Biology subjects

Miles, C. E.

Publications and source records attributed to Miles, C. E..

12 recordsLinked to original sources

bioRxiv2026

Hierarchy of spindle forces in prometaphase

Dramatic movements characterize rapid assembly of mitotic spindle in prometaphase: chromosomes congress to the spindle equator, while spindle poles segregate rapidly elongating the spindle. Direct biophysical measurements of forces driving these movements are lacking, but the forces could be learned from the rapid pole and chromosome movements. We analyzed 3D trajectories of chromosomes and spindle poles in the wild-type and PRC1-depleted cells during early prometaphase and reconstructed the spindle forces. This analysis posits that (i) chromosomes do not interact with each other, except for a short-range steric repulsion, (ii) effective pole-chromosome forces are repