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bioRxiv2026

Latent biophysical diffusion properties underlying cerebral microstructure revealed through multimodal MRI covariation in the squirrel monkey

Diffusion MRI provides multiple quantitative descriptors of neural tissue derived from distinct physical and biophysical models, each probing different aspects of cerebral microstructure. While these metrics are often interpreted independently, their joint statistical structure may encode information about latent biophysical properties that constrain and organize brain microarchitecture. Here, we investigate whether stable patterns of multimodal diffusion MRI covariation reveal underlying biophysical diffusion properties of cerebral microstructure in the squirrel monkey (Saimiri sciureus). Using a high-resolution multimodal diffusion MRI dataset acquired at 11.7T (400 m isotropic resolution) from 15 adult subjects, we analyzed tensor-based metrics alongside advanced multicompartment and axonal models, including NODDI and ActiveAx. Voxelwise and region-level correlation analyses were performed across multiple spatial scales to examine the structure, redundancy, and stability of relationships among diffusion-derived maps. We show that metrics originating from conceptually distinct models consistently organize into a low-dimensional structure characterized by stable clusters of covariation, robust to changes in parcellation and spatial aggregation. These patterns cannot be explained by trivial metric redundancy alone, but instead suggest convergence toward a reduced set of effective biophysical degrees of freedom governing diffusion behavior in neural tissue, including axonal organization, neurite density, orientation dispersion, and isotropic diffusion components. We interpret these covariation structures as manifestations of latent biophysical diffusion properties--emergent tissue states that are not directly observable through any single metric but become apparent through their structured relationships. From a systems neuroscience perspective, the stability of these latent dimensions supports the view that adult cerebral microstructure reflects the steady-state outcome of neurodevelopmental dynamics operating under biophysical constraints. Rather than providing a regional atlas, this work proposes a conceptual framework for interpreting multimodal diffusion MRI as a projection of an underlying low-dimensional biophysical state space organizing cerebral microstructure in primate brains. Key pointsO_LIMultimodal diffusion MRI metrics derived from distinct biophysical models exhibit stable and non-trivial patterns of covariation, revealing a low-dimensional organization of cerebral microstructure. C_LIO_LIThese covariation structures suggest the existence of latent biophysical diffusion properties that are not directly observable through individual metrics but emerge from their structured relationships across spatial scales. C_LIO_LIThe stability of these latent dimensions supports the interpretation of adult cerebral microstructure as a structurally stable outcome of neurodevelopmental dynamics governed by biophysical constraints. C_LI

neuroscience↗

bioRxiv2026

Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis

Protein evolution is constrained by multidimensional biophysical factors, in which mutations that enhance one property often compromise another. Antibodies represent an extreme case: they evolve rapidly to bind diverse antigens, yet mutations that improve affinity can disrupt folding, reduce cell-surface trafficking, or promote self-reactivity, and are typically selected against during affinity maturation. Though biophysical characterization of individual antibodies suggests that such trade-offs are pervasive, their impact on antibody evolutionary trajectories remains unclear, in part because existing high-throughput biophysical methods rely on heterologous systems that are often poorly suited for human proteins. Here, we develop a high-throughput platform to quantify multiple biophysical parameters of large libraries of full-length proteins that are natively synthesized, processed, and displayed on human cells. We apply this approach to a human antibody lineage that matures to recognize divergent SARS-CoV-2 variants by measuring the surface expression, antigen affinity, and self-reactivity for all 213 possible evolutionary intermediates between the unmutated and mature sequences. These measurements reveal that mutations differentially affect these biophysical properties - in some cases, improving one property at the expense of another. We leverage these data to compute the likelihood of all possible evolutionary paths, finding that very few paths can navigate these multidimensional requirements. The few accessible paths acquire mutations in a specific order that either circumvent trade-offs between biophysical properties or offset deleterious effects on one property with beneficial effects on another. By determining the structures of the ancestral and evolved antibodies, we find that these coordinated mutational effects arise from a conformational rearrangement that alleviates steric clashes and reshapes the biophysical landscape, enabling otherwise inaccessible mutational paths. Together, this work defines the multidimensional biophysical constraints and structural mechanisms that govern antibody evolution and establishes a general framework for mapping and predicting the biophysical effects of mutations in human proteins.

