Biology subjects

Goriely, A.

Publications and source records attributed to Goriely, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Oscillatory flow and steady streaming of cerebrospinal fluid in cranial subarachnoid space

Cerebrospinal fluid (CSF) is a Newtonian fluid that bathes the brain and spinal cord and oscillates in response to the physiological periodic changes in brain volume, of which the cardiac cycle is a major driver. Understanding this motion is essential for clarifying its contribution to solute transport, waste clearance, and drug delivery. In this work, we study oscillatory and steady streaming flow in the cranial subarachnoid space using a lubrication-based theoretical framework. The model represents the cranial CSF compartment as a thin fluid layer bounded internally by the brain surface and externally by the dura, driven by time-dependent brain surface displacements. We first derive simplified governing equations for flow over an arbitrary smooth sphere-like brain surface and obtain analytical solutions for an idealised spherical geometry with uniform displacements. We then incorporate realistic displacement fields reconstructed from MRI measurements in healthy subjects and solve the reduced equations numerically. The results show that oscillatory forcing produces a steady streaming component that may enhance solute transport compared with diffusion alone. This work provides a mechanistic description of the flow generated by physiological brain motion and highlights the potential presence of steady streaming in cranial subarachnoid fluid dynamics.

biophysics↗

bioRxiv2026

Dynamical Aβ-Tau-Neurodegeneration Model Predicts Alzheimer's Disease Mechanisms and Biomarker Progression

Alzheimers disease is characterised by the pathological interaction of two proteins, amyloid-beta (A{beta}) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of A{beta}, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of A{beta}, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of A{beta} and tau, network-based tau propagation, A{beta}-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional A{beta}, tau, and neurodegeneration data. Using the dATN model, we show that A{beta}-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of A{beta} and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic-pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting A{beta}, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence SummaryColocalisation of A{beta} and tau predicts regional tau progression and optimal A{beta}-targeted intervention windows, while tau predicts neurodegeneration.

neuroscience↗

bioRxiv2025

A role for the male germline in the expansion of the mammalian brain

The brain and testis share many molecular similarities, yet the evolutionary implications of this overlap remain unclear. We have previously hypothesised that, throughout evolution, some genetic variants contributing to brain size expansion first arose in spermatogonia where they conferred a selective advantage to the male germline stem cells via selfish spermatogonial selection, a process analogous to oncogenesis. Once transmitted to the next generation, these selfish variants became constitutive, disproportionately accumulating in signalling pathways active in both spermatogenesis and neurogenesis and which regulate stem cell proliferation. However, testing this hypothesis is stymied by the relative scarcity of spermatogonia and the inherent stochasticity of single-cell transcriptomic profiling. Accordingly, the molecular signatures of spermatogonia are incompletely understood, and their similarity with neural programs difficult to assess. To address this, we combine single-cell testis and brain transcriptomic datasets with multi-tissue proteomic data to show that genes associated with brain-expansion phenotypes, and neurodevelopment generally, are not only widely expressed in the male germline but particularly enriched in spermatogonia, an observation consistent across mammalian evolutionary history. We contextualise these results with an extensive literature survey and conclude that enquiry into the testis-brain connection may yield novel insight into the evolutionary processes that shaped the human condition.

genomics↗

bioRxiv2025

Neurodegeneration emerges at a cellular tipping point between protein accumulation and removal.

Protein aggregates are a pathological hallmark across neurodegenerative diseases. Yet, the disconnect between molecular-level aggregation and the emergence of disease severely limits mechanistic understanding of neurodegeneration. Here, we bridge this disconnect by showing that a cellular tipping point emerges as a universal feature across diseases from the competition between aggregate accumulation and removal. We map the resulting cellular phase transition with our high-throughput live-cell assay, measuring the tipping point that separates healthy cells from those with large aggregate loads. Using super-resolution imaging of brain tissue from Alzheimers and Parkinsons disease, we quantify how the balance of accumulation and removal is shifted in disease. We validate our framework by predicting how designed aggregation inhibitors shift the tipping point to restore cellular homeostasis. Our results provide a mechanistic framework connecting molecular-level aggregation to disease, paving the way for a quantitative, unified understanding of neurodegeneration and enabling predictions of therapeutic efficacy.

neuroscience↗

bioRxiv2025

Heterogeneity in human brain clearance adds resilience against tauopathy -- a computational model informed by glymphatic MRI

