of Cell Biology and Biophysics, Department of Biology, National &Kapodistrian University of Athens. She has a PhD in Bioinformatics. She is currently working on computational analysis of membrane proteins focusing on the automated recognition and classification of single-spanning membrane proteins, CWPs, GPCRs and Ion channels. Biological Network Analysis, Prediction algorithms, Algorithm Visualization techniques in Bioinformatics, High throughput sequencing analysis and visualization, Clustering Analysis, Knowledge discovery, management and representation, Data integration, Chemoinformatics, Pharmacogenomics, Text Mining in Bioinformatics, Personalized Medicine, Parallel programming.\n\nDr. Vassiliki A. Iconomidou is an Assistant Professor of Structural Biology/Molecular Biophysics and a group leader of Biophysics and Bioinformatics Lab at the Department of Biology of the National and Kapodistrian University of Athens. Her research interests include: 1) Structural and self-assembly studies of fibrous proteins, which form extracellular, proteinaceous structures of physiological importance like lepidopteran, dipteran and fish chorions and arthropod cuticle, 2) Structural and self-assembly studies of silkmoth chorion peptide-analogues as novel self-assembled polymers with amyloid properties, aiming at the construction of novel biomaterials with extraordinary physical properties, 3) Experimental studiesof the role of a great variety of amyloidogenic ( aggregation-prone) peptides, predicted by our AMYLPRED prediction algorithm, in several widespread and also rare pathological amyloidoses. She had been visiting European Molecular Biology Laboratory (EMBL Heidelberg) for more than ten years, conducting research on molecular self-assembly focusing especially on functional, protective and pathological amyloids and amyloidoses, and she was there when she published the first article on natural protective amyloids. She is the author of 41 publications and 6 book chapters which focus mostly on functional and pathological amyloid studies.

bioinformatics↗
bioRxiv2024

Measurement of adhesion and traction of cells at high yield (MATCHY) reveals an energetic ratchet driving nephron condensation

Engineering of embryonic strategies for tissue-building has extraordinary promise for regenerative medicine. This has led to a resurgence in interest in the relationship between cell biophysical properties and morphological transitions. However, mapping gene or protein expression data to cell biophysical properties to physical morphogenesis remains challenging with current techniques. Here we present MATCHY (multiplexed adhesion and traction of cells at high yield). MATCHY advances the multiplexing and throughput capabilities of existing traction force and cell-cell adhesion assays using microfabrication and an automated computation scheme with machine learning-driven cell segmentation. Both biophysical assays are coupled with serial downstream immunofluorescence to extract cell type/signaling state information. MATCHY is especially suited to complex primary tissue-, organoid-, or biopsy-derived cell mixtures since it does not rely on a priori knowledge of cell surface markers, cell sorting, or use of lineage-specific reporter animals. We first validate MATCHY on canine kidney epithelial cells engineered for RET tyrosine kinase expression and quantify a relationship between downstream signaling and cell traction. We go on to create a biophysical atlas of primary cells dissociated from the mouse embryonic kidney and use MATCHY to identify distinct biophysical states along the nephron differentiation trajectory. Our data complement expression-level knowledge of adhesion molecule changes that accompany nephron differentiation with quantitative biophysical information. These data reveal an energetic ratchet that explains spatial nephron progenitor cell condensation from the niche as they differentiate, which we validate through agent-based computational simulation. MATCHY offers automated cell biophysical characterization at >104-cell throughput, a highly enabling advance for fundamental studies and new synthetic tissue design strategies for regenerative medicine.

bioengineering↗
bioRxiv2026

Kv4, Kv2, and Kv3 currents shape intrinsic lateral olivocochlear excitability independent of hair cell dysfunction during development and ageing.

