SEARCH · Search bioRxiv

Results for “Biophysics”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36Linked to original sources

bioRxiv2026

A framework for the organization of microtubules in developing neurons

The development and physiology of neurons rely on their microtubule organization, which is characterized by plus-end-out oriented microtubules in the axon and a mix of plus-end-out and plus-end-in oriented microtubules in dendrites. This orientational pattern is established early in neuronal development and is tightly linked to axon-dendrite differentiation. Even though multiple potentially relevant mechanisms have been proposed, fundamental questions remain: How does the microtubule organization in neurons emerge, and how does a neuron develop a single axon and multiple dendrites? Here, we address these questions at two distinct, complementary levels: at a higher level by proposing a conceptual framework, in which we classify mechanisms into three categories based on how they contribute to the microtubule organization: orientational bias, parallel amplification, and polarization; at a lower level we build a biophysical model that incorporates multiple mechanisms of microtubule dynamics in a neuron, from which, using analytical calculations and simulations, we derive insights into the emergence of microtubule organization in developing neurons. We show that geometrical effects alone can confer a bias in microtubule orientation. Parallel amplification then enhances the resulting polarity. Coupling multiple neurites to a common cell body that serves as a shared reservoir of resources allows for a polarization mechanism that ensures that the microtubule organization of one neurite becomes axonal while all others are dendritic. This framework unifies diverse molecular observations and yields experimentally testable predictions about microtubule self-organization in early neuronal development. Author summaryNeurons communicate through long protrusions called neurites, which are of two types: dendrites, which receive signals, and axons, which send signals. Their development relies primarily on microtubules, which are polar filaments with two distinct ends, known as the plus and minus ends. Microtubules self-organize into functional architectures that are significantly different between axons and dendrites. In axons, all microtubules point their plus end away from the cell body, whereas in dendrites, they point either towards the cell body or have mixed orientations depending on the species. This orientation guides intracellular transport by motors and is closely linked to whether a neurite develops into an axon or a dendrite. Despite decades of research identifying individual mechanisms, the bigger picture behind the emergence of microtubule orientation in neurons remains unclear. Here, we construct a conceptual framework and a biophysical model to identify the principles underlying the emergence of microtubule orientation in developing neurons. Our conceptual framework provides a high-level perspective on how individual mechanisms influence microtubule organization in neurites. In our concrete biophysical model, we study a selection of mechanisms to gain specific, quantitative insight into the organizational process. We propose a minimal model of a neuron that exhibits neuronal polarization, giving rise to a single axon-like neurite and multiple dendrite-like ones, consistent with experimental observations. This in silico neuron helps to explain how neurons break symmetry during development and provides a systematic way to generate and test new hypotheses about neuronal polarity.

biophysics↗

bioRxiv2015

Synergy of AMPA and NMDA receptor currents in dopaminergic neurons: a modeling study

Dopaminergic (DA) neurons display two modes of firing: low-frequency tonic and high-frequency bursts. The high frequency firing within the bursts is attributed to NMDA, but not AMPA receptor activation. In our models of the DA neuron, both biophysical and abstract, the NMDA receptor current can significantly increase their firing frequency, whereas the AMPA receptor current is not able to evoke high-frequency activity and usually suppresses firing. However, both currents are produced by glutamate receptors and, consequently, are often co-activated. Here we consider combined influence of AMPA and NMDA synaptic input in the models of the DA neuron. Different types of neuronal activity (resting state, low frequency, or high frequency firing) are observed depending on the conductance of the AMPAR and NMDAR currents. In two models, biophysical and reduced, we show that the firing frequency increases more effectively if both receptors are co-activated for certain parameter values. In particular, in the more quantitative biophysical model, the maximal frequency is 40% greater than that with NMDAR alone. The dynamical mechanism of such frequency growth is explained in the framework of phase space evolution using the reduced model. In short, both the AMPAR and NMDAR currents flatten the voltage nullcline, providing the frequency increase, whereas only NMDA prevents complete unfolding of the nullcline, providing robust firing. Thus, we confirm a major role of the NMDAR in generating high-frequency firing and conclude that AMPAR activation further significantly increases the frequency.

