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bioRxiv2026

Unifying Non-Equilibrium Information Thermodynamics and Genome Engine Dynamics: Maxwell's Demon Control of Cancer Cell Fates

Molecular biology has revealed immense spatiotemporal complexity in cancer regulatory networks. A fundamental physical question remains: Does a physical principle exist that governs how this complexity self-organizes into genome-wide fate commitment and determines whether and when that commitment becomes irreversible? We address this question through an integrative biophysical analysis of time-series transcriptomes from two cancer systems under four conditions: MCF-7 human breast cancer cells stimulated with heregulin (HRG) or epidermal growth factor (EGF), and HL-60 human leukemia cells stimulated with all-trans retinoic acid (atRA) or dimethyl sulfoxide (DMSO). Our analysis establishes self-organized criticality (SOC) control as a biophysical principle that unifies information thermodynamics with genome-engine dynamics in a single open, non-equilibrium physical system. This principle elucidates how information processing is converted into mechanical work during cancer-fate commitment. Under SOC control, the genome operates as an open, non-equilibrium engine that maintains a critical dynamic balance between homeostatic stability and fate-guiding critical transitions. The critical point (CP) gene ensemble drives this genome-wide control. Within the environment-coupled whole expression system, the CP performs two coupled roles converging in a Maxwells demon (MD) actuation cycle. This cycle is implemented through rewritable chromatin memory exhibiting bistable switching. Thermodynamically, the CP acts as an MD operator regulating entropy and information-work conversion through phase synchronization. Dynamically, it acts as an SOC controller synchronizing with the genome attractor, driving genome-wide reorganization, and generating the dominant mechanical work. This unified mechanism distinguishes the rapid HRG, delayed DMSO, and two-step atRA commitments from the non-committing EGF response. It satisfies three open-system thermodynamic criteria for an irreversible arrow of time and defines time-gated rules with predictive intervention windows for dynamic cancer-fate control.

genomics↗

bioRxiv2026

High-throughput Genome Wide CRISPR Knock Out mechanical sort identifies genes driving metastatic cancer cell softening

Cell mechanics can serve as an important biomarker for cell state and phenotype, such as metastatic ability. While some molecular mechanisms underlying cell mechanical properties have been investigated through targeted analyses, a genome-wide study of human genes and gene networks that modulate cell biophysical properties has not been attempted. In this work, we combined a microfluidic stiffness-based sorting device with a genome-scale CRISPR knockout (GeCKO) screen in order to investigate the effect of individual gene knockouts on cell stiffening and cell softening across the entire protein-coding genome. We processed approximately 150 million Cas9-expressing ovarian cancer cells that had been transduced with a library of 76,000 single guide RNAs (sgRNAs) against the 19,000 protein-coding genes in the genome. The cells were sorted into 5 mechanical subsets. We identified 7 gene knockouts that were significantly depleted in the softer subsets and over 700 gene knockouts that were significantly enriched in the stiffer subsets. Of these significant genes of interest, we selected 3 genes that were highly expressed in our ovarian cancer cell line with greater than 100-fold enrichment in the stiff outlet and resulted in significant changes in ovarian cancer patient survival. These genes, PIK3R4, CCDC88A, and GSK3B, when knocked out result in a significant and predicted increase in cell stiffness. This study is the first to explore the relation between human gene expression and cell mechanics at the genome-scale to generate datasets at the intersection between cell genotype, mechanotype, and phenotype for metastatic cancer cells. The method could also be applied to study the effect of genes on other biophysical cell processes as well as for identifying pathways for the control of cellular mechanics across many cell types.

bioengineering↗

bioRxiv2026

The role of charge, hydrophobicity, and cooperativity in target search of SOX2 and ESRRB