evolutionary biology↗

bioRxiv2025

Biophysical model for predicting muscle short-range stiffness during movement

Musculoskeletal simulations can offer valuable insight into how the properties of our musculoskeletal system influence the biomechanics of our daily movements. One such property is muscles history-dependent initial resistance to stretch, also known as short-range stiffness, which is key to stabilizing movements in response to external perturbations. Short-range stiffness is poorly captured by existing musculoskeletal simulations since they employ phenomenological Hill-type muscle models that lack the mechanisms underlying short-range stiffness. While it has been previously shown that biophysical cross-bridge models can reproduce muscle short-range-stiffness, it is unclear which specific biophysical properties are necessary to capture history-dependent muscle force responses in behaviorally relevant conditions. Here, we tested the ability of various biophysical cross-bridge models to reproduce empirical short-range stiffness and its history-dependent changes across a broad range of behaviorally relevant length changes and activation levels, using an existing dataset on permeabilized rat soleus muscle fibers (N = 11). We found that a biophysical cross-bridge model with cooperative myofilament activation reproduced the effects of muscle activation (R2 = 0.86), stretch amplitude (R2 = 0.71) and isometric recovery time (R2 = 0.79) on history-dependent changes in short-range stiffness after shortening. Similar results were obtained when the cross-bridge distribution of the biophysical model was approximated by a Gaussian (R2 = 0.73 - 0.88), but at a 20 times lower computational cost. These effects could not be reproduced by either a biophysical cross-bridge model without cooperative myofilament activation or a Hill-type model (R2 < 0.5). The reduced computational demand of the Gaussian-approximated models facilitates implementing biophysical cross-bridge models with cooperative myofilament activation in musculoskeletal simulations to improve the prediction of short-range stiffness during movements.

physiology↗

bioRxiv2026

A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks

Recurrent neural networks (RNNs) have proven to be highly successful in emulating human-like cognitive functions such as working memory. In recent years, RNNs are evolving to incorporate more biophysical realism to produce more plausible predictions on how cognitive tasks are solved in real neural circuits. However, there are major challenges in constructing and training networks with the complex and nonlinear properties of real neurons. A major component of the success of RNNs is that they share the same mathematical base as deep neural networks, permitting highly efficient optimization of model parameters using standard deep learning techniques. To do so, they use abstract representations of neurons which fail to capture the impact of cell-level biophysical and morphologic properties that may benefit network-level function. Expanding task-trained RNNs with biophysical properties such as dendrites and active ionic currents poses substantial challenges, as it moves these models away from the validated training regimes known to be highly effective for RNNs. To address this gap, we developed a biophysically detailed reservoir computing (BRC) framework with the goal of extracting mechanistic insights from biophysical neural models, and propose that these insights can be used to guide model choices that will work for specific categories of cognitive tasks. The BRC network was constructed with synaptically coupled excitatory and inhibitory cells, in which the excitatory cells include multicompartment biophysically active dendrites; motivated by empirical studies suggesting dendrites have desirable computational benefits (e.g. pattern classification and coincidence detection). We trained the BRC network to do a simplified working memory task where it had to maintain the representation of an extrinsic "cue" input. We studied the impact of extrinsic input time constants (fast AMPA vs slow NMDA) and location (dendrite vs soma) on the ability of a network to solve the task. Our results revealed that cue inputs through NMDA receptors are particularly efficient for solving the working memory task. Further, the properties of NMDA receptors are uniquely suited for cue inputs delivered at the dendrite, as networks trained with dendritic AMPA cue inputs failed to solve the task. Detailed examination of the cell and network dynamics that solve the task reveals distinct local network configurations and computing principles for the different types of extrinsic input. Overall, much like the body of mechanistic insights that have underpinned the success of training RNNs, this study lays the groundwork for applying the BRC framework to train biophysically detailed neural models to solve complex human-like cognitive tasks.

neuroscience↗

bioRxiv2026

Undergraduate Biophysical Chemistry Series: Teaching through a Combination of a Purpose-built Textbook, Research-derived Biomolecular Samples and Computer Labs