Intrinsic lateral olivocochlear (iLOC) neurons provide vital brainstem efferent feedback to the cochlea in order to modulate hearing sensitivity through synapses onto type-I spiral ganglion neurons. During ageing or mutations affecting hair cell transduction in mice, efferent neurons rewire to form direct axo-somatic synapses onto inner hair cells (IHCs), recapitulating a synaptic configuration typically only restricted to the immature cochlea. Whether this rewiring reflects a compensatory mechanism or some form of attempted repair, or how iLOC biophysics change throughout ageing and this rewiring process, is not known. We utilised whole-cell patch-clamp electrophysiology to investigate iLOC activity and their underlying biophysics across the wild-type mouse lifespan. We show that iLOC neurons undergo a progressive increase in excitability with post-natal development and ageing, producing more spikes for a given stimulus. This intrinsic excitability shift was driven by the developmental decline in the A-type Kv4 mediated potassium current and increase in Kv2 mediated current. In ageing animals, and distinct from post-natal development, further increased firing rates were supported by an increased size of the fast-activating Kv3 current. Spontaneous bursting activity remained present in ageing iLOC neurons, and no reversion to an immature biophysics profile was evident. Interestingly, despite robustly eliciting efferent rewiring of IHCs, an accelerated ageing-like re-innervation genetic model did not recreate the biophysical changes in the iLOC neurons that reflected the ageing system. This work reveals distinct processes occurring within the iLOC feedback system, and shows that age-related enhancements of SGN resting activity are not triggered by deficits in IHC transduction. Significance StatementModulation of cochlea output from the brainstem remains poorly understood, and the efferent feedback from the intrinsic lateral olivocochlear (iLOC) neurons has been shown to be vital for maintaining hearing sensitivity after noise exposure. Efferent neurons undergo significant rewiring within the cochlea during maturation that appears to revert to an immature state with ageing or with dysfunction of the inner hair cell sound-transducing apparatus, though how iLOC activity adapts to this is unknown. Here we show that ageing involves distinct shifts in sodium and potassium current biophysics that support the generation and development of spontaneous bursting activity. Disrupting sound transduction revealed that iLOC age-related biophysical changes and cochlear rewiring are independent, and defective transduction is insufficient to recreate ageing biophysics.

neuroscience↗
bioRxiv2021

Predicting the Functional Impact of KCNQ1 Variants with Artificial Neural Networks

Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasing applied to problems in biology, biophysical understanding will be critical with respect to explainable AI, i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org. Author summaryHeartbeat is maintained by electrical impulses generated by ion-conducting channel proteins in the heart such as the KCNQ1 potassium channel. Pathogenic variants in KCNQ1 can lead to channel loss-of-function and predisposition to fatal life-threatening irregularities of heart rhythm (arrhythmia). Machine learning methods that can predict the outcome of a mutation on KCNQ1 structure and function would be of great value in helping to assess the risk of a heart rhythm disorder. Recently, machine learning has made great progress in predicting the structures of proteins from their sequences. However, there are limited studies that link the effect of a mutation and change in protein structure with its function. This work presents the development of neural network models designed to predict mutation-induced changes in KCNQ1 functional parameters such as peak current density and voltage dependence of activation. We compare the predictive ability of features extracted from sequence, structure, and physicochemical properties of KCNQ1. Moreover, input sensitivity analysis connects biophysical features with specific functional parameters that provides insight into underlying molecular mechanisms for KCNQ1 channels. The best performing neural network model is publicly available as a webserver, called Q1VarPredBio, that delivers predictions about the functional phenotype of KCNQ1 variants.

biophysics↗
bioRxiv2022

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies ([lsim] 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models ({gtrsim} 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ( spikes) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficacy of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework. Author summaryUnderstanding the brains function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanitys great undertakings. In experimental and clinical work probing the brains activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