Transcription factors (TFs) locate and bind specific DNA sequences within a crowded nuclear environment, yet how their biophysical properties and interactions influence their search remains unclear. Here, we modulate hydrophobicity and charge of the pluripotency TFs SOX2 and ESRRB, and combine single-molecule imaging with genomic analyses to dissect their target search dynamics. We show that increased hydrophobicity impairs specific binding while promoting confined intranuclear interactions, whereas additional negative charges decrease sampling of weak binding sites. While SOX2 retains effective target site recognition despite decreased search efficiency, impairment of ESRRB search results in a global loss of genome occupancy and redirection to SOX2-bound regions, indicating a strong reliance of ESRRB on TF-TF interactions for locating its binding sites. Single molecule imaging analysis of ESRRB search upon SOX2 depletion demonstrated that SOX2 reduces ESRRB search time by restricting its nuclear diffusion and facilitating stable DNA binding. Together, our findings highlight how TF biophysical features and cooperativity jointly shape the efficiency and specificity of target search in the nucleus.

molecular biology↗

bioRxiv2026

Small molecule ensembles reshape amyloid aggregation landscapes

Proteins in living systems are highly dynamic, continuously populating heterogeneous ensembles of functionally distinct conformational and aggregation states whose relative populations and interconversion rates are governed by an underlying free-energy landscape. Perturbations such as ligand binding, chemical modification, or changes in the environment remodel these assemblies, thereby reshaping protein behavior and function. Understanding how such protein networks are dynamically remodeled is a fundamental challenge in biophysics. Here, we utilize the A{beta}42 aggregation landscape as a model to demonstrate that remodeling can occur without detectable changes in the apparent activation free-energy barrier, instead arising from system-level redistribution of species along a conserved kinetic pathway. Using EPPS (4-(2-Hydroxyethyl)-1-piperazinepropanesulfonic acid) as a model modulator, temperature-dependent kinetic analysis reveals an invariant apparent activation free energy, suggesting that the underlying energetic framework remains unchanged even as the system is reshaped. We find that EPPS modulates A{beta}42 disaggregation in a concentration-dependent manner. At intermediate concentrations, enhanced redistribution toward soluble species is observed, whereas at higher concentrations, modulator self-association into supramolecular clusters is associated with reduced net disaggregation and partial recovery of aggregation signatures. Thermal disruption of these assemblies restores activity, consistent with a role of modulator self-association in governing the observed behavior. Furthermore, our experiments indicate that this disaggregation process is reduced in crowded environments compared to dilute conditions. Together, these results support a biophysical framework in which amyloid remodeling is driven by species redistribution along a conserved kinetic landscape, where the extent of disaggregation is determined by modulator concentration-dependent partitioning of protein species, modulator self-association, and environmental constraints. SignificanceAn emerging view of protein function recognizes that proteins exist as dynamic networks of interconverting conformational and aggregation states. Binding interactions and environmental perturbations remodel these networks, thereby altering protein behavior. Amyloid aggregates associated with Alzheimers disease exemplify such dynamic systems, yet how small molecules remodel them remains poorly understood. Using A{beta}42 as a model, we show that small molecules remodel amyloid networks by redistributing protein populations in a concentration-dependent manner. This redistribution is modulated by self-association of the small molecule, molecular crowding, and temperature. These findings establish population redistribution within dynamic amyloid networks as a mechanism for remodeling protein assemblies, offering new insights into controlling pathological aggregation.

molecular biology↗

bioRxiv2026

Extreme Heat as the New Normal: A Methodological Roadmap for Behavior, Physiology, and Species Distributions

A defining feature of climate change is the increasing frequency, intensity, and severity of extreme weather events. Among them, extreme heat is recognized as a critical driver of ecological and evolutionary change. Intense heat episodes can exceed physiological limits, alter animal movement, restructure geographic ranges, and increase extinction risk more than gradual changes to mean temperatures. Yet links between extreme heat events and organismal biology remain limited, in part because definitions and metrics are not standardized, and user-friendly workflows and guides are lacking for many biologists. We present a methodological roadmap, with reproducible code, for integrating extreme heat into studies of behavior, physiology, biophysical ecology, species distribution models (SDMs), and population dynamics. First, we provide standardized computational approaches to define and quantify extreme heat. Second, we fit species distribution models for California quail (Callipepla californica) that include an extreme heat metric and showcase improved predictions of habitat suitability, particularly at range edges. Third, we compute biophysical simulations to quantify exposure to thermal stress in Sleepy lizards (Tiliqua rugosa) across distinct macro- and microclimates. Finally, accounting for temporal autocorrelation in temperature profiles in population simulation models, we show that clustered heat extremes--missed by averages--can increase the risk of population collapse. As extreme heat events become more common, incorporating their dynamics is essential for understanding ecological and evolutionary change, designing experiments across species geographic ranges, and supporting conservation in a rapidly warming world. Together, these case studies illustrate a reproducible, organism-informed roadmap to integrate extreme heat into predictions of ecological impacts and inference across levels of biological organization under ongoing climate change.

