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bioRxiv2023

Millimeter-scale magnetic implants paired with a fully integrated wearable device for wireless biophysical and biochemical sensing

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors must rely on transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces the biocompatibility, or limits the portability. Here, we develop a set of millimeter-scale, chip-less and battery-less magnetic implants that can measure biophysical and biochemical signals wirelessly. In particular, the implants form two-way communications with a fully integrated wearable device, where the wearable device can induce a large-amplitude damped vibration of the magnetic implants and capture their subsequent motions in a wireless manner. Such damped vibrations reflect not only the biophysical conditions surrounding the implants movements, but also the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities in measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure (ICP), and CSF glucose levels. This miniaturized system opens possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism.

bioengineering↗

bioRxiv2024

Biophysical carbon concentrating mechanisms in land plants: insights from reaction-diffusion modeling

Carbon Concentrating Mechanisms (CCMs) have evolved numerous times in photosynthetic organisms. They elevate the concentration of CO2 around the carbon-fixing enzyme rubisco, thereby increasing CO2 assimilatory flux and reducing photorespiration. Biophysical CCMs, like the pyrenoid-based CCM of Chlamydomonas reinhardtii or carboxysome systems of cyanobacteria, are common in aquatic photosynthetic microbes, but in land plants appear only among the hornworts. To predict the likely efficiency of biophysical CCMs in C3 plants, we used spatially resolved reaction-diffusion models to predict rubisco saturation and light use efficiency. We find that the energy efficiency of adding individual CCM components to a C3 land plant is highly dependent on the permeability of lipid membranes to CO2, with values in the range reported in the literature that are higher than used in previous modeling studies resulting in low light use efficiency. Adding a complete pyrenoid-based CCM into the leaf cells of a C3 land plant is predicted to boost net CO2 fixation, but at higher energetic costs than those incurred by photorespiratory losses without a CCM. Two notable exceptions are when substomatal CO2 levels are as low as those found in land plants that already employ biochemical CCMs and when gas exchange is limited such as with hornworts, making the use of a biophysical CCM necessary to achieve net positive CO2 fixation under atmospheric CO2 levels. This provides an explanation for the uniqueness of hornworts CCM among land plants and evolution of pyrenoids multiple times.

plant biology↗

bioRxiv2024

Design of a Cereblon construct for crystallographic and biophysical studies of protein degraders

The ubiquitin E3 ligase cereblon (CRBN) is the target of therapeutic drugs thalidomide and lenalidomide and is recruited by most targeted protein degraders (PROTACs and molecular glues) in clinical development. Biophysical and structural investigation of CRBN has been limited by current constructs that either require co-expression with the adaptor DDB1 or inadequately represent full-length protein, with high-resolution structures of degraders ternary complexes remaining rare. We present the design of CRBNmidi, a construct that readily expresses from E. coli with high yields as soluble, stable protein without DDB1. We benchmark CRBNmidi for wild-type functionality through a suite of biophysical techniques and solve high-resolution co-crystal structures of its binary and ternary complexes with degraders. We qualify CRBNmidi as an enabling tool to accelerate structure-based discovery of the next generation of CRBN based therapeutics. One sentence summaryA novel Cereblon construct (CRBNmidi) allows structural and biophysical enablement of ligand and degrader design

biochemistry↗

bioRxiv2024

Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

bioinformatics↗

bioRxiv2024

Biophysical Modeling and Experimental Analysis of the Dynamics of C. elegans Body-Wall Muscle Cells