Neuroscience↗

bioRxiv2020

Electrophysiology prediction of single neurons based on their morphology

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon). In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model. Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

neuroscience↗

bioRxiv2020

A quantitative principle to understand 3D cellular connectivity in epithelial tubes

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended/folded epithelia is achieved by a surface tension energy minimization mechanism that leads to a novel cellular shape: the scutoid. However, further cellular and tissue level implications of this new developmental paradigm remain unknown. Here we focus on the relationship between this complex cellular shape and the connectivity between cells. We address this problem using a combination of computational, experimental, and biophysical approaches in tubular epithelia. In particular, we examine how energy drivers affect the three-dimensional packing of these tissues. We challenge our biophysical model by reducing the cell adhesion in epithelial cells. As a result, we observed an increment on the cell apico-basal intercalation propensity that correlated with a decrease of the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle, that links tissue geometry and energetics with the average cellular connectivity.

developmental biology↗

bioRxiv2020

Systematic comparison and automated validation of detailed models of hippocampal neurons

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and concluded that each of these models provides a good match to experimental results in some domains but not in others. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community. Author summary Anatomically and biophysically detailed neuronal models are useful tools in neuroscience because they allow the prediction of the behavior and the function of the studied cell type under circumstances that are hard to investigate experimentally. However, most detailed biophysical models have been built to capture a few selected properties of the real neuron, and it is often unknown how they would behave under different circumstances, or whether they can be used to successfully answer different scientific questions. To help the modeling community develop better neural models, and make the process of model building more reproducible and transparent, we developed a test suite that enables the comparison of the behavior of models of neurons in the rat hippocampus and their evaluation against experimental data. Applying our tests to several models available in the literature, we show that each model is able to capture some of the important properties of the real neuron but fails to match experimental data in other domains. We also use the test suite in the model development workflow of the European Human Brain Project to aid the construction of better models of hippocampal neurons and networks.

neuroscience↗

bioRxiv2020

Predicting an epistasis-rich genotype-phenotype map with a coarse-grained bottom-up model of budding yeast polarity

Accurate phenotype prediction based on genotypical information has numerous societal applications, such as design of useful crops of cellular factories. However, the prevalence of epistasis, a phenomenon that prevents many biological systems to perform in accordance with the sum of its parts, necessitates modelling the complex path between genotype and phenotype. Defining intermediate levels in this path reduces the complexity of prediction, and may also elucidate the phenotype coupling to other levels by evolution. Inconveniently, the latter requires definitions that maintain biophysical justification from the bottom-up, which conflicts with tractability. By means of a cell growth model, we exemplify a resolution for this conflict by polarization of Cdc42p in budding yeast, a process requiring clustering of active Cdc42p to one zone on the membrane and known to generate ample epistasis. Concretely, our model parsimoniously encompasses constant membrane area growth, stochastic Cdc42p turnover and a simple, justifiable polarity rule we define as the mesotype. Through intuitively interpretable simulations, we describe previously documented, yet puzzling epistasis inside the polarity module. Moreover, we generate evolutionary relevant predictions e.g., on environmental perturbations, which are general enough to apply to other systems. We quantify how poor growth medium can equalize fitness differentials and enables, otherwise very distinct, evolutionary paths. For example, the fitness of the crippled {Delta}bem1 relative to WT can easily be raised from 0.2 to above 0.95. Finally, we can extend our predictions on epistasis to other modules. We determine that modelled epistasis predictions only add predictive value when functional information of the involved modules is included. This inspires a road-map towards modelling the bidirectional genotype-phenotype map for other model systems with abundant interactions, where the intermediate levels reveal targets that evolution can optimize and facilitate a biophysical justifiable incorporation of epistasis. Author summaryEfforts to understand how traits follow from genes facilitate a broad range of applications. For example, crops can be engineered faster to better resist drought, salt and heat stress, and medicines can be better tailored to individuals. Unfortunately, the path from genes to traits can generally involve a complex interplay of hundreds of genes and gene products whose individual contributions can be heavily context-dependent. In this work, we provide the proof-of-concept in a relatively simple system for a road-map towards elucidating this path. We have constructed a cell growth model for budding yeast, only involving simple rules on membrane growth, protein production and centrally, polarity, the process where yeast chooses the future division site. Despite the simplicity, the polarity rule is fully justifiable from underlying biophysics. Model simulations show good accordance with formerly puzzling traits, and also predict the ease with which the environment can change evolutionary paths. While lab conditions may prohibit the emergence of certain polarity mutations, this becomes much more feasible in the wild. The tractable model nature allows us to extrapolate the context dependence of mutational effects beyond polarity, showing that this method for understanding trait generation also helps to elucidate protein evolution.