ecology↗

bioRxiv2026

Human breast milk extracellular vesicles from mothers with asthma differentially modulate the release of inflammatory cytokines by primary human airway smooth muscle cells in a recipient-cell specific manner

Breastfeeding provides health benefits in childhood, reducing the frequency of gastrointestinal and respiratory infections. Breastmilk (BM) is a rich source of bioactive molecules including extracellular vesicles (EVs), which exert immunomodulatory signalling in recipient cells, with cargo that is affected by maternal characteristics. Here we investigated the biophysical characteristics of BM-EVs from mothers with (asthmatic BM-EVs) or without asthma (control BM-EVs) and their effect on the release of cytokines from primary human hTERT-immortalized airway smooth muscle cells (hASMs) from asthmatic or non-asthmatic (control) donors. BM-EVs were isolated using size exclusion chromatography (N=5/group), characterized biophysically and by EV-specific protein markers. In addition, BM-EV were co-cultured (48h) with primary hASM cells from both non-asthmatic (control) and asthmatic donors to determine the effect on cytokine release. All participants were Caucasian and the BM was collected 12-15 weeks postpartum. BM-EVs showed the presence of intact and small-EVs ([~]100 nm). Asthmatic BM-EVs appeared to have a smaller average EV size (135.6 nm) vs. controls (148.3 nm, p=0.0613), but [~]5-fold higher concentration of both total (p=0.0014) and small EVs (p=0.0016). The expression of EV subtype protein expression was reduced in asthmatic BM-EVs vs. control BM-EVs: CD63 by 86% (p=0.0224), flotillin-1 by 40% (p=0.0196), CD9 by 24% (p=0.0646) and HSP70 by 69% (p=0.0873). Asthmatic BM-EVs co-cultured with hASMs from control donors decreased pro-inflammatory cytokine release: MCP-1 by 55% (p=0.0286), IL-6 by 45% (p=0.0801) and IL-2 by 32% (p=0.0970) vs. control-BM-EVs. Conversely, asthmatic BM-EVs co-cultured with hASMs from asthmatic donors increased secretion of anti-inflammatory cytokine IL-10 by 32% (p=0.0660), and IL-1Ra by 75% (p=0.0875), and pro-inflammatory IL-2 by 57% (p=0.0688) vs. control-BM-EVs. Internalization of control and asthmatic BM-EVs was confirmed by labelled EV uptake experiments. No detrimental effects on cell viability with BM-EV treatment were observed. In summary, asthmatic BM-EVs are smaller and enriched in BM, and exert differential effects on cytokine release in a BM-donor and recipient-cell specific manner. Given that BM can enter infant airways, the immunomodulatory effects of BM-EVs on hASMs warrants further investigation to delineate the under underlying mechanisms.

cell biology↗

bioRxiv2026

Structural basis of MfpD, a versatile pathogeny protein from the mfp conservon of Mycobacterium tuberculosis

The mfp conservon of Mycobacterium tuberculosis has been associated with fluoroquinolone resistance and encodes five conserved proteins, including the small GTPase MfpB and its regulatory partner MfpD. In this study, we combined phylogenetic, structural, and biophysical approaches to define the molecular basis of MfpD function. MfpD adopts a Roadblock/LC7-like /{beta} fold and forms a stable dimer in solution, with hydrophobic 2-helix interactions stabilizing the interface. Additional biophysical analyses and AlphaFold3 modeling suggest that MfpD may promote GTP hydrolysis by MfpB through a noncanonical Switch I-dependent mechanism. These findings establish the first structural framework for MfpD-MfpB interactions, building on previously identified in vitro catalytic properties and proposing new insights into MfpDs non-catalytic pathogenesis activity of MfpD in macrophages. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/709265v2_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@77b2d2org.highwire.dtl.DTLVardef@7d1908org.highwire.dtl.DTLVardef@f66eeforg.highwire.dtl.DTLVardef@ed159c_HPS_FORMAT_FIGEXP M_FIG GRAPHICAL ABSTRACT C_FIG