Neurotoxic protein fragments such as amyloid-beta and tau accumulate in characteristic staging patterns in Alzheimers disease (AD). The brain clears such metabolic substances via multiple different systems, including via the glymphatic (extracellular/extravascular) pathway. Here, we ask how the distinct features that characterize human glymphatic function would affect the prion-like cascade of protein invasion associated with AD. To address this question, we extract and analyze individual clearance rates from human glymphatic MRI (gMRI) data sets. These clearance rates define subject-specific maps of glymphatic clearance that vary both across cortical lobes and Braak staging regions. We apply these clearance maps as initial states in a computational network model linking misfolded proteins, tissue damage, and local clearance to simulate a series of individual proteinopathy trajectories. Our results show that the spatial heterogeneity in initial clearance induces characteristic propagation patterns, delaying and redirecting the disease progression. Moreover, reducing this spatial heterogeneity accelerates disease progression and induces staging patterns typically associated with AD. A comparison between well-rested subjects and subjects who underwent a single night of sleep deprivation did not reveal differences in initial clearance maps nor in simulated disease progression. These findings suggest that spatial heterogeneity in brain clearance may be a key factor for neurodegenerative resilience.

neuroscience↗

bioRxiv2025

Neuronal activity and amyloid-beta cause tau seeding in the entorhinal cortex in Alzheimer's disease

The entorhinal cortex is the earliest site of tau pathology in both Alzheimers disease and primary age-related tauopathy, yet the mechanisms underlying this selective vulnerability remain poorly understood. Here, we use a computational model integrating neuronal activity and amyloid-{beta} deposition with interneuronal tau transport to predict regional susceptibility to tau seeding. Using fluorodeoxyglucose PET as a measure of neuronal activity, we show that brain-wide activity patterns drive tau accumulation in the medial temporal lobe, independent of amyloid status. Incorporating amyloid PET, we further show that amyloid-{beta} selectively amplifies tau seeding in the entorhinal cortex, aligning with its early involvement in Alzheimers disease. These predictions are supported by cross-subject correlation analysis, which reveals a significant association between model-derived seeding concentrations and empirical tau deposition. Our findings suggest that neuronal activity patterns shape the early landscape of tau pathology, while amyloid-{beta} deposition creates a unique vulnerability in the entorhinal cortex, potentially triggering the pathological cascade that defines Alzheimers disease.

neuroscience↗

bioRxiv2024

Ultra-fast physics-based modeling of the elephant trunk

With more than 90,000 muscle fascicles, the elephant trunk is a complex biological structure and the largest known muscular hydrostat. It achieves an unprecedented control through intricately orchestrated contractions of a wide variety of muscle architectures. Fascinated by the elephant trunks unique performance, scientists of all disciplines are studying its anatomy, function, and mechanics, and use it as an inspiration for biomimetic soft robots. Yet, to date, there is no precise mapping between microstructural muscular activity and macrostructural trunk motion, and our understanding of the elephant trunk remains incomplete. Specifically, no model of the elephant trunk employs formal physics-based arguments that account for its complex muscular architecture, while preserving low computational cost, to enable fast screening of its configuration space. Here we create a reduced-order model of the elephant trunk that can-within a fraction of a second- predict the trunks motion as a result of its muscular activity. To ensure reliable results in the finite deformation regime, we integrate first principles of continuum mechanics and the theory of morphoelasticity for fibrillar activation. We employ dimensional reduction to represent the trunk as an active slender structure, which results in closed-form expressions for its curvatures and extension as functions of muscle activation and anatomy. We create a high-resolution digital representation of the trunk from magnetic resonance images to quantify the effects of different muscle groups. We propose a general solution method for the inverse motion problem and apply it to extract the muscular activations of three representative trunk motions: picking a fruit; lifting a log; and lifting a log asymmetrically. For each task, we identify key features in the muscle activation profiles. Our results suggest that, upon reaching maximum contraction in select muscle groups, the elephant trunk autonomously reorganizes muscle activation to perform specific tasks. Our study provides a complete quantitative characterization of the fundamental science behind elephant trunk biomechanics, with potential applications in the material science of flexible structures, the design of soft robots, and the creation of flexible prosthesis and assist devices.

bioengineering↗

bioRxiv2024

Two for tau: Automated model discovery reveals two-stage tau aggregation dynamics in Alzheimer's disease