evolutionary biology↗

bioRxiv2020

Uncovering the Organization of Neural Circuits with Generalized Phase Locking Analysis

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand a mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as a dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies, thereby providing rich yet interpretable information. In particular, we show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA used jointly with biophysical modeling can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

neuroscience↗

bioRxiv2021

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls

BackgroundCell-free methylated DNA immunoprecipitation-sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). This method allows for DNA methylation profiling of circulating tumour DNA in cancer patients blood samples. Such epigenetic profiling of circulating tumour DNA provides information about in which tissues tumour DNA originates, a key requirement of any test for early cancer detection. In addition, DNA methylation signatures provide prognostic information and can detect relapse. For robust quantitative comparisons between samples, immunoprecipitation enrichment methods like cfMeDIP-seq require normalization against common reference controls. MethodsTo provide a simple and inexpensive reference for quantitative normalization, we developed a set of synthetic spike-in DNA controls for cfMeDIP-seq. These controls account for technical variation in enrichment efficiency due to biophysical properties of DNA fragments. Specifically, we designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G+C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). We ensured that the spike-in synthetic DNA sequences do not align to the human genome. We integrated unique molecular indices (UMIs) into cfMeDIP-seq to control for differential amplification after enrichment. To assess enrichment bias according to distinct biophysical properties, we conducted cfMeDIP-seq solely on spike-in DNA fragments. To optimize the amount of spike-in DNA required, we added varying quantities of spike-in control DNA to sheared HCT116 colon cancer genomic DNA prior to cfMeDIP-seq. To assess batch effects, three separate labs conducted cfMeDIP-seq on peripheral blood plasma samples from acute myeloid leukemia (AML) patients. ResultsWe show that cfMeDIP-seq enriches for highly methylated regions, capturing [≥] 97% of methylated spike-in control fragments with [≤] 3% non-specific binding and preference for both high G+C content fragments and fragments with more CpGs. The use of 0.01 ng of spike-in control DNA in each sample provided sufficient sequencing reads to adjust for variance due to fragment length, G+C content, and CpG fraction. Using the known amount of each spiked-in fragment, we created a generalized linear model that absolutely quantifies molar amount from read counts across the genome, while adjusting for fragment length, G+C content, and CpG fraction. Employing our spike-in controls greatly mitigates batch effects, reducing batch-associated variance to [≤] 1% of the total variance within the data. DiscussionIncorporation of spike-in controls enables absolute quantification of methylated cfDNA generated from methylated DNA immunoprecipitation-sequencing (MeDIP-seq) experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases. We created an R package, spiky, to convert read counts to picomoles of DNA fragments, while adjusting for fragment properties that affect enrichment. The spiky package is available on Bioconductor (https://bioconductor.org/packages/spiky) and GitHub (https://github.com/trichelab/spiky). Contactmichael.hoffman@utoronto.ca

genomics↗

bioRxiv2021

Estimating Ectopic Beat Probability with Simplified Statistical Models that Account for Experimental Uncertainty