biochemistry↗

bioRxiv2026

Hydration-dehydration cycles drive compartment dynamics in minimal protocells

Compartmentalization is a defining feature of cellular systems, yet how early compartments could undergo repeated cycles of growth, division, and content organization without complex chemistry remains unresolved. Here we study a minimal membrane-based system subjected to periodic hydration- dehydration cycles, mimicking fluctuating physical environments on the early Earth. We show that cyclic environmental conditions alone drive a sequence of reproducible compartment dynamics, including macromolecule encapsulation, membrane growth, division, and the generation of a highly crowded interior. These processes emerge from biophysical transformations of a single-component membrane and do not require any chemical reactions or metabolic activity. Importantly, compartments retain their structural integrity across multiple cycles, enabling repeated encapsulation without loss of individuality. Our results demonstrate that fluctuating physical conditions can be transduced by membrane biophysics into sustained, cell-like cycles, challenging the view that primordial cellular dynamics necessarily required chemically driven growth and division.

synthetic biology↗

bioRxiv2026

Structural and evolutionary insights into DAF-12 interactions with transcriptional coactivators in parasitic nematodes

Parasitic nematodes infect billions of humans and livestock worldwide, causing major health and economic burdens, while the spread of anthelmintic resistance threatens current control strategies. A critical step in parasite infection is the resumption of development of infective third-stage larvae (iL3) upon host entry, a process controlled by the nuclear receptor DAF-12. Activation of DAF-12 by dafachronic acids promotes developmental progression and reproductive maturation, making this receptor an attractive therapeutic target. However, the molecular mechanisms governing DAF-12 activation, particularly transcriptional coactivator recruitment, remain poorly understood. Here, we combined biophysical, cellular, structural, and bioinformatic approaches to investigate coactivator recognition by DAF-12 from the parasitic nematodes Brugia malayi and Haemonchus contortus. Crystal structures of ligand-bound DAF-12 ligand-binding domains in complex with coactivator-derived peptides reveal conserved features of ligand-dependent coactivator recruitment shared with mammalian nuclear receptors. In addition, we uncover previously unrecognized interaction features, including motif-specific contacts that extend beyond the canonical LXXLL binding mode of coactivators and distinct patterns of DAF-12 conservation across nematode clades. Structure-guided analyses redefine the interaction motif of the only described parasite-specific coactivator DIP-1 and suggest novel candidate motifs for DAF-12-interacting proteins. Together, these findings establish the structural basis of coactivator binding to nematode DAF-12 and provide mechanistic insight into the transcriptional regulation underlying parasite development. These results expand current understanding of nuclear receptor signaling in parasitic nematodes and provide a framework for the future design of strategies aimed at disrupting DAF-12 activation as a potential antiparasitic approach. Author SummaryParasitic nematodes infect billions of people and livestock worldwide, causing major health and economic burdens, while increasing resistance threatens current treatments. These parasites rely on a developmental switch that allows infectious larvae to resume growth inside their host, a process controlled by the nuclear receptor DAF-12. Blocking this pathway could prevent parasites from establishing infection. However, the molecular mechanisms regulating DAF-12 activation remain poorly understood. Here, we investigate how DAF-12 from two parasitic nematodes, Brugia malayi and Haemonchus contortus, interacts with transcriptional coactivators that enable gene activation, using a combination of biophysical, cellular, structural, and bioinformatic approaches. We identified conserved features of ligand-dependent coactivator recruitment shared with mammalian nuclear receptors as well as nematode-specific interaction mechanisms that vary across evolutionary clades. These findings provide new insights into the structural basis of coactivator binding to DAF-12 and advance our understanding of a key pathway controlling parasitic nematode development.

biochemistry↗

bioRxiv2026

Multiscale conformational sampling of multidomain fusion proteins by a physics informed diffusion model