This study combines experimental techniques and mathematical modeling to investigate the dynamics of C. elegans body-wall muscle cells. Specifically, by conducting voltage clamp and mutant experiments, we identify key ion channels, particularly the L-type voltage-gated calcium channel (EGL-19) and potassium channels (SHK-1, SLO-2), which are crucial for generating action potentials. We develop Hodgkin-Huxley-based models for these channels and integrate them to capture the cells electrical activity. To ensure the model accurately reflects cellular responses under depolarizing currents, we develop a parallel simulation-based inference method for determining the models free parameters. This method performs rapid parallel sampling across high-dimensional parameter spaces, fitting the model to the responses of muscle cells to specific stimuli and yielding accurate parameter estimates. We validate our model by comparing its predictions against cellular responses to various current stimuli in experiments and show that our approach effectively determines suitable parameters for accurately modeling the dynamics in mutant cases. Additionally, we discover an optimal response frequency in body-wall muscle cells, which corresponds to a burst firing mode rather than regular firing mode. Our work provides the first experimentally constrained and biophysically detailed muscle cell model of C. elegans, and our analytical framework combined with robust and efficient parametric estimation method can be extended to model construction in other species. Author summaryDespite the availability of many biophysical neuron models of C. elegans, a biologically detailed model of its muscle cell remains lacking, which hampers an integrated understanding of the motion control process. We conduct voltage clamp and mutant experiments to identify ion channels that influence the dynamics of body-wall muscle cells. Using these data, we establish Hodgkin-Huxley-based models for these ion channels and integrate them to simulate the electrical activity of the muscle cells. To determine the free parameters of the model, we develop a simulation-based inference method with parallel sampling that aligns the model with the muscle cells responses to specific stimuli. Our method allows for swift parallel sampling of parameters in high dimensions, facilitating efficient and accurate parameter estimation. To validate the effectiveness of the determined parameters, we verify the cells responses under different current stimuli in wild type and mutant cases. Furthermore, we investigate the optimal response frequency of body-wall muscle cells and find that it exhibits a frequency consistent with burst firing mode rather than regular firing mode. Our research introduces the first experimentally validated and biophysically detailed model of muscle cells in C. elegans. Additionally, our modeling and simulation framework for efficient parametric estimation in high-dimensional dynamical systems can be extended to model constructions in other scenarios.

neuroscience↗

bioRxiv2024

Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt

1In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalisability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported. Here, we present a comprehensive study exploring various approaches for protein fitness prediction from limited data, leveraging pre-trained embeddings, repeated stratified nested cross-validation, and ensemble learning to ensure an unbiased assessment of the performances. We applied our framework to introduce NanoMelt, a predictor of nanobody thermostability trained with a dataset of 640 measurements of apparent melting temperature, obtained by integrating data from the literature with 129 new measurements from this study. We find that an ensemble model stacking multiple regression using diverse sequence embeddings achieves state-of-the-art accuracy in predicting nanobody thermostability. We further demonstrate NanoMelts potential to streamline nanobody development by guiding the selection of highly stable nanobodies. We make the curated dataset of nanobody thermostability freely available and NanoMelt accessible as a downloadable software and webserver. 2 Significance StatementRapidly predicting protein biophysical traits with accuracy is a key goal in protein engineering, yet efforts to develop reliable predictors are often hindered by limited and disparate experimental measurements. We introduce a framework to predict biophysical traits using few training data, leveraging diverse machine learning approaches via a semi-supervised framework combined with ensemble learning. We applied this framework to develop NanoMelt, a tool to predict nanobody thermostability trained on a new dataset of apparent melting temperatures. Nanobodies are increasingly important in research and therapeutics due to their ease of production and small size, which allows deeper tissue penetration and seamless combination into multi-specific compounds. NanoMelt outperforms available methods for protein thermostability prediction and can streamline nanobody development by guiding the design and selection of highly stable nanobodies during discovery and optimization campaigns.