Alzheimers disease is a neurodegenerative disorder characterized by the presence of amyloid-{beta} plaques and the accumulation of misfolded tau proteins and neurofibrillary tangles in the brain. A thorough understanding of the local accumulation of tau is critical to develop effective therapeutic strategies. Tau pathology has traditionally been described using reaction-diffusion models, which succeed in capturing the global spread, but fail to accurately describe the local aggregation dynamics. Current mathematical models enforce a single-peak behavior in tau aggregation, which does not align well with clinical observations. Here we identify a more accurate description of tau aggregation that reflects the complex patterns observed in patients. We propose an innovative approach that uses constitutive neural networks to autonomously discover bell-shaped aggregation functions with multiple peaks from clinical positron emission tomography (PET) data of misfolded tau protein. Our method reveals previously overlooked two-stage aggregation dynamics by uncovering a twoterm ordinary differential equation that links the local accumulation rate to the tau concentration. When trained on data from amyloid-{beta} positive and negative subjects, the neural network clearly distinguishes between both groups and uncovers a more subtle relationship between amyloid-{beta} and tau than previously postulated. In line with the amyloid-tau dual pathway hypothesis, our results show that the presence of toxic amyloid-{beta} influences the accumulation of tau, particularly in the earlier disease stages. We expect that our approach to autonomously discover the accumulation dynamics of pathological proteins will improve simulations of tau dynamics in Alzheimers disease and provide new insights into disease progression.

biophysics↗

bioRxiv2024

Multilevel irreversibility reveals higher-order organisation of non-equilibrium interactions in human brain dynamics

Information processing in the human brain can be modelled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of non-equilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analysing neural recordings using network analysis of time-series. Our approach constructs directed multi-layer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of non-equilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions whilst for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most non-equilibrium interactions are between cognitive-sensorial pairs alongside medial regions. Finally, for quintuplets, our analysis finds higher irreversibility when the prefrontal cortex is included in the interaction. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organisation of neural dynamics and presents a new perspective on the analysis of brain network dynamics.

biophysics↗

bioRxiv2024

Human spermatogonial stem cells retain states with a foetal-like signature

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling it has been proposed that undifferentiated human spermatogonia can be sub-classified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc)RNA-seq libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate state 0B with a suppressive transcriptomic program that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells - which are mitotically arrested - can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mouse (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive life while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.

cell biology↗

bioRxiv2023

Pseudo-craniotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients

Brain tumors can induce pathological changes in neuronal dynamics both on a local and global level. Here, we use a whole-brain modeling approach to investigate these pathological alterations in neuronal activity. By fitting a Hopf whole-brain model to empirical functional connectivity, we demonstrate that phase correlations are largely determined by the ratio of interregional coupling strength and intraregional excitability. Furthermore, we observe considerable differences in interregional-versus-intraregional dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, we show that local tumor pathology induces shifts in the global brain dynamics by promoting the contribution of interregional interactions. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.

neuroscience↗

bioRxiv2023

Neuronal activity induces symmetry breaking in neurodegenerative disease spreading

Dynamical systems on networks typically involve several dynamical processes evolving at different timescales. For instance, in Alzheimers disease, the spread of toxic protein throughout the brain not only disrupts neuronal activity but is also influenced by neuronal activity itself, establishing a feed-back loop between the fast neuronal activity and the slow protein spreading. Motivated by the case of Alzheimers disease, we study the multiple-timescale dynamics of a heterodimer spreading process on an adaptive network of Kuramoto oscillators. Using a minimal two-node model, we establish that heterogeneous oscillatory activity facilitates toxic outbreaks and induces symmetry breaking in the spreading patterns. We then extend the model formulation to larger networks and perform numerical simulations of the slow-fast dynamics on common network motifs and on the brain connectome. The simulations corroborate the findings from the minimal model, underscoring the significance of multiple-timescale dynamics in the modeling of neurodegenerative diseases.

neuroscience↗

bioRxiv2023

Personalised Regional Modelling Predicts Tau Progression in the Human Brain

Aggregation of the hyperphosphorylated tau protein is a central driver of Alzheimers disease, and its accumulation exhibits a rich spatio-temporal pattern that unfolds during the course of the disease, sequentially progressing through the brain across axonal connections. It is unclear how this spatio-temporal process is orchestrated - namely, to what extent the spread of pathologic tau is governed by transport between brain regions, local production or both. To address this, we develop a mechanistic model from tau PET data to describe tau dynamics along the Alzheimers disease timeline. Our analysis reveals longitudinal changes in production and transport dynamics on two independent cohorts, with subjects in early stage of the disease exhibiting transport-dominated spread, consistent with an initial spread of pathologic tau seeds, and subjects in late stage disease (Braak stage 3/4 onwards) characterised primarily by local production of tau. Furthermore, we demonstrate that the model can accurately predict subject-specific longitudinal tau accumulation at a regional level, potentially providing a new clinical tool to monitor and classify patient disease progression. TeaserA mechanistic model reveals tau protein dynamics in Alzheimers, showing stage-specific shifts in transport and local production.

neuroscience↗

bioRxiv2023

Mechanics reveals the role of peristome geometry in prey capture in carnivorous pitcher plants (Nepenthes)