Multidomain fusion proteins, such as bispecific antibodies, rely on highly flexible linker regions for their therapeutic efficacy. Characterizing these vast conformational ensembles is crucial for rational drug design; however, while all-atom molecular dynamics (MD) is the traditional gold standard, its immense computational cost makes simulating large-scale domain motions prohibitive. Recently, deep generative diffusion models have emerged as a rapid alternative for sampling protein dynamics. Yet, being trained primarily on massive databases of structured, static domains, these generic models often lack the biophysical constraints required to thoroughly sample the large-scale dynamics of highly flexible multidomain architectures. To overcome this, we leverage microsecond MD trajectories of a multidomain protein construct with various linkers to train a multiscale diffusion framework utilizing an Equivariant Graph Neural Network (EGNN). To efficiently model the dynamics of the large molecular complexes, we employ a coarse-grained spatial graph that condenses rigid domains into center-of-mass anchors while preserving explicit backbone resolution for the flexible linker. By further integrating foundational rules in biophysics directly into both the training objective and the inference process, our model generates high-fidelity conformational ensembles that reproduce the thermodynamic distributions of long-timescale MD. This physics-informed approach provides a mathematically stable, highly scalable platform for the rapid multiscale characterization of flexible biologics, significantly accelerating the rational design of fusion protein therapeutics.

bioinformatics↗

bioRxiv2026

Dynamic shifts in brain criticality support cognitive processing

Systems operating near their critical point, or close to a transition between order and disorder, have computational advantages. In the case of neural networks, proximity to criticality is proposed to support optimal brain function. However, different cognitive processes rely on disparate computational demands. Using large-scale electrophysiological recordings in behaving rodents, we examined how critical dynamics in the hippocampus are regulated during learning and sleep-dependent memory consolidation. We found that operating near criticality enables learning by facilitating hippocampal coordination with input regions and maximizing flexibility of neural representations. In contrast, the hippocampal network shifts toward a more ordered, subcritical regime during sleep memory replay, and recovers its proximity to criticality through cholecystokinin interneurons-mediated inhibition. Overall, our findings provide a biophysical substrate for understanding how critical dynamics in neuronal networks can support a variety of brain functions. Importantly, our results suggest that optimal learning systems, whether biological or artificial, may require a dynamic regulation between flexible and rigid states, and can offer biophysical constraints to guide the design of Large Language Models (LLM) tuned to criticality.

neuroscience↗

bioRxiv2026

In vivo motor unit decoding and in vitro cellular characterisation of spinal circuits for urination in adult mice

Urinary dysfunction affects billions of individuals worldwide; however, the fundamental cellular and circuit properties that govern perineal motor control remain largely unknown, serving as a functional "black box". Here, we describe several methods that, when used in concert, characterise cellular, synaptic, and motor unit properties underlying the control of urination in adult mice. High-density electromyography combined with real-time cystometry were used to study external urethral sphincter (EUS) motor units, which follow a hierarchical ("onion skin") recruitment pattern during bladder filling. The transition to the voiding phase is marked by inhibition, followed by synchronised bursts. Furthermore, through concurrent recordings of ischiocavernosus (IC) muscles, the relationship between IC and EUS motor units could be studied to look for shared common inputs that could shed light on circuitry. Whole-cell patch-clamp recordings from retrogradely identified neurons revealed a fundamental biophysical divergence: urinary parasympathetic preganglionic neurons (PPGN) are significantly smaller and more excitable than somatic EUS and IC motoneurons and lack the recurrent excitatory and inhibitory circuits present in both EUS and IC motor pools. Finally, using a novel pressure-clamp preparation, we showed that acute tibial nerve stimulation (a widely used treatment for urinary dysfunction) evokes short-latency inhibition of EUS motor units. Collectively, these methods can be used to delineate patterns of motor unit recruitment, local recurrent microcircuit architecture, and distinct biophysical properties of the perineal motor system, providing mechanistic insights into urinary function.

neuroscience↗

bioRxiv2026

Time-Varying Dynamic Causal Modelling for Sequential Responses: Neural Mechanisms of Slow Cortical Potentials, Preparation, Planning and Beyond