bioengineering↗

bioRxiv2024

Capturing the emergent dynamical structure in biophysical neural models

Complex neural systems can display structured emergent dynamics. Capturing this structure remains a significant scientific challenge. Using information theory, we apply Dynamical Independence (DI) to uncover the emergent dynamical structure in a minimal 5-node biophysical neural model, shaped by the interplay of two key aspects of brain organisation: integration and segregation. In our study, functional integration within the biophysical neural model is modulated by a global coupling parameter, while functional segregation is influenced by adding dynamical noise, which counteracts global coupling. DI defines a dimensionally-reduced macroscopic variable (e.g., a coarse-graining) as emergent to the extent that it behaves as an independent dynamical process, distinct from the micro-level dynamics. We measure dynamical dependence (a departure from dynamical independence) for macroscopic variables across spatial scales. Our results indicate that the degree of emergence of macroscopic variables is relatively minimised at balanced points of integration and segregation and maximised at the extremes. Additionally, our method identifies to which degree the macroscopic dynamics are localised across microlevel nodes, thereby elucidating the emergent dynamical structure through the relationship between microscopic and macroscopic processes. We find that deviation from a balanced point between integration and segregation results in a less localised, more distributed emergent dynamical structure as identified by DI. This finding suggests that a balance of functional integration and segregation is associated with lower levels of emergence (higher dynamical dependence), which may be crucial for sustaining coherent, localised emergent macroscopic dynamical structures. This work also provides a complete computational implementation for the identification of emergent neural dynamics that could be applied both in silico and in vivo. Author summaryUnderstanding how complex neural systems give rise to emergent macroscopic patterns is a central challenge in neuroscience. Emergence, where macroscopic structures appear from underlying microscopic interactions, plays a crucial role in brain function, yet identifying the specific dynamics involved remains elusive. Traditionally, methods have quantified the extent of emergence but have struggled to pinpoint the emergent dynamical structure itself. In this study, we develop and apply a method, based on a quantity called Dynamical Independence (DI), which simultaneously captures the extent of emergence and reveals the underlying dynamical structure in neurophysiological data. Using a minimal 5-node biophysical neural model, we explore how a balance between functional integration and segregation--two key organisational principles in the brain--affects emergent macroscopic dynamics. Our results show that a finely balanced system produces highly localised, coherent macroscopic structures, while extreme deviations lead to more distributed, less localised dynamics. This work provides a computational framework for identifying emergent dynamical structure in both theoretical models and potentially in empirical brain data, advancing our understanding of the brains complex organisation across higher-order scales.

neuroscience↗

bioRxiv2024

Polysomes and mRNA control the biophysical properties of the eukaryotic cytoplasm

The organization and biophysical properties of the cytoplasm influence all cellular reactions, including molecular interactions and the mobility of biomolecules. It has become clear that the cytoplasm does not behave like a simple fluid but instead is a densely crowded and highly organized environment. Yet, the detailed properties of the cytoplasm, the molecular mechanisms that control them and how they influence the biochemistry of cells remain poorly understood. Here, we investigate the diffusive properties of the cytoplasm in silico and in vivo, employing mRNPs (messenger ribonucleoprotein) and GEM (genetically encoded multimeric) particles as rheological probes in proliferating cells. We demonstrate that cytoplasmic diffusivity increases upon polysome disassembly due to translation inhibition or upon a reduction in mRNA levels. Reducing ribosome concentration by up to 20-25% without a change in polysome levels has no effect in vivo. In addition, we show that upon polysome disassembly, mRNA condensation into P-bodies does not affect cytosolic diffusion in budding yeast. Altogether, our results show that mRNAs and their organization into polysomes control the biophysical properties of the eukaryotic cytoplasm. HighlightsO_LIPolysomes control the biophysical properties of cytoplasm. C_LIO_LImRNP and GEM mobility is enhanced upon translation inhibition that leads to polysome disassembly C_LIO_LIPerturbation of mRNA levels leads to an increase in cytosolic diffusion. C_LI

cell biology↗

bioRxiv2024

Higher-Order Interactions in Neuronal Function: From Genes to Ionic Currents in Biophysical Models