Biophysics is a rapidly advancing field with an incredible breadth of topics. Thus, undergraduate biophysics instructors have to strategize and decide what topics they will cover in their courses. Educational institutions utilize a variety of biophysics textbooks. A common deficiency of each of the existing texts is that it serves well a given set of topics (theory, illustrations, practice problems) and leaves out other areas. A typical example includes good theory and problems for thermodynamics and kinetics while presenting molecular dynamics and various spectroscopic methods in a lacking or outdated way. The authors of this manuscript teach a capstone Biophysical Chemistry three-quarter series (Western Washington University/WWU, Bellingham, WA) which ideally should resonate with the general and major-specific courses the students take within their major at WWU. To achieve this goal and to enrich the traditional lecture-based delivery, the instructors have developed and brought together key pedagogical elements: purpose-built online textbook with a uniform structure of the academic content and practice problems, a study sample (oligopeptide) of biophysical significance with a growing set of experimental and computational data and student-centric in-class activities including computer labs. Our Biophysical series emphasizes concepts and methods of computational structural biology (Molecular Dynamics) and spectroscopic approaches (IR, UV and NMR). Here we describe the details of our integrative approach, summarize key outcomes and chart ways to advance the biophysical chemistry series further. Our textbook can be found through LibreText.

scientific communication and education↗

bioRxiv2017

Prospects for recurrent neural network models to learn RNA biophysics from high-throughput data

RNA is a functionally versatile molecule that plays key roles in genetic regulation and in emerging technologies to control biological processes. Computational models of RNA secondary structure are well-developed but often fall short in making quantitative predictions of the behavior of multi-RNA complexes. Recently, large datasets characterizing hundreds of thousands of individual RNA complexes have emerged as rich sources of information about RNA energetics. Meanwhile, advances in machine learning have enabled the training of complex neural networks from large datasets. Here, we assess whether a recurrent neural network model, Ribonet, can learn from high-throughput binding data, using simulation and experimental studies to test model accuracy but also determine if they learned meaningful information about the biophysics of RNA folding. We began by evaluating the model on energetic values predicted by the Turner model to assess whether the neural network could learn a representation that recovered known biophysical principles. First, we trained Ribonet to predict the simulated free energy of an RNA in complex with multiple input RNAs. Our model accurately predicts free energies of new sequences but also shows evidence of having learned base pairing information, as assessed by in silico double mutant analysis. Next, we extended this model to predict the simulated affinity between an arbitrary RNA sequence and a reporter RNA. While these more indirect measurements precluded the learning of basic principles of RNA biophysics, the resulting model achieved sub-kcal/mol accuracy and enabled design of simple RNA input responsive riboswitches with high activation ratios predicted by the Turner model from which the training data were generated. Finally, we compiled and trained on an experimental dataset comprising over 600,000 experimental affinity measurements published on the Eterna open laboratory. Though our tests revealed that the model likely did not learn a physically realistic representation of RNA interactions, it nevertheless achieved good performance of 0.76 kcal/mol on test sets with the application of transfer learning and novel sequence-specific data augmentation strategies. These results suggest that recurrent neural network architectures, despite being naive to the physics of RNA folding, have the potential to capture complex biophysical information. However, more diverse datasets, ideally involving more direct free energy measurements, may be necessary to train de novo predictive models that are consistent with the fundamentals of RNA biophysics.\n\nAuthor SummaryThe precise design of RNA interactions is essential to gaining greater control over RNA-based biotechnology tools, including designer riboswitches and CRISPR-Cas9 gene editing. However, the classic model for energetics governing these interactions fails to quantitatively predict the behavior of RNA molecules. We developed a recurrent neural network model, Ribonet, to quantitatively predict these values from sequence alone. Using simulated data, we show that this model is able to learn simple base pairing rules, despite having no a priori knowledge about RNA folding encoded in the network architecture. This model also enables design of new switching RNAs that are predicted to be effective by the \"ground truth\" simulated model. We applied transfer learning to retrain Ribonet using hundreds of thousands of RNA-RNA affinity measurements and demonstrate simple data augmentation techniques that improve model performance. At the same time, data diversity currently available set limits on Ribonets accuracy. Recurrent neural networks are a promising tool for modeling nucleic acid biophysics and may enable design of complex RNAs for novel applications.