Cognitive processes such as decision-making, working memory, and motor planning operate across a hierarchy of timescales, manifesting as rapid neural transients alongside slower physiological mechanisms like short-term plasticity. Conventional Dynamic Causal Modelling (DCM) limits our ability to study these dynamics by assuming stationary parameters, whilst recent time-varying approaches often rely on segmenting data into epochs. This segmentation artificially resets neural states between windows, fundamentally obscuring the continuous hysteresis essential to sequential processing. To address this limitation, we introduce DCM for Sequential Responses (DCM-SR), a generative framework that embeds parameter evolution directly within the first-level model whilst employing a continuous state-space formulation that removes the requirement for epoching. This approach generalises non-stationarity to all neural mass parameters, including synaptic gains and time constants, modelling them as piecewise smooth trajectories that evolve alongside continuous neural states. Consequently, the model explicitly captures two distinct forms of temporal memory: transient history dependence, where responses are shaped by the carryover effect of recent perturbations, and path dependence, where the systems trajectory through parameter space determines its responsiveness. The framework accommodates both exogenous, stimulus-locked transitions and endogenous, autonomous state changes, permitting inference on both external perturbations and internal drivers of network evolution. Simulations establish the models face validity, demonstrating robust parameter recovery and conservative model selection that accurately discriminates between genuine parameter evolution and spurious complexity. We applied the framework to empirical data from an auditory go/no-go task, modelling a full sequence of cognitive phases from initial cue processing and anticipation through to motor preparation and execution. This analysis established construct validity by resolving the biophysical generators of the contingent negative variation, attributing this slow potential to sustained thalamocortical drive and deep-layer hyperpolarisation rather than superficial-layer activity. Furthermore, the model captured trial-specific modulations of the hyperdirect pathway during motor inhibition, tracking the dynamic interplay between prefrontal executive control and basal ganglia gating. DCM-SR offers the first principled approach to decomposing compound signals such as slow cortical potentials into evolving synaptic mechanisms and continuous state trajectories, and provides a necessary bridge for investigating the biophysical implementation of extended cognitive phenomena including evidence accumulation and physiological hysteresis.

neuroscience↗

bioRxiv2026

Digital Twins for Fungal Computing: Viable XOR Regimes, Parameter Inference, and Waveform-Guided Rediscovery

Fungal substrates are promising candidates for unconventional computing, but specimen-to-specimen variability makes logic-gate fabrication difficult to reproduce. This paper presents a digital-twin workflow for fungal excitable networks and evaluates three components needed for computeraided design: identifying parameter regimes that support XOR computation, inferring latent biophysical parameters from electrical characterization data, and refining those inferred parameters by waveform matching. The model represents mycelium as a random geometric graph with FitzHugh-Nagumo node dynamics and memristive edge conductances. A systematic optimization study over 160 simulated specimens identifies a viable XOR subspace defined by tuned biophysical parameters, electrode geometry, and stimulus timing. A characterization study over 400 simulated specimens uses step-response, paired-pulse, and triangle-sweep protocols to extract 94 response features. Random forest regressors recover several latent parameters reliably (R2 = 0.912 for{tau} v, 0.816 for{tau} w, 0.717 for a), while vscale, Ron, and Roff remain weakly identifiable. On a preliminary rediscovery validation using 15 optimized specimens (20-50 nodes), ML initialization followed by local waveform-matching refinement reduces mean waveform mismatch from 1.070 to 0.042 (96.0%; one-sided Wilcoxon p = 3.1 x 10-5) and reduces mean core-parameter error from 16.6% to 8.8% (p = 6.1 x 10-5). A sensitivity analysis on 72 viable specimens reveals that{tau} w and are the most consequential parameters for XOR twin accuracy, while vscale and Roff are both hard to identify and tolerant to error. These results show that fungal digital twins can already narrow the search for viable computational substrates, partially recover the excitable dynamics that govern them, and support small-scale specimen-specific refinement without yet claiming full XOR transfer.

bioengineering↗

bioRxiv2026

Functionally convergent but parametrically distinct solutions: Robust degeneracy in a population of computational models of early-birth rat CA1 pyramidal neurons