Neuronal firing patterns are the consequence of precise variations in neuronal membrane potential, which are themselves shaped by multiple ionic currents. In this study, we use biophysical models, statistical methods, and information theory to explore the interaction between these ionic currents and neuron electrophysiological phenotype. We created numerous electrical models with diverse firing patterns. By analyzing these models, we identified intricate relationships between model parameters and electrical features. Our findings show that neuronal activity is often influenced by multiple biophysical model parameters, in a non-additive (i.e. synergistic) fashion. When comparing this with single-cell RNAseq data, we found a contrasting structure: gene expression profiles were dominated by redundancy, reflecting differences in regulatory constraints and sampling diversity. This research sheds light on the complex links between biophysical parameters and neuronal phenotypes.

neuroscience↗

bioRxiv2024

Solution biophysics identifies lipid nanoparticle non-sphericity, polydispersity, and dependence on internal ordering for efficacious mRNA delivery

Designing lipid nanoparticle (LNP) delivery systems with specific targeting, potency and minimal side effects is crucial for their clinical use. However, traditional characterization methods, such as dynamic light scattering, cannot accurately quantify physicochemical properties of LNPs and how they are influenced by the lipid composition and mixing method. Here we structurally characterize polydisperse LNP formulations by applying emerging solution-based biophysical methods that have higher resolution and provide biophysical data beyond size and polydispersity. These techniques include sedimentation velocity analytical ultracentrifugation, field-flow fractionation followed by multi-angle light scattering, and size-exclusion chromatography in-line with synchrotron small-angle X-ray scattering. We show that LNPs have intrinsic polydispersity in size, RNA loading, and shape, which depends on both the formulation technique and lipid composition. Lastly, we predict LNP transfection in vitro and in vivo by examining the relationship between mRNA translation and physicochemical characteristics. Solution-based biophysical methods will be essential for determining LNP structure-function relationships, facilitating the creation of new design rules for LNPs.

bioengineering↗

bioRxiv2025

Prediction of Antibody Non-Specificity using Protein Language Models and Biophysical Parameters

The development of therapeutic antibodies requires optimizing target binding affinity and pharmacodynamics, while ensuring high developability potential, including minimizing non-specific binding. In this study, we address this problem by predicting antibody non-specificity by two complementary approaches: (i) antibody sequence embeddings by protein language models (PLMs), and (ii) a comprehensive set of sequence-based biophysical descriptors. These models were trained on human and mouse antibody data from Boughter et al. (2020) and tested on three public datasets: Jain et al. (2017), Shehata et al. (2019) and Harvey et al. (2022). We show that non-specificity is best predicted from the heavy variable domain and heavy-chain complementary variable regions (CDRs). The top performing PLM, a heavy variable domain-based ESM 1v LogisticReg model, resulted in 10-fold cross-validation accuracy of up to 71%. Our biophysical descriptor-based analysis identified the isoelectric point as a key driver of non-specificity. Our findings underscore the importance of biophysical properties in predicting antibody non-specificity and highlight the potential of protein language models for the development of antibody-based therapeutics. To illustrate the use of our approach in the development of lead candidates with high developability potential, we show that it can be extended to therapeutic antibodies and nanobodies.

bioinformatics↗

bioRxiv2025

In vivo neural activity of electrosensory pyramidal cells: Biophysical characterization and phenomenological modeling