biophysics↗

bioRxiv2023

The SCN8A p.(Gly1625Arg) variant associated with developmental and epileptic encephalopathy causes complex biophysical changes and reduced neuronal firing

BackgroundMutations in the SCN8A gene, encoding the voltage-gated sodium channel NaV1.6, lead to various neurodevelopmental disorders. The SCN8A p.(Gly1625Arg) mutation (NaV1.6G1625R) was identified in a patient diagnosed with developmental epileptic encephalopathy (DEE), presenting with moderate epilepsy and severe developmental delay. MethodsWe performed biophysical and neurophysiological characterizations of NaV1.6G1625R in Neuro-2a cells and cultured hippocampal neurons, followed by computational modeling to determine the impact of its heterozygous expression on cortical neuron function. FindingsVoltage-clamp analyses of NaV1.6G1625R demonstrated a heterogeneous mixture of gain-and loss-of-function properties, including reduced current amplitudes, a marked increase in the time constant of fast voltage-dependent inactivation and a depolarizing shift in the voltage dependence of inactivation. Recordings in transfected cultured neurons showed that these intricate biophysical properties had a minor effect on neuronal excitability when firing relayed on both endogenous and transfected NaV channels. Conversely, there was a marked reduction in the number of action potentials when firing was driven by the transfected mutant NaV1.6 channels. Computational modeling of mature cortical neurons further revealed a mild reduction in neuronal firing when mimicking the patients heterozygous NaV1.6G1625R expression. Structural modeling of NaV1.6G1625R and a double-mutant cycle analysis suggested the possible formation of pathophysiologically-relevant cation-{pi} interaction between R1625 and F1588, affecting the voltage dependence of inactivation. InterpretationOur analyses demonstrate a complex combination of gain and loss-of-function changes resulting in an overall mild reduction in neuronal firing, related to a perturbed interaction network within the voltage sensor domain. FundingISF, DFG, BMBF, The Hartwell Foundation, ICRF, ISCA Research in contextO_ST_ABSEvidence before this studyC_ST_ABSMutations in the SCN8A gene, encoding the voltage-gated sodium channel NaV1.6, result in multiple neurodevelopmental syndromes ranging from benign epilepsy to developmental delay without epilepsy or developmental epileptic encephalopathy (DEE). Recent studies established that most DEE-causing SCN8A mutations result in a gain of function effect. However, several SCN8A mutations that diverge from this pattern were described. Added value of this studyWe performed a multi-tiered study of the SCN8A p.(Gly1625Arg) variant (NaV1.6G1625R), identified in a patient with atypical DEE presentation, featuring moderate epilepsy that is well controlled by the sodium channel blocker Oxcarbazepine, along with profound stagnated developmental delay. This variant is positioned within the S4 segment of domain IV, a critical region for NaV1.6 function, where pathogenic variants were shown to cause either a loss or gain of channel function, but often with mixed biophysical alterations. Our biophysical characterization of NaV1.6G1625R in Neuro-2a cells demonstrated complex gain-and loss-of-function properties, cumulating to reduced firing in cultured hippocampal neurons and computational modeling of mature cortical neurons, demonstrating an overall loss-of-function effect. Implications of all the available evidenceOur results indicate the necessity for combined biophysical and neuronal characterization of individual SCN8A variants, especially those presenting with complex biophysical changes or atypical clinical presentation. Moreover, while sodium channel blockers are the recommended treatment for SCN8A variants associated with gain-of-function, additional considerations may be needed for DEE-causing variants that are associated with mild loss-of-function.

physiology↗

bioRxiv2024

Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

Biophysiscal neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match physiological measurements at scale or such that they perform computational tasks. Here, we describe a framework for simulation of detailed biophysical models in neuroscience--JO_SCPLOWAXLEYC_SCPLOW--which addresses this challenge. By making use of automatic differentiation and GPU acceleration, JO_SCPLOWAXLEYC_SCPLOW opens up the possibility to efficiently optimize large-scale biophysical models with gradient descent. We show that JO_SCPLOWAXLEYC_SCPLOW can learn parameters of biophysical neuron models with several hundreds of parameters to match voltage or two photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. We then demonstrate that JO_SCPLOWAXLEYC_SCPLOW makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a feedforward network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Our analyses show that JO_SCPLOWAXLEYC_SCPLOW dramatically improves the ability to build large-scale data- or task-constrained biophysical models, creating unprecedented opportunities for investigating the mechanisms underlying neural computations across multiple scales.