Ectopic beats (EBs) are cellular arrhythmias that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EBs (P(EB)). As an example, in this study, we build an LRM for P(EB) as a function of diastolic cytosolic Ca2+ concentration ([Ca2+]i), sarcoplasmic reticulum (SR) Ca2+ load, and kinetic parameters of the inward rectifier K+ current (IK1) and ryanodine receptor (RyR). This approach, which we refer to as arrhythmia sensitivity analysis, allows for evaluation of the relationship between these arrhythmic event probabilities and their associated parameters. This LRM is also used to demonstrate how uncertainties in experimentally measured values determine the uncertainty in P(EB). In a study of the role of [Ca2+]SR uncertainty, we show a special property of the uncertainty in P(EB), where with increasing [Ca2+]SR uncertainty, P(EB) uncertainty first increases and then decreases. Lastly, we demonstrate that IK1 suppression, at the level that occurs in heart failure myocytes, increases P(EB). Author summaryAn ectopic beat is an abnormal cellular electrical event which can trigger dangerous arrhythmias in the heart. Complex biophysical models of the cardiac myocyte can be used to reveal how cell properties affect the probability of ectopic beats. However, such analyses can pose a huge computational burden. We develop a simplified approach that enables a highly complex biophysical model to be reduced to a rather simple statistical model from which the functional relationship between myocyte model parameters and the probability of an ectopic beat is determined. We refer to this approach as arrhythmia sensitivity analysis. Given the efficiency of our approach, we also use it to demonstrate how uncertainties in experimentally measured myocyte model parameters determine the uncertainty in ectopic beat probability. We find that, with increasing model parameter uncertainty, the uncertainty in probability of ectopic beat first increases and then decreases. In general, our approach can efficiently analyze the relationship between cardiac myocyte parameters and the probability of ectopic beats and can be used to study how uncertainty of these cardiac myocyte parameters influences the ectopic beat probability.

bioengineering↗

bioRxiv2021

Encapsulation and recovery of murine hematopoietic stem and progenitor cells in a thiol-crosslinked maleimide-functionalized gelatin hydrogel

Biomaterial platforms are an integral part of stem cell biomanufacturing protocols. The collective biophysical, biochemical, and cellular cues of the stem cell niche microenvironment play an important role in regulating stem cell fate decisions. Three-dimensional (3D) culture of stem cells within biomaterials provides a route to present biophysical and biochemical stimuli such as cell-matrix interactions and cell-cell interactions via secreted biomolecules. Herein, we describe a maleimide-functionalized gelatin (GelMAL) hydrogel that can be crosslinked via thiol-Michael addition click reaction for the encapsulation of sensitive stem cell populations. The maleimide functional units along the gelatin backbone enables gelation via the addition of a dithiol crosslinker without requiring external stimuli (e.g., UV light, photoinitiator), reducing reactive oxide species generation. Additionally, the versatility of crosslinker selection enables easy insertion of thiol-containing bioactive or bioinert motifs. Hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs) were encapsulated in GelMAL, with mechanical properties tuned to mimic the in vivo bone marrow niche. We report insertion of a cleavable peptide crosslinker that can be degraded by the proteolytic action of SortaseA, a mammalian-inert enzyme. Notably, SortaseA exposure preserves stem cell surface markers, an essential metric of hematopoietic activity used in immunophenotyping. This novel GelMAL system enables a route to producing artificial stem cell niches with tunable biophysical properties with intrinsic cell-interaction motifs and orthogonal addition of bioactive crosslinks.

bioengineering↗

bioRxiv2021

RNA polymerase II depletion from the inactive X chromosome territory is not mediated by physical compartmentalization.

Sub-nuclear compartmentalization has been proposed to play an important role in gene regulation by segregating active and inactive parts of the genome in distinct physical and biochemical environments, where transcription and epigenetic factors are either concentrated or depleted. The inactive X chromosome offers a paradigm for studying sub-nuclear compartmentalization. When the non-coding Xist RNA coats the X chromosome, it recruits repressors and chromatin factors that trigger gene silencing, and forms a dense body of heterochromatin from which the transcription machinery appears to be excluded. Phase separation has been proposed to be involved in X-chromosome inactivation (XCI) and might explain exclusion of the transcription machinery by preventing its diffusion into the Xist-coated territory. Here, using quantitative fluorescence microscopy and single particle tracking, we show that RNA polymerase II (RNAPII) freely accesses the Xist territory during initiation of XCI, and that its diffusion is not prevented by biophysical constraints. Instead, the apparent depletion of RNAPII is due to the loss of its chromatin bound fraction. These findings demonstrate that initial exclusion of RNA Pol2 from the inactive X is a consequence of its reduced binding rate at the chromatin and gene level, rather than the biophysical compartmentalization of the inactive X heterochromatin domain. The Xist silent compartment is thus a biochemical rather than a biophysical compartment, at least during initiation of XCI.