BackgroundFlexibility and robustness of neuronal function are closely linked to degeneracy, the ability of distinct structural or parametric configurations to produce similar functional outcomes. At the cellular level, this often manifests as ion-channel degeneracy, in which multiple combinations of intrinsic conductances yield comparable electrophysiological phenotypes. MethodologyWe used a population-based, data-driven modelling framework to generate large ensembles of biophysically detailed CA1 pyramidal neuron models constrained by somatic electrophysiological features extracted from patch-clamp recordings in acute slices from early-birth rats. 10 reconstructed morphologies were incorporated, and model populations were analyzed using parameter correlation analysis, principal component analysis, and generalization tests to assess robustness, degeneracy, and morphology dependence of intrinsic properties. ConclusionsAcross the model population, similar somatic firing behaviours emerged from widely different combinations of intrinsic parameters, demonstrating robust two-level ion channel degeneracy both within and across morphologies. Each morphology occupied a distinct region of parameter space, indicating morphology-specific compensatory effects, while weak pairwise parameter correlations suggested distributed compensation rather than tight parameter dependencies. Even with a fixed morphology, multiple parameter subspaces supported comparable electrophysiological phenotypes. Generalization across morphologies was structure-dependent and non-reciprocal, with successful parameter similarity occurring preferentially between structurally similar neurons. Interestingly, to accurately simulate spike-frequency adaptation, it was important to retain some kinetic properties of the ion channel models as free parameters during optimization. Together, these findings show that dendrite morphology shapes the valid parameter space, and similar electrophysiology of CA1 pyramidal neurons arises from the interplay between structural variability and ion-channel diversity. This work highlights the importance of population-based modelling for capturing biological variability and provides insights into how neuronal robustness might be maintained despite substantial heterogeneity, and offers a scalable pipeline for generating biophysically realistic CA1 neuron populations for use in network simulations. Author summaryNeurons must reliably process information even though their internal components, such as ion channels and cellular shape, can vary widely from cell to cell. How stable behaviour emerges from such variability is a fundamental question in neuroscience. In this study, we explored this problem using detailed computer models of early-birth rat hippocampal CA1 pyramidal neurons, a cell type that plays a central role in learning and memory. Instead of building a single "average" neuron model, we created large populations of models that all reproduced key experimental recordings but differed in their internal parameters. We found that neurons with different shapes and different combinations of ion channels could nevertheless generate similar electrical activity. This phenomenon, known as ion channel degeneracy, allows neurons to remain functional despite biological variability or perturbations. Our results show that neuronal shape strongly influences which parameter combinations are viable, but that multiple solutions exist even for the same morphology. The population of models we provide offers a resource for future studies of early-birth CA1 pyramidal cell function and dysfunction.

neuroscience↗

bioRxiv2026

Coupled beta and high-frequency oscillations emerge from synchronized bursting in a minimal model of the parkinsonian subthalamic nucleus

Local field potentials recorded from the subthalamic nucleus (STN) in Parkinson's disease (PD) exhibit a distinctive multiscale spectral signature: exaggerated beta-band oscillations (13-30 Hz) coupled to high-frequency oscillations (HFOs, 200-400 Hz), with HFO amplitude being phase-locked to the beta cycle. This phase-amplitude coupling (PAC) has been identified as a promising biomarker of the parkinsonian state, yet no biophysical model has explained how it emerges, what determines the HFO frequency, or how HFOs can exist without beta modulation in the medicated STN. Here we show that a heterogeneous population of excitatory Izhikevich neurons with recurrent coupling produces three dynamical regimes: (i) asynchronous tonic firing, (ii) asynchronous bursting, in which neurons burst individually producing broadband HFO power but without coherent population-level PAC, and (iii) synchronous bursting, which gives rise to beta-HFO PAC. The regimes are governed by two biophysically interpretable parameters that capture complementary effects of dopamine depletion: one reflecting changes in intrinsic neuronal excitability, the other reflecting changes in synaptic coupling strength. The transition from asynchronous to synchronous bursting in this model captures the emergence of pathological STN neuronal activity in the parkinsonian state. HFO peak frequency varies continuously across the two-parameter landscape, suggesting a possible mechanism of the clinically observed shift from slow (200-300 Hz) to fast (300-400 Hz) HFOs between medication states. The character of the synchronization transition depends on baseline excitability, ranging from a sharp co-emergence of bursting and synchrony at low excitability to a decoupled transition at intermediate excitability, where the bursting fraction saturates while the population synchronization continues to increase with coupling. We also extend the model to include reciprocal coupling to an inhibitory population of globus pallidus externa (GPe)-like neurons and report a similar asynchronous-to-synchronous bursting transition and beta-HFO PAC emerging in the STN population, with the beta rhythm being set by the delay in the synaptic coupling between the two populations. The model generates testable predictions for future clinical and experimental studies, provides a numerical dissection of how mesoscopic LFP features map onto microscopic neuronal dynamics, and serves as a computational building block for future circuit-level models that may inform brain stimulation strategies tailored to the patient-specific dynamical state of the STN.