neuroscience↗

bioRxiv2023

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates

PurposeIdentify differences between the acquisition-time efficient axisymmetric diffusion kurtosis imaging (DKI) model and standard DKI and their consequences on biophysical parameter estimates using standard DKI parameters as the ground truth. MethodsNoise-free, synthetic diffusion MRI (dMRI) human brain data are generated using standard DKI and fitted with axisymmetric DKI and standard DKI. Then, the five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, attainable with both DKI models are computed. Next, the five biophysical parameters axon water fraction and dispersion, extra axonal parallel and perpendicular diffusivity and intra axonal parallel diffusivity are estimated from the AxTM using the WMTI-Watson model. Finally, the number of substantially differing voxels (SDV), defined as voxels where estimation results of both DKI models differ more than 5%, is calculated for the AxTM and the biophysical parameters. ResultsFor the AxTM, the number of SDV was biggest for the parallel (26%) and perpendicular (51%) kurtosis while the other three AxTM had very few SDV (less than 5%). The biophysical parameters had much more SDV than the AxTM from which they were computed, ranging from 29% to 50%. ConclusionAxisymmetric DKI is a viable alternative to standard DKI in studies focusing on effects based on the parallel and perpendicular diffusion and mean of the kurtosis tensor. However, our findings urge caution when using axisymmetric DKI to investigate effects based on the parallel and perpendicular kurtosis or use it to estimate the biophysical parameters.

biophysics↗

bioRxiv2023

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike proteins receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBDs biophysical properties contribute to SARS-CoV-2s epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the discovery of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by binding constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto a epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics. Significance StatementThis research presents a biophysical model that maps the molecular properties of SARS-CoV-2s receptor binding domain into an epistatic fitness landscape. By linking the binding affinities of the virus to its epidemic fitness, we offer a powerful tool for understanding and predicting the emergence and success of new viral variants. Our model, validated with real-world data and informed by theoretical insights, provides a foundation for interpreting the evolutionary trajectory of past pandemics and predicting those of the future. The adaptability of this biophysical model extends to the key proteins of other viruses as well, signifying its potential in guiding public health interventions, and advancing our understanding of viral evolution.

biophysics↗

bioRxiv2024

Biophysical characterization of high-confidence, small human proteins

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. Additionally, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from non-coding ones in otherwise ambiguous cases. Why it MattersThe identification of proteins smaller than 100 amino acids has lagged being that of larger proteins in part because their small size makes proteomic and genomic approaches more challenging. However, small proteins can perform significant functions. There are now enough proteins with high confidence of function to enable meaningful comparison with larger proteins, and to determine whether putative small proteins might be identified, based on their biophysical properties. As an example, we found that small proteins often contain transmembrane helices but are relatively devoid of beta sheets, which helped to support the annotation of a putative protein as functional. We also identified a wide range of biological functions and localization for small proteins, including many with disease associations.

biophysics↗

bioRxiv2019

Biophysical principles of choanoflagellate self-organization

Inspired by the patterns of multicellularity in choanoflagellates, the closest living relatives of animals, we quantify the biophysical processes underlying the morphogenesis of rosette colonies in the choanoflagellate Salpingoeca rosetta. We find that rosettes reproducibly transition from an early stage of 2D growth to a later stage of 3D growth, despite the underlying stochasticity of the cell lineages. We postulate that the extracellular matrix (ECM) exerts a physical constraint on the packing of proliferating cells, thereby sculpting rosette morphogenesis. Our perturbative experiments coupled with biophysical simulations demonstrates the fundamental importance of a basally-secreted ECM for rosette morphogenesis. In addition, this yields a morphospace for the shapes of these multicellular colonies, consistent with observations of a range of choanoflagellates. Overall, our biophysical perspective on rosette development complements previous genetic perspectives and thus helps illuminate the interplay between cell biology and physics in regulating morphogenesis.\n\nSignificance statementComparisons among animals and their closest living relatives, the choanoflagellates, have begun to shed light on the origin of animal multicellularity and development. Here we complement previous genetic perspectives on this process by focusing on the biophysical principles underlying colony morphology and morphogenesis. Our study reveals the crucial role of the extracellular matrix in shaping the colonies and leads to a phase diagram that delineates the range of morphologies as a function of the biophysical mechanisms at play.