developmental biology↗

bioRxiv2021

Mechanically Activated Extracellular Vesicle Functionalised Melt Electrowritten Materials for Bone Regeneration: A Mechano-Biomimetic Scaffold

1Mechanobiological cues arising directly via tissue/scaffold mechanics or indirectly via mechanically activated cell secretomes represent potent stimuli that mediate cell behaviour and tissue adaptation. Exploiting these cues in regeneration strategies holds great promise for tissue repair. In this study, we harness indirect biophysical cues originating from osteocytes in a combination with direct biophysical cues from Melt ElectroWritten (MEW) scaffolds to form a single engineered construct with the aim of synergistically enhancing osteogenesis. The secretome of mechanically activated osteocytes was collected within conditioned media (CM) and extracellular vesicles (EV) were subsequently isolated. Building on MEW micro-fibrous scaffolds with controlled microarchitecture and mineral nanotopography optimised for bone repair, a protocol was developed to functionalise these materials with CM or EVs. Human MSC proliferation was enhanced in both CM and EV functionalised scaffolds. EV functionalised scaffolds were further found to significantly enhance MSC osteogenesis, with enhanced alkaline phosphatase expression, collagen production, and mineralisation compared to control scaffolds. Furthermore, enhanced formation of mineralised nodules was identified in EV functionalised materials. Combining direct biophysical cues provided by the fibrous architecture/mineral nanotopography with the indirect cues provided by EVs, these constructs hold great promise to enhance the repair of damaged bone in a physiologically relevant manner.

bioengineering↗

bioRxiv2021

A gene duplication of a septin provides a developmentally-regulated filament length control mechanism

Septins are a family of conserved filament-forming proteins that function in a variety of processes including cell cycle progression, cell morphogenesis and autophagy. Despite their conservation from yeast to humans, the number of septin genes within an organism varies and higher eukaryotes express many septin isoforms due to alternative splicing. It is unclear how variability in septin complex composition influences the biophysical properties of septin polymers. Here we report that a complex duplication event within the CDC11 locus in the fungus, Ashbya gossypii, gave rise to two similar, but distinct Cdc11 proteins, Cdc11a and Cdc1b. CDC11b transcription is developmentally regulated producing different ratios of Cdc11a and b complexes during Ashbyas lifecycle. Moreover, deletion of either CDC11a or CDC11b results in distinct cell polarity defects. Remarkably, despite substantial identity in amino acid sequence, Cdc11a and Cdc11b complexes have distinct biophysical properties with clear filament length and membrane-binding ability differences. Thus, septin subunit composition has functional consequences for filament properties and such functional plasticity can be exploited for distinct biophysical properties and cell functions.

cell biology↗

bioRxiv2021

De novo evolution of macroscopic multicellularity

While early multicellular lineages necessarily started out as relatively simple groups of cells, little is known about how they became Darwinian entities capable of open-ended multicellular adaptation1,2. To explore this, we initiated the Multicellularity Long Term Evolution Experiment (MuLTEE), selecting for larger group size in the snowflake yeast (Saccharomyces cerevisiae) model system. Given the historical importance of oxygen limitation3, our ongoing experiment consists of three metabolic treatments4: anaerobic, obligately aerobic, and mixotrophic yeast. After 600 rounds of selection, snowflake yeast in the anaerobic treatment evolved to be macroscopic, becoming ~2{middle dot}104 times larger (~mm scale) and ~104-fold more biophysically tough, while retaining a clonal multicellular life cycle. They accomplished this through sustained biophysical adaptation, evolving increasingly elongate cells that initially reduced the strain of cellular packing, then facilitated branch entanglements that enabled groups of cells to stay together even after many cellular bonds fracture. In contrast, snowflake yeast competing for low oxygen remained microscopic, evolving to be just ~6-fold larger, underscoring the critical role of oxygen levels in the evolution of multicellular size. Taken together, this work provides unique insight into an ongoing evolutionary transition in individuality, showing how simple groups of cells overcome fundamental biophysical limitations via gradual, yet sustained, multicellular adaptation.

evolutionary biology↗

bioRxiv2021

Identifying microstructural changes in diffusion MRI; How to break parameter degeneracy

Biophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH). We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change. BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions. Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.