neuroscience↗
bioRxiv2018

Analysis of proteins in computational models of synaptic plasticity

The desire to explain how synaptic plasticity arises from interactions between ions, proteins and other signalling molecules has propelled the development of biophysical models of molecular pathways in hippocampal, striatal and cerebellar synapses. The experimental data underpinning such models is typically obtained from low-throughput, hypothesis-driven experiments. We used high-throughput proteomic data and bioinformatics datasets to assess the coverage of biophysical models.\n\nTo determine which molecules have been modelled, we surveyed biophysical models of synaptic plasticity, identifying which proteins are involved in each model. We were able to map 4.2% of previously reported synaptic proteins to entities in biophysical models. Linking the modelled protein list to Gene Ontology terms shows that modelled proteins are focused on functions such as calmodulin binding, cellular responses to glucagon stimulus, G-alpha signalling and DARPP-32 events.\n\nWe cross-linked the set of modelled proteins with sets of genes associated with common neurological diseases. We find some examples of disease-associated molecules that are well represented in models, such as voltage-dependent calcium channel family (CACNA1C), dopamine D1 receptor, and glutamate ionotropic NMDA type 2A and 2B receptors. Many other disease-associated genes have not been included in models of synaptic plasticity, for example catechol-O-methyltransferase (COMT) and MAO A. By incorporating pathway enrichment results, we identify LAMTOR, a gene uniquely associated with Schizophrenia, which is closely linked to the MAPK pathway found in some models.\n\nOur analysis provides a map of how molecular pathways underpinning neurological diseases relate to synaptic biophysical models that can in turn be used to explore how these molecular events might bridge scales into cellular processes and beyond. The map illustrates disease areas where biophysical models have good coverage as well as domain gaps that require significant further research.\n\nAuthor summaryThe 100 billion neurons in the human brain are connected by a billion trillion structures called synapses. Each synapse contains hundreds of different proteins. Some proteins sense the activity of the neurons connecting the synapse. Depending on what they sense, the proteins in the synapse are rearranged and new proteins are synthesised. This changes how strongly the synapse influences its target neuron, and underlies learning and memory. Scientists build computational models to reason about the complex interactions between proteins. Here we list the proteins that have been included in computational models to date. For good reasons, models do not always specify proteins precisely, so to make the list we had to translate the names used for proteins in models to gene names, which are used to identify proteins. Our translation could be used to label computational models in the future. We found that the list of modelled proteins contains only 4.2% of proteins associated with synapses, suggesting more proteins should be added to models. We used lists of genes associated with neurological diseases to suggest proteins to include in future models.

neuroscience↗
bioRxiv2022

Characteristic energy scales of active fluctuations in adherent cells

Cell-matrix and cell-cell adhesion play important roles in a wide variety of physiological processes, from the single cell level to the large scale, multicellular organization of tissues. Cells actively apply forces to their environment, either extracellular matrix or neighboring cells, as well as sense its biophysical properties. The fluctuations associated with these active processes occur on an energy scale much larger than that of ordinary thermal equilibrium fluctuations, yet their statistical properties and characteristic scales are not fully understood. Here, we compare measurements of the energy scale of active cellular fluctuations -- an effective cellular temperature -- in four different biophysical settings, involving both single cell and cell aggregates experiments under various control conditions, different cell types and various biophysical observables. The results indicate that a similar energy scale of active fluctuations might characterize the same cell type in different settings, though it may vary among different cell types, being approximately 6 to 8 order of magnitude larger than the ordinary thermal energy at room temperature. These findings call for extracting the energy scale of active fluctuations over a broader range of cell types, experimental settings and biophysical observables, and for understanding the biophysical origin and significance of such cellular energy scales.

biophysics↗
bioRxiv2024

Scalable Antigen-Antibody Binding Affinity Landscape: A Case Study with ENHERTU

Optimization of binding affinities for antibody-drug conjugates (ADCs) is inextricably linked to their therapeutic efficacy and specificity, where the majority of ADCs are engineered to achieve equilibrium dissociation constants (Kd values) in the range of 10-9 to 10-10 M. Yet, there is a paucity of published data delineating the optimal binding affinity or its range that ensures improved therapeutic outcomes for ADCs. This study addresses this issue by integrating structural biophysics within a scalable in silico workflow to generate antigen-antibody binding affinity landscapes, with a focus on Trastuzumab, a monoclonal antibody employed in the treatment of HER2-positive breast cancer. By leveraging high-throughput computational techniques, including homology structural modeling and structural biophysics-based Kd calculations, this research puts forward a set of high-accuracy structural and intermolecular binding affinity data for Her2-Trastuzumab-Pertuzumab (PDB entry 6OGE). Beyond the design of Her2-targeting ADCs with enhanced efficacy and specificity, this scalable antigen-antibody binding affinity landscape also offers a technically feasible workflow for the high-throughput generation of synthetic structural and biophysical data with reasonable accuracy. Overall, in combination with artificial intelligence (e.g., deep learning) algorithms, this synthetic data approach aims to catalyze a paradigm shift in the discovery and design of antibodies and ADCs with improved efficacy and specificity. SIGNIFICANCEWith Trastuzumab as an example, this study presents a scalable computational biophysical generation of antigen-antibody binding affinity landscapes, serving two purposes: design of Her2-targeting ADCs with enhanced efficacy and specificity and continued accumulation of synthetic structural biophysics data for the development of useful AI-based drug discovery and design model in future. This scalable approach is broadly applicable to databases such as Protein Data Bank.