biophysics↗

bioRxiv2019

Biophysical Basis of Hb-S Polymerization in Red Blood Cell Sickling

Sickle cell disease (SCD) is an autosomal recessive genetic disease caused by the Glu6Val mutation in the {beta} chain (Hb) of the oxygen-carrying hemoglobin protein in sicklemia patients. In the molecular pathogenesis of SCD, the sickle hemoglobin (Hb-S) polymerization is a major driver for structural deformation of red blood cells, i.e. red blood cell (RBC) sickling. Biophysically, it still remains elusive how this SCD-linked E6V mutation leads to Hb-S polymerization in RBC sickling. Therefore, with a comprehensive set of analysis of experimental Hb structures, this letter highlights electrostatic repulsion as a key biophysical mechanism of Hb-S polymerization in RBC sickling, which provides atomic-level insights into the functional impact of the SCD-linked E6V substitution from a biophysical point of view.\n\nSIGNIFICANCEDuring the past 25 years, a total of 104 Hb-related structures have been deposited in PDB. For the first time, this article presents a comprehensive set of electrostatic analysis of the 104 experimental structures, highlighting electrostatic repulsion as a fundamental biophysical mechanism for Hb-S polymerization in RBC sickling. The structural and electrostatic analysis here also provides biophysical insights into the functional impact of the SCD-linked E6V substitution.

biophysics↗

bioRxiv2019

Biophysical mechanism of ultrafast helical twisting contraction in the giant unicellular ciliate Spirostomum ambiguum

The biophysical mechanism of cytoskeletal structures has been fundamental to understanding of cellular dynamics. Here, we present a mechanism for the ultrafast contraction exhibited by the unicellular ciliate Spirostomum ambiguum. Powered by a Ca2+ binding myoneme mesh architecture, Spirostomum is able to twist its two ends in the same direction and fully contract to 75% of its body length within five milliseconds, followed by a slow elongation mechanism driven by the uncoiling of the microtubules. To elucidate the principles of this rapid contraction and slow elongation cycle, we used high-speed imaging to examine the same-direction coiling of the two ends of the cell and immunofluorescence techniques to visualize and quantify the structural changes in the myoneme mesh, microtubule arrays, and the cell membrane. Lastly, we provide support for our hypotheses using a simple physical model that captures key features of Spirostomums ultrafast twisting contraction. SIGNIFICANCEUltrafast movements are ubiquitous in nature, and some of the most fascinating ultrafast biophysical systems are found on the cellular level. Quantitative studies and models are key to understand the biophysics of these fast movements. In this work, we study Spirostomums ultrafast contraction by using high-speed imaging, labeling relevant cytoskeletal structures, and building a physical model to provide a biophysical mechanism especially of the helical same direction twisting of this extremely large single cell organism. Deeper understanding of how single cells can execute extreme shape changes hold potential for advancing basic cell biophysics and also inspire new cellular inspired actuators for engineering applications.