Burst firing is an important property of neuronal activity, thought to enhance sensory encoding. While previous studies show significant differences in burst firing between in vivo and in vitro conditions, how burst firing contributes to neural coding in vivo and how it is modulated by underlying biophysical mechanisms when neurons are under active synaptic bombardments remains poorly understood. Here, we combined intracellular recordings and computational modeling to investigate how cellular and synaptic mechanisms can explain the in vivo firing activity of electrosensory lateral line lobe (ELL) pyramidal cells in Apteronotus leptorhynchus. We developed a biophysically detailed compartmental model incorporating voltage-gated currents, NMDA receptor-mediated Ca2+ influx, Ca2+-activated SK channels, Ca2+ handling, and stochastic synaptic inputs to reproduce in vivo firing activities of ELL pyramidal cells. Specifically, using bifurcation analysis, we identified dynamical transitions between quiescent, tonic, and bursting regimes, governed by interactions among SK conductance, NMDA receptor activation, and applied current. Model parameters were optimized against in vivo data, accurately reproducing action potential waveforms and temporal dynamics, including characteristic bimodal interspike interval distributions reflecting intra- and inter-burst intervals. We further developed a modified Hindmarsh-Rose model incorporating dual adaptation variables and stochastic noise. This simplified phenomenological model successfully captured burst firings comparable to those observed in the biophysical model and recorded data, while replicating diverse firing patterns observed across the population. Finally, parameter sensitivity analysis revealed slow adaptation dynamics and noise intensity as key determinants of spiking variability within cells. Overall, our modeling results demonstrate that in vivo bursting arises from synergistic interactions between intrinsic conductances (e.g., NMDA-SK coupling), Ca2+ mobilization, and synaptic stochasticity, offering a potential reconciliation for discrepancies with in vitro firing activity. The models provide mechanistic insights into how background synaptic activity modulates burst firing and validate simplified frameworks for studying population-level dynamics.

neuroscience↗

bioRxiv2025

Distribution and association of biophysical factors with onion (Allium cepa L.) downy mildew (Peronospora destructor) disease epidemics in northwestern Ethiopia.

Onion (Allium cepa L.) is a significant food bulb crop that can be used for medicine, diverse recipes, and income. However, downy mildew disease limits productivity and production of onion in onion growing-regions. Thus, this survey was conducted to assess the distribution and intensity of onion downy mildew and determine the associations of disease parameters with biophysical factors. During the 2024/2025 onion cropping season, 130 onion fields were surveyed in order to measure biophysical and disease data. The associations of disease parameters and biophysical factors were analyzed using the binary logistic regression model by employing the SAS GENEMODE procedure. The surveys findings verified that downy mildew was 100% prevalent. The highest disease intensity was assessed from Fogera (50.68% and 23.48%) and Libokemkem (45.16% and 21.62%) districts, respectively. High disease incidence (>40%) and severity (>20%) were strongly associated with maturity crop growth stages, field size (>0.25 ha), blub previous crop, late October transplanting, less than four times fungicide application and less than three times land preparation. Lower disease incidence ([≤] 40%) and severity ([≤] 20%) had a strong association with early December transplanting, more than three times land preparation, fungicide spray (>4 times), field size ([≤] 0.25 ha), fields previously planted with cereals, could be considered as relative management options to minimize the diseases effects in onion growing areas of northwest Ethiopia and other similar onion cultivating areas. In addition, In order to develop effective management strategies, future research may examine pathogen variability.

pathology↗

bioRxiv2025

Apical and basolateral plasma membranes in epithelial cells have distinct lipidomes and biophysical properties

Epithelial cell polarization is essential for many physiological processes, including tissue morphogenesis, nutrient absorption, barrier integrity, and directional secretion. A defining feature of such polarization is the separation of plasma membrane lipids and proteins into distinct apical and basolateral compartments. It has long been suggested that the apical compartment is rich in glycolipids and cholesterol and that this composition arises through trafficking of self-assembled membrane domains (termed lipid rafts). However, neither the detailed composition nor the mechanisms of protein and lipid sorting between plasma membrane compartments in epithelial cells have been fully resolved. Particularly, the lipid profile of the basolateral membrane, and consequently the lipid disparity between the apical and basolateral membrane, remain undefined. The general determinants of protein sorting are also poorly understood. We developed a novel method to separately isolate the apical and basolateral plasma membranes and used lipidomics and biophysical profiling to characterize the changes in membrane composition and properties between these compartments in polarized Madin-Darby canine kidney cells. We find that the apical membrane is enriched in cholesterol, saturated lipids, and glycolipids relative to the basolateral membrane, and that its biophysical properties reflect a more ordered environment. Further, we evaluate the longstanding hypothesis that lipid rafts contribute to apical protein trafficking by assessing the relationship between transmembrane domain raft affinity and apical localization. We observed that lipid raft affinity only modestly influences apical versus basolateral sorting. These findings define the distinct compositional and biophysical features of apical and basolateral compartments of epithelial cells and provide mechanistic evidence for their biogenesis.