biophysics↗
bioRxiv2020

Predicting changes in protein thermostability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability ({triangleup}{triangleup}G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based prediction of {triangleup}{triangleup}Gs upon point mutation. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous {triangleup}{triangleup}G prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the {triangleup}{triangleup}Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D convolutional neural networks can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms. Author SummaryThe thermodynamic stability of a protein, usually represented as the Gibbs free energy for the biophysical process of protein folding ({triangleup}G), is a fundamental thermodynamic quantity. Predicting mutation-induced changes in protein thermodynamic stability ({triangleup}{triangleup}G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. However, predicting {triangleup}{triangleup}Gs in an accurate and unbiased manner has been a long-standing challenge in the field of computational biology. In this work, we introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based {triangleup}{triangleup}G prediction. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. ThermoNet demonstrates performance comparable to the best available methods. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We also demonstrate that the presence of homologous proteins in commonly used training and testing sets for {triangleup}{triangleup}G prediction methods has likely influenced previous performance estimates. Finally, we highlight the practical utility of ThermoNet by applying it to predicting the {triangleup}{triangleup}Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar.

bioinformatics↗
bioRxiv2021

BIAPSS - BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences

Liquid-liquid phase separation (LLPS) has recently emerged as a cornerstone mechanism underlying the biogenesis of membraneless organelles (MLOs). However, a quantitative molecular grammar of protein sequences that controls the LLPS remains poorly understood. The progress in this field is hampered by the insufficiency of comprehensive databases and associated computational infrastructure for targeting biophysical and statistical analysis of phase separating biopolymers. Therefore, we have created a novel open-source web platform named BIAPSS (BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences) which contains interactive data analytic tools in combination with a comprehensive repository of bioinformatic data for on-the-fly exploration of sequence-dependent properties of proteins with known LLPS behavior. BIAPSS includes a residue-resolution biophysical analyzer for interrogating individual protein sequences (SingleSEQ tab). The latter allows users to correlate regions prone to phase separation with a large array of physicochemical attributes and various short linear motifs. BIAPSS also includes global statistics derived over the universe of most of the known LLPS-driver protein sequences (MultiSEQ tab) for revealing the regularities and sequence-specific signals driving phase separation. Finally, BIAPSS incorporates an extensive cross-reference section that links all entries to primary LLPS databases and other external resources thereby serving as a central navigation hub for the phase separation community. All of the data used by BIAPSS is freely available for download as well-formatted pre-processed data with detailed descriptions, facilitating rapid implementation in user-defined computational protocols. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/430806v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1d90ddorg.highwire.dtl.DTLVardef@111d084org.highwire.dtl.DTLVardef@90b3b0org.highwire.dtl.DTLVardef@51d71_HPS_FORMAT_FIGEXP M_FIG TOC - graphical abstract C_FIG Author summaryProteins, especially those with low complexity and intrinsically disordered regions, have recently come into the limelight because of mounting evidence showing that these regions can drive the formation of membraneless organelles (MLOs) in cells. The underlying physical mechanism for forming MLOs is liquid-liquid phase separation (LLPS); a thermodynamically driven process whereby a cellular milieu with a relatively well-mixed distribution of biomolecules gets decomposed into liquid droplets where the concentration of selected biomolecules is higher. Deciphering molecular sequence grammar of phase separation has turned out to be challenging because of the complexity of this process in cells and the vastness of sequence space of LLPS-driver proteins. While the field is still in its infancy the growth of experimental data has already spurred the creation of several major databases which collect and annotate bimolecular systems with confirmed LLPS behavior. What is currently missing is a framework that would leverage the existing databases by integrating them with deep biophysical and bioinformatic analysis for identifying statistically significant features of protein sequences implicated in LLPS. In this work, we have addressed this challenge by creating an open-source web platform named BIAPSS (BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences) which integrates a comprehensive repository of pre-processed bioinformatic data for LLPS-driver protein sequences with interactive analytic applications for on-the-fly analysis of biophysical features relevant for LLPS behavior. BIAPSS empowers users with novel and effective tools for exploring LLPS-related sequence signals for individual proteins (SingleSEQ tab) and globally by integrating common regularities across subgroups or the entire LLPS sequence superset (MultiSEQ). The long-term plan for BIAPSS is to serve as a unifying hub for the experimental and computational community with a comprehensive set of analytic tools, biophysically featured data, and standardized protocols facilitating the identification of sequence hot spots driving the LLPS, which all can support applications for designing new sequences of biomedical interest.