neuroscience↗

bioRxiv2021

Caveats to deep learning approaches to RNA secondary structure prediction

Machine learning (ML) and in particular deep learning techniques have gained popularity for predicting structures from biopolymer sequences. An interesting case is the prediction of RNA secondary structures, where well established biophysics based methods exist. The accuracy of these classical methods is limited due to lack of experimental parameters and certain simplifying assumptions and has seen little improvement over the last decade. This makes RNA folding an attractive target for machine learning and consequently several deep learning models have been proposed in recent years. However, for ML approaches to be competitive for de-novo structure prediction, the models must not just demonstrate good phenomenological fits, but be able to learn a (complex) biophysical model. In this contribution we discuss limitations of current approaches, in particular due to biases in the training data. Furthermore, we propose to study capabilities and limitations of ML models by first applying them on synthetic data (obtained from a simplified biophysical model) that can be generated in arbitrary amounts and where all biases can be controlled. We assume that a deep learning model that performs well on these synthetic, would also perform well on real data, and vice versa. We apply this idea by testing several ML models of varying complexity. Finally, we show that the best models are capable of capturing many, but not all, properties of RNA secondary structures. Most severely, the number of predicted base pairs scales quadratically with sequence length, even though a secondary structure can only accommodate a linear number of pairs.

bioinformatics↗

bioRxiv2022

Properties and computational consequences of fast dendritic spikes during natural behavior

Dendritic membrane potential was recently measured for the first time in drug-free, naturally behaving rats over several days. These showed that neuronal dendrites generate a lot of sodium spikes, up to ten times as many as the somatic spikes. These key experimental findings are reviewed here, along with a discussion of computational models, and computational consequences of such intense spike traffic in dendrites. We overview the experimental techniques that enabled these measurements as well as a variety of models, ranging from conceptual models to detailed biophysical models. The biophysical models suggest that the intense dendritic spiking activity can arise from the biophysical properties of the dendritic voltage-dependent and synaptic ion channels, and delineate some computational consequences of fast dendritic spike activity. One remarkable aspect is that in the model, with fast dendritic spikes, the efficacy of synaptic strength in terms of driving the somatic activity is much less dependent on the position of the synapse in dendrites. This property suggests that fast dendritic spikes is a way to confer to neurons the possibility to grow complex dendritic trees with little computational loss for the distal most synapses, and thus form very complex networks with high density of connections, such as typically in the human brain. Another important consequence is that dendritically localized spikes can allow simultaneous but different computations on different dendritic branches, thereby greatly increasing the computational capacity and complexity of neuronal networks.

neuroscience↗

bioRxiv2022

Protein Refolding Guided by High-Throughput Differential Scanning Fluorimetry: a case study of an HtrA-Family Bacterial Protease

The high temperature requirement A (HtrA) serine protease family presents an attractive target class for antibacterial therapeutics development. These proteins possess dual protease and chaperone functions and contain numerous binding sites and regulatory loops, displaying diverse oligomerization patterns dependent on substrate type and occupancy. HtrA proteins that are natively purified coelute with contaminating peptides and activating species, shifting oligomerization and protein structure to differently activated populations. Here, a redesigned HtrA production results in cleaner preparations with high yields by overexpressing and purifying target protein from inclusion bodies under denaturing conditions, followed by a high-throughput screen for optimal refolding buffer composition using function-agnostic biophysical techniques that do not rely on target-specific measurements. We use the Borrelia burgdorferi HtrA to demonstrate the effectiveness of our function-agnostic approach, while characterization with both new and established biophysical methods shows the retention of proteolytic and chaperone activity of the refolded protein. This systematic workflow and toolset will translate to the production of HtrA-family proteins in higher quantities of pure and monodisperse composition than the current literature standard, with applicability to a broad array of protein purification strategies. StatementThe production of a therapeutically-relevant protein family sensitive to coeluting contaminants is greatly improved by optimized expression and refolding workflow. A miniaturized, high-throughput system supported by a function-agnostic biophysics assay and modified data analysis scripts results in a refolded protein that is highly pure, monodisperse, and retains proteolytic and chaperone activity. This approach has broad applicability towards hard-to-express proteins and proteins sensitive to coeluting species. Additionally, novel methods are presented to characterize protein chaperone activity.

biochemistry↗