cell biology↗

bioRxiv2025

Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances

Interspecific comparisons of cell-type-specific gene expression levels can provide information about the evolutionary processes that drove divergence between species. From these comparisons, it is now evident that the predominant mode of gene expression evolution has been stabilizing selection, both on the steady-state (mean) protein levels as well as on the mRNA levels with additional lineage-specific shifts resulting from directional selection. However, as all previous work has used bulk RNA measurements, it has been impossible to determine which of the many cellular processes that contribute to mean abundances are highly constrained and which are more evolutionary labile. Assessing this is further complicated by the expectation that components of complex systems will evolve over time independent of changes in the selective regime so long as the net output of a system (i.e., mean expression) remains near the evolutionary optima. This process, known as evolutionary systems drift (ESD), has been frequently invoked as a non-adaptive explanation for changes in cellular phenotypes but has never been quantitatively tested or accounted for in any statistical test for selective constraints. Here, we develop a new paradigm that addresses both of these open problems simultaneously. Using single-cell expression data and biophysical models, we estimate mRNA transcriptional bursting rates, splicing rates, and decay rates across multiple vertebrate species. We then derive new mathematical results that describe how these various biophysical parameters are expected to co-evolve under ESD and then test whether we need additional evolutionary constraints to explain the divergences in these parameters. We find evidence that the biophysical parameters are indeed evolving in a coordinated manner as predicted by ESD and that there are additional strong constraints on transcriptional bursting, likely as a consequence of selection to reduce noise in expression. More broadly, this work opens up a whole new approach for studying the evolutionary dynamics of complex cellular systems.

evolutionary biology↗

bioRxiv2018

Deciphering the contribution of oriens-lacunosum/moleculare (OLM) cells to intrinsic theta rhythms using biophysical local field potential (LFP) models

Oscillations in local field potentials (LFPs) commonly occur and analyses of them fuel brain function hypotheses. An understanding of the cellular correlates and pathways affecting LFPs is needed but many overlapping pathways in vivo makes this difficult to achieve. A prevalent LFP rhythm in the hippocampus is theta (3-12 Hz). Theta rhythms emerge intrinsically in an in vitro whole hippocampus preparation and thus can be produced by local interactions between interneurons and pyramidal (PYR) cells. Overlapping pathways are much reduced in this preparation making it possible to decipher the contribution of different cell types to LFP generation. We focus on oriens-lacunosum/moleculare (OLM) cells as a major class of interneurons in the hippocampus. They can influence PYR cells through two distinct pathways, (i) by direct inhibition of PYR cell distal dendrites, and (ii) by indirect disinhibition of PYR cell proximal dendrites by inhibiting bistratified cells (BiCs) that target them. We use previous inhibitory network models and build biophysical LFP models using volume conductor theory. We assess the effect of OLM cells to ongoing intrinsic LFP theta rhythms by directly comparing our model LFP features with experiment. We find that robust LFP theta responses adhering to reproducible experimental criteria occur only for particular connectivities between OLM cells and BiCs. Decomposition of the LFP reveals that OLM cell inputs onto the PYR cell regulate robustness of LFP responses without affecting average power and that the robust response depends on co-activation of distal inhibition and basal excitation. We use our models to estimate the spatial extent of the region generating LFP theta rhythms, leading us to predict that about 22,000 PYR cells participate in generating the LFP theta rhythm. Besides allowing us to understand OLM cells contributions to intrinsic theta rhythms, our work can drive hypothesis developments of cellular contributions in vivo.\n\nAuthor SummaryOscillatory local field potentials (LFPs) are extracellularly recorded potentials that are widely used to interpret information processing in the brain. For example, theta LFP rhythms (3-12 Hz) are correlated with memory processing and it is known that particular inhibitory cell types control their existence. As such, it is critical for us to appreciate how various cell types contribute to the characteristics of LFP rhythms. A precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established. However, it is difficult to assess cellular contributions in vivo because of many spatiotemporally overlapping pathways that prevent the unambiguous separation of signals. Using an in vitro preparation that exhibits intrinsic theta (3-12 Hz) rhythms and where there is much less overlap, we build biophysical LFP models to explore cell contributions to ongoing intrinsic theta rhythms. We uncover distinct contributions from different cell types and show that robust theta rhythms depend specifically on one of the cell types. We are able to determine this because our LFP models have direct links with experiment and we are able to perform thousands of simulations.