neuroscience↗

bioRxiv2026

Mapping Slow Speckle Dynamics to Probe Cellular Metabolic Activity In Vivo using Laser Speckle Contrast Imaging

SignificanceLaser speckle contrast imaging (LSCI) is widely used to measure blood flow, but speckle fluctuations may also encode biologically meaningful dynamics beyond perfusion. Foundational studies in dynamic light scattering (DLS) and micro-optical coherence tomography (OCT) have also demonstrated that slow coherent signal fluctuations can arise from energy-dependent intracellular motion in in vitro and ex vivo systems. Building upon these advances, recent work has shown that LSCI has the potential to detect slow speckle dynamics (SSD) correlated with cellular dynamics in vivo. However, the biophysical mechanisms underlying SSD in intact brain tissues remain insufficiently validated. Establishing a mechanistic bridge from controlled ex vivo and in vitro conditions to in vivo brain measurements is critical for translating speckle-based imaging beyond perfusion measurements to enable label-free assessment of cellular and metabolic activity in disease models. AimThe objective of this study is to investigate the biophysical origin of the SSD in vivo and evaluate its sensitivity to intracellular metabolic activity in brain tissue. ApproachWe utilize an epi-illumination LSCI system to measure speckle contrast as a function of camera exposure time and extract characteristic decorrelation time constants. SSD was investigated in acute mouse brain slices, where blood flow is absent, to eliminate vascular confounds. Cellular metabolism was systematically modulated using 2-deoxyglucose and glucose. Complementary in vivo measurements were performed to reveal SSDs response to hyperoxia and normoxia after ischemic stroke. ResultsSSD signals persisted in acute brain slices in the absence of blood flow. Inhibition of glycolysis significantly reduced SSD, while restoration of metabolic substrates partially recovered the signal. In in vivo measurements, SSD increased during hyperoxia compared to normoxia after ischemic stroke, suggesting increased oxygen-supported cellular metabolic activity. ConclusionsThese results indicate that SSD is sensitive to energy-dependent cellular processes closely tied to metabolic activity. SSD represents a previously uncharacterized, label-free in vivo optical contrast that enables assessment of cellular metabolic activity as well as vascular dynamics. This work establishes a mechanistic foundation for using SSD as a general optical marker of cellular viability in in vivo measurements.

neuroscience↗

bioRxiv2026

ProDive reveals pervasive cross-family protein fragment reuse

Cross-family reuse of short protein fragments has been a long-standing mystery whose resolution first demands an algorithm for their systematic detection. Here we introduce ProDive, a closed-form symmetric KL divergence between profile HMMs that enables GPU-accelerated, fragment-level screening across all 25,545 Pfam families. ProDive identifies [~]318,000 cross-family fragment correspondences involving compact cores of 8-13 residues with RMSD values far below random background. Their organisation into diverse graph communities and four-fold enrichment in de novo designed proteins point away from family-specific functions and toward a general biophysical property. Their helix dominance and moderate solvent exposure suggest a role in folding initiation--a link corroborated by overlap with experimentally measured{phi} -values and by a monotonic density gradient across disordered regions. Together, these observations converge on a single explanation: cross-family fragment reuse likely reflects shared requirements for early structure formation during folding, the one biophysical constraint common to all proteins.

bioinformatics↗