Carnivorous pitcher plants (Nepenthes) are a striking example of a natural pitfall trap. The traps slippery rim, or peristome, plays a critical role in insect capture via an aquaplaning mechanism that is well documented. Whilst the peristome has received significant research attention, the conspicuous variation in peristome geometry across the genus remains unexplored. We examined the mechanics of prey capture using Nepenthes pitcher plants with divergent peristome geometries. Inspired by living materials, we developed a mathematical model that links the peristomes three-dimensional geometries to the physics of prey capture under the laws of Newtonian mechanics. Linking form and function enables us to test hypotheses related to the function of features such as shape and ornamentation, orientation in a gravitational field, and the presence of teeth, while analysis of the energetic costs and gains of a given geometry provides a means of inferring potential evolutionary pathways. In a separate modeling approach, we show how prey size may correlate with peristome dimensions for optimal capture. Our modeling framework provides a physical platform to understand how divergence in peristome morphology may have evolved in the genus Nepenthes in response to shifts in prey diversity, availability, and size. Significance StatementPitcher plants (Nepenthes) produce an astonishing array of leaf-derived traps into which preys (typically insects) slide from a rim (the peristome). How prey capture varies across this varied genus is a mystery. We hypothesized that insects behave differently depending on peristome size and geometry. We examined the physics of prey capture under the laws of Newtonian mechanics to show that prey size and behavior correlate with peristome shape. We conclude that a diversity of peristomes in Nepenthes evolved in response to variation in prey capture.

plant biology↗

bioRxiv2023

Two-component Turing reaction-diffusion models can explain how mother centrioles break PLK4 symmetry to generate a single daughter

Centrioles duplicate when a mother centriole gives birth to a daughter that grows from its side. Polo-like-kinase 4 (PLK4), the master regulator of centriole duplication, is recruited symmetrically around the mother centriole, but it then concentrates at a single focus that defines the daughter centriole assembly site. How PLK4 breaks symmetry is unclear. Here, we propose that phosphorylated and unphosphorylated species of PLK4 form the two components of a classical Turing reaction-diffusion system. These two components bind-to/unbind-from the surface of the mother centriole at different rates, allowing a slow-diffusing activator species of PLK4 to accumulate at a single site on the mother, while a fast-diffusing inhibitor species of PLK4 suppresses activator accumulation around the rest of the centriole. This "short-range activation/long-range inhibition", inherent to Turing-systems, can drive PLK4 symmetry breaking on a continuous centriole surface, with PLK4 overexpression producing multiple PLK4 foci and PLK4 kinase inhibition leading to uniform PLK4 accumulation--as observed experimentally.

cell biology↗

bioRxiv2022

A mechanistic model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease

Alzheimers disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-{beta} and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-{beta} and tau affect neurons at the cellular scale. On a larger scale, Alzheimers patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency-slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-{beta} and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-{beta} and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation, and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency-slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-{beta} and tau protein.

neuroscience↗

bioRxiv2022

Rheology of growing axons

The growth of axons is a key process in neural system development, which relies upon a subtle balance between external mechanical forces and remodeling of cellular constituents. A key problem in the biophysics of axons is therefore to understand the overall response of the axon under stretch, which is often modeled phenomenologically using morphoelastic or viscoelastic models. Here, we develop a microscopic mixture model of growth and remodeling based on protein turnover and damage to obtain the macroscopic rheology of axonal shafts. First, we provide an estimate for the instantaneous elastic response of axons. Second, we demonstrate that under moderate traction velocities, axons behave like a viscoelastic Maxwell material. Third, for larger velocities, we show that failure takes place due to extensive damage.

developmental biology↗

bioRxiv2022

The role of clearance in neurodegenerative diseases

Alzheimers disease, the most common form of dementia, is a systemic neurological disorder associated with the formation of toxic, pathological aggregates of proteins within the brain that lead to severe cognitive decline, and eventually, death. In normal physiological conditions, the brain rids itself of toxic proteins using various clearance mechanisms. The efficacy of brain clearance can be adversely affected by the presence of toxic proteins and is also known to decline with age. Motivated by recent findings, such as the connection between brain cerebrospinal fluid clearance and sleep, we propose a mathematical model coupling the progression of toxic proteins over the brains structural network and protein clearance. The model is used to study the interplay between clearance in the brain, toxic seeding, brain network connectivity, aging, and progression in neurodegenerative diseases such as Alzheimers disease. Our findings provide a theoretical framework for the growing body of medical research showing that clearance plays an important role in the etiology, progression and treatment of Alzheimers disease.

neuroscience↗