biophysics↗

bioRxiv2020

Biophysical modeling of the neural origin of EEG and MEG signals

Electroencephalography (EEG) and magnetoencephalography (MEG) are among the most important techniques for non-invasively studying cognition and disease in the human brain. These signals are known to originate from cortical neural activity, typically described in terms of current dipoles. While the link between cortical current dipoles and EEG/MEG signals is relatively well understood, surprisingly little is known about the link between different kinds of neural activity and the current dipoles themselves. Detailed biophysical modeling has played an important role in exploring the neural origin of intracranial electric signals, like extracellular spikes and local field potentials. However, this approach has not yet been taken full advantage of in the context of exploring the neural origin of the cortical current dipoles that are causing EEG/MEG signals. Here, we present a method for reducing arbitrary simulated neural activity to single current dipoles. We find that the method is applicable for calculating extracranial signals, but less suited for calculating intracranial electrocorticography (ECoG) signals. We demonstrate that this approach can serve as a powerful tool for investigating the neural origin of EEG/MEG signals. This is done through example studies of the single-neuron EEG contribution, the putative EEG contribution from calcium spikes, and from calculating EEG signals from large-scale neural network simulations. We also demonstrate how the simulated current dipoles can be used directly in combination with detailed head models, allowing for simulated EEG signals with an unprecedented level of biophysical details. In conclusion, this paper presents a framework for biophysically detailed modeling of EEG and MEG signals, which can be used to better our understanding of non-inasively measured neural activity in humans. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/181875v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@47f029org.highwire.dtl.DTLVardef@e8244dorg.highwire.dtl.DTLVardef@a765c1org.highwire.dtl.DTLVardef@96550_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICurrent dipoles are computed from biophysically detailed simulated neuron and network activity C_LIO_LIExtracted current dipoles allow for accurate computation of EEG and MEG signals in simplified and detailed head models C_LIO_LICurrent-diplole approximation generally not suitable for accurate calculations of ECoG signals C_LIO_LIMethod provides biophysics-based link between detailed neural activity and systems-level signals C_LI

neuroscience↗

bioRxiv2020

Functional drug susceptibility testing based on biophysical measurements predicts patient outcome in glioblastoma patient-derived neurosphere models

Functional precision medicine aims to match each cancer patient to the most effective treatment by performing ex vivo drug susceptibility testing on the patients tumor cells. Despite promising feasibility studies, functional drug susceptibility testing is not yet used in clinical oncology practice to make treatment decisions. Often, functional testing approaches have measured ex vivo drug response using metabolic assays such as CellTiter-Glo, which measures ATP as a proxy for numbers of viable cells. As a complement to these existing metabolic drug response assays, we evaluated whether biophysical assays based on cell mass (the suspended microchannel resonator mass assay) or size as measured by microscopy (the IncuCyte assay) could be used as a readout for ex vivo drug response. Using these biophysical assays, we profiled the ex vivo temozolomide responses of a retrospective cohort of 70 glioblastoma patient-derived neurosphere models with matched clinical outcomes and found that both biophysical assays predicted patients overall survival with similar power to MGMT promoter methylation, the clinical gold standard biomarker for predicting temozolomide response in glioblastoma. These findings suggest that biophysical assays could be a useful complement to existing metabolic approaches as "universal biomarkers" to measure sensitivity or resistance to anti-cancer drugs with a wide variety of cytostatic or cytotoxic mechanisms. One-sentence summaryBy using biophysical assays to perform ex vivo drug susceptibility testing on 70 glioblastoma patient-derived neurosphere models, we find that functional testing predicts the duration that patients survive when treated with temozolomide, the standard of care chemotherapy.

cancer biology↗

bioRxiv2023

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference

Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics. Author summaryA central problem in computational neural modeling is estimating model parameters that can account for observed activity patterns. While several techniques exist to perform parameter inference in special classes of abstract neural models, there are comparatively few approaches for large scale biophysically detailed neural models. In this work, we describe challenges and solutions in applying a deep learning based statistical framework to estimate parameters in a biophysically detailed large scale neural model, and emphasize the particular difficulties in estimating parameters for time series data. Our example uses a multi-scale model designed to connect human MEG/EEG recordings to the underlying cell and circuit level generators. Our approach allows for crucial insight into how cell-level properties interact to produce measured neural activity, and provides guidelines for diagnosing the quality of the estimate and uniqueness of predictions for different MEG/EEG biomarkers.

neuroscience↗

bioRxiv2024

Biophysics-based protein language models for protein engineering

Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure, and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose Mutational Effect Transfer Learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure, and energetics. We finetune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity, and fluorescence. METL excels in challenging protein engineering tasks like generalizing from small training sets and position extrapolation, although existing methods that train on evolutionary signals remain powerful for many types of experimental assays. We demonstrate METLs ability to design functional green fluorescent protein variants when trained on only 64 examples, showcasing the potential of biophysics-based protein language models for protein engineering.

bioinformatics↗