bioinformatics↗
bioRxiv2024

An electrodiffusive network model with multicompartmental neurons and synaptic connections

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, shortcuts are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks. Author summaryNeurons communicate using electrical signals called action potentials. To create these signals, sodium ions must flow into the cells and potassium ions must flow out. This transmembrane flow requires a concentration difference across the neuronal membrane, which the brain works continuously to maintain. When scientists build mathematical models of neurons, they often apply the simplifying assumption that these ion concentration differences remain constant over time. This assumption works well for many scenarios, but not all. For instance, during events like stroke or epilepsy, the ion concentrations can change dramatically, affecting how neurons behave. Moreover, recent literature suggests that changing ion concentrations also play an important role in normal brain function. To study these scenarios, we need models that can dynamically track changes in ion concentrations. The neuroscience community currently lacks a computational model describing the effects of ion concentration dynamics on neuronal networks, while maintaining a biophysical consistent relationship between ion concentrations and electrical potentials. To address the need for such a model, we have developed a neuronal network model that predicts changes in both intra- and extracellular ion concentrations, electrical potentials, and volumes in a biophysically consistent way.

neuroscience↗
bioRxiv2024

Vestibular afferent neurons develop normally in the absence of quantal/glutamatergic input

The vestibular nerve is comprised of neuron sub-groups with diverse functions related to their intrinsic biophysical properties. This diversity is partly due to differences in the types and numbers of low-voltage-gated potassium channels found in the neurons membranes. Expression for some low-voltage gated ion channels like KCNQ4 is upregulated during early post-natal development; suggesting that ion channel composition and neuronal diversity may be shaped by hair cell activity. This idea is consistent with recent work showing that glutamatergic input from hair cells is necessary for the normal diversification auditory neurons. To test if biophysical diversity is similarly dependent on glutamatergic input in vestibular neurons, we examined the maturation of the vestibular epithelium and ganglion neurons in Vglut3-ko mice whose hair cell synapses lack glutamate. Despite lacking glutamatergic input, the knockout mice showed no notable balance deficits and crossed challenging balance beams with little difficulty. Immunolabeling of the Vglut3-ko vestibular epithelia showed normal development as indicated by an identifiable striolar zone with calyceal terminals labeled by molecular marker calretinin, and normal expression of KCNQ4 by the end of the second post-natal week. We found similar numbers of Type I and Type II hair cells in the knockout and wildtype animals, regardless of epithelial zone. Thus, the presumably quiescent Type II hair cells are not cleared from the epithelium. Patch-clamp recordings showed that biophysical diversity of vestibular ganglion neurons in the Vglut3-ko mice is comparable to that found in wildtype controls, with a similar range firing patterns at both immature and juvenile ages. However, our results suggest a subtle biophysical alteration to the largest ganglion cells (putative somata of central zone afferents); those in the knockout had smaller net conductance and were more excitable than those in the wild type. Thus, unlike in the auditory nerve, glutamatergic signaling is unnecessary for producing biophysical diversity in vestibular ganglion neurons. And yet, because the input signals from vestibular hair cells are complex and not solely reliant on quantal release of glutamate, whether diversity of vestibular ganglion neurons is simply hardwired or regulated by a more complex set of input signals remains to be determined.

neuroscience↗
bioRxiv2018

A national ‘safe and just operating space’ for all in India: Past, Present and Futurea

With 1.3 billion populaces on the commencement of the 21st century, India is currently impending towards upholding a subtle equilibrium between persisting social development and well-being without depleting existing biophysical resources at the national level or surpassing global average per capita obtainability. In this paper, we have structured a top-down per capita framework to explore national safe and just operating space (NSJOS) to apprehend not only past fluctuations that bring about the present conditions but also the plausible future consequences, with India as a case study. Coalescing 27 indicators, all pertaining to Sustainable Development Goals (except - SDG 17), accompanied by their corresponding environmental boundaries or preferred social thresholds, present study probes into both biophysical (for environmental stress) and social development (for social deficit) attributes of India. This analysis shows India has already crossed three of seven dimensions of biophysical boundaries (freshwater, nitrogen and phosphorus use). Also, at the existing rate, India is going to cross the remainder of the boundaries within 2045-2050 (climate change, arable land use, ecological and material footprint). Of 20 indicators used for social development, only five have already or will meet corresponding desired thresholds of United Nations Sustainable Development Goals 2015. Using tendencies of past variations, the results indicate that if lowest per capita consumption can be attained and uphold, even with projected population growth, total consumption of four biophysical resources (climate change, nitrogen use, ecological and material footprint) can be slashed from todays level in 2050. Adaptations in national policy are indispensable if India wants to accomplish sufficiency in biophysical resources whilst bestowing social equity in access and exploitation of those resources towards the continuance of social developments in forthcoming times.