neuroscience↗

bioRxiv2018

Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

Bacteriocins are ribosomally produced antimicrobial peptides that represent an untapped source of promising antibiotic alternatives. However, inherent challenges in isolation and identification of natural bacteriocins in substantial yield have limited their potential use as viable antimicrobial compounds. In this study, we have developed an overall pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using a machine-learning algorithm and a simple biophysical trait filter to generate minimal 20 amino acid peptide candidates that can be readily synthesized and evaluated for activity. We generated 28,895 total 20-mer peptides and scored them for charge, -helicity, and hydrophobic moment, allowing us to identify putative peptide sequences with the highest potential for interaction and activity against bacterial membranes. Of those, we selected sixteen sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. We show that bacteriocin-based peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against E. coli and P. aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination, to create an original approach to rapidly discover novel bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

microbiology↗

bioRxiv2018

Data-Constrained Biophysical Modeling Of Human VIM To Assess Contributions Of Intrinsic Oscillatory Activity To Essential Tremor

Essential tremor (ET) is the most common movement disorder, in which the primary symptom is a prominent, involuntary 4-10 Hz rhythmic movement. The presence of tremor frequency oscillations (TFOs) in the ventral intermediate nucleus of the thalamus (VIM) is well-established, but it is often assumed that it is driven by cerebellar tremor frequency activity, while the role of intrinsic oscillatory activity in VIM is not clear. An improved understanding of the mechanisms of tremor and non-tremor frequency activity in VIM is critical to the development of improved pharmacological and neuromodulatory therapies. Starting from a canonical model of thalamus, we developed a biophysically-principled computational model of tremor field activity in the VIM, coupled with the thalamic reticular nucleus (TRN). We simulated TFOs in the model generated either by extrinsic tremor-periodic drive or intrinsic VIM-TRN interaction to understand whether these networks exhibited distinct biophysical properties, which may impact the efficacy of pharmacological or stimulation treatment for TFOs. Extrinsic and intrinsic TFOs in the model depended on T-type Ca2+ channels in different ways. Each also depended on GABA modulation in a site- and type-specific manner. These results suggested that efficacy of pharmacological manipulations may depend upon the mechanisms generating TFOs in VIM. Simulated non-tremor-related motor activity from cerebellum decreased extrinsic but increased intrinsic TFOs. Our results suggest that both mechanisms may be important to understand the emergence and cessation of TFOs in VIM and lead to experimentally testable predictions on how to modulate tremor frequency activity to improve treatment strategies for ET.\n\nSignificance StatementEssential Tremor (ET) is a movement disorder in which the primary symptom is a prominent, involuntary, and rhythmic shaking, often of the hands. Electrical activity in many areas of the brain exhibit rhythmicity related to the patients tremor. One such area resides in a structure called the thalamus, but it is not fully known what gives rise to tremor-related activity. We created a computational model of this activity, which suggested how to differentiate tremor mechanisms and how these differences may contribute to other impairments in ET. Knowledge of the biophysical mechanisms contributing to tremor can ultimately lead to improvements in treatments to alleviate symptoms of ET.

neuroscience↗