ecology↗
bioRxiv2018

A comparative study of ‘safe and just operating space’ for the south and south-east Asian countries

The world is presently maintaining a delicate balance of continuing well-being and social development for the people through consumption of biophysical resources of nature without topping global average per capita availability. In this paper, we have framed a per capita top-down framework to survey national safe and just operating space (NSJOS) for the countries of south and southeast Asia to understand past variations and as a consequence, the present scenario. Amalgamating 27 indicators, all regarding Sustainable Development Goals (except - SDG 17), in consort with their respective environmental boundaries or desirable social development thresholds, this study explores into both biophysical (for ecological stress) and social development (for social deprivation) attributes of 19 countries of south and southeast Asia. This analysis shows, only 2 have remained either unchanged (political voice) or declining (social equity) among the 12 dimensions of social development in countries of this region. The remaining 10 dimensions of social development showing positive progress and will meet corresponding desired thresholds of United Nations Sustainable Development Goals 2015. All the 7 indicators showing tendencies of overconsumption of biophysical resources, that might be leading to exceeding per capita global average planetary boundaries in forthcoming future. However, ecological boundaries have remained protected to a decent degree so far for these countries. The challenge would be to maintain and increase the pace of social development and bringing it in equal strata of a global standard in future without depleting drivers of these, i.e. biophysical resources. National policy adaptations are crucial if these countries of south and southeast Asia desire to bring about adequacy in biophysical resources reserve whilst granting social equity in access and exploitation of these resources for the people towards the persistant social development in impending decades.

ecology↗
bioRxiv2018

Bayesian parameter estimation in non-stationary semiflexible polymers from ensembles of trajectories

During the cell-cycle and meiosis, during development, or in response to stress, chromosomes undertake dramatic programs of reorganisation, which can result in major changes to genomic architecture, as well as local changes to chromatin structure via chromatin remodelling and epigenetic modification. The biophysical properties of the genome may therefore vary significantly over time, from region to region, and from cell to cell.\n\nSemifleixble polymer models are frequently used to decipher the spatial and temporal aspects of chromosome organisation. Such models allow for parameter estimation from experimental observations (Bystricky et al., 2004, Ding et al., 2006, Koszul et al., 2008, Arbona et al., 2017), and so provide a concise quantification of the state of the system in terms of meaningful biophysical parameters, such as the compaction factor and bending-modulus. Simulation studies using appropriately parameterised models may also provide novel insights, and allow for predictions without confounding pleiotropic effects (Penfold et al., 2012), thus guiding future studies.\n\nMost semifleixble polymer models do not explicitly consider the spatial non-stationarity of chromosomes and chromatin. Furthermore, recent advances in chromosome conformation capture (3C)-based allow chromosome organisation to be (indirectly) measured in single cells (Belton et al., 2012, Nagano et al., 2013, 2016). The increasing availability of ensembles of trajectories sampled from potentially heterogeneous populations of cells means it is of interest to develop polymer statistic models that can capture both the spatial nonstationarity of the biophysical parameters, and the statistical relationships that exist within the population. Here we outline a statistical framework for non-stationary semiflexible polymers, and demonstrate how inference can be performed using ensembles of trajectories. For cells belonging to a homogenous population where the biophysical parameters are approximately identical in all cells, a (transformed) Gaussian process prior is assigned to the bending-modulus, and Markov chain Monte Carlo (MCMC) used to infer the posterior distribution of free parameters. For heterogeneous populations of cells, a transformed hierarchical GP (HGP) prior is assigned to the biophysical parameters, which naturally captures the statistical dependency of the parameters that exist across the population. Simulation studies demonstrate the accuracy of the model for homogenous and heterogeneous populations, while applications to yeast chromosome data demonstrates an improved ability to recapitulate trajectories of held out loci compared to related stationary models.\n\nCode used for these analyses, and a graphical user interface (GUI) for simulating non-stationary semifleixble polymer trajectories, have been implemented in MATLAB, and are available to download from: https://github.com/cap76/3MC.

bioinformatics↗
bioRxiv2019

Comparative Analysis of Acoustic Propagation Parameters of Natural Sounds of Anopheles gambiae s.s and Odorrana tormota Significant in Mosquito Startle

Acoustics of varied frequency ranges generated naturally by animals or artificially by electronic devices have shown startle effect to insects. It has been shown that mosquitoes use the reactive near-field in antennae communication with negative phonotaxis in male Aedes diantaeus evoked by low frequency acoustic signals of a carrier frequency 140-200 Hz. Also, studies with the 35-60 kHz Odorrana tormota sound recorded a 46 % repellence in female Anopheles gambiae, the malaria vectors. Declining malaria morbidity and mortality is attributed to current vector and pathogen interventions. However, the rate of decline in malaria morbidity and mortality is impeded by buildup of resistance in pathogens and vectors to chemicals. This study therefore characterised animal sounds essential for further investigation in the control of malaria through mosquito startle. The research determined, analysed and compared the acoustic propagation parameters of the recorded natural sounds of the male Anopheles gambiae, female Anopheles gambiae and Odorrana tormota using Avisoft SASLAB Pro and Raven Pro 1.5. All sounds were observed to have frequency modulation with harmonics stretching to ultrasonic levels. Uniquesly, the sound of O. tormota showed constant frequency modulation. The pupae of A. gambiae were reared in vials quarter filled with water and covered with a net at 60-80 % humidity, 25{+/-}2 {degrees}C temperature and equal light-darkness hour cycle at Kenya Medical Research Institute (KEMRI) entomology laboratories. The parameters showed a significant deference in fundamental frequency (maximum entire), Peak amplitude (maximum), peak amplitude (mean), Peak amplitude (mean entire) and peak amplitude (maximum entire) of the sound of male A. gambiae and O. tormota (p < 0.05). The maximum frequency (minimum entire) of both sexes of A. gambiae was equal (1.90 kHz) with variability being observed in maximum frequency (end), maximum frequency (maximum), maximum frequency (mean), maximum frequency (maximum entire) and maximum frequency (mean entire). Frequency (maximum). A paired samples t-test comparison of the maximum frequency (mean), maximum frequency (maximum), maximum frequency (end), maximum frequency (maximum entire) and maximum frequency (mean entire) of the sound of the female A. gambiae and male A. gambiae indicated no significant difference between the sounds (p > 0.05). The maximum frequency (mean) of the sounds of both sexes of A. gambiae correlated highly negative (r = -0.658). The bandwidth (end), bandwidth (maximum), bandwidth (maximum entire), peak amplitude (mean) and bandwidth (mean entire) of the sound of the male compared with female A. gambiae differed significantly. The signal power for the non-pulsate sounds of the male A. gambiae remained almost constant at 80 dB from 10 kHz to 65 kHz beyond which the acoustic energy declining to 45 dB. Also, the sounds of the female A. gambiae did not exhibit any spikes in power but remained steady at 85 dB from 10 kHz up to 60 kHz beyond which the acoustic energy declined to 50 dB. The signal power of the pulsate sound of O. tormota was 89 dB. The propagation parameters of the male mosquito and O. tormota compared favourably indicating its potential in the startle of the female mosquito.\n\nThe author summaryPhilip Amuyunzu Mangare is a PhD. Physics student in Egerton University. He has authored many papers and books. He is currently a Lecturer of Physics (Electronics), Masinde Muliro University of Science and Technology. He is a member of the Biophysical Society and the current President of Biophysical society (Kenya). Prof. Ndiritu F. Gichuki, is a Professor of Physics Egerton University. Currently he is the Registrar Academic Affairs in Chuka University. His vast experience has seen him supervise many postgraduate students who have taken key positions in the society. Prof. Samwel Rotich is a Profesor of Physics in Moi University specialising in Electronics. He has a wide experience in Physics and Biophysics. He is a registered member of the Biophysical Society and the Patron of Biophysical Society Kenya Chapter. He has published many papers and supervised many postgraduate students. Dr. Makatiani Kubochi is a Lecturer in Moi University with vast experience in entomology. She has published many papers and supervised many postgraduate students. Dr. Rapando Bernard Wakhu is a renown theoretical Physicist with experience in acoustics and Fourier analysis based in Masinde Muliro University of Science and Technology. He has supervised many postgraduate students and published many papers.

animal behavior and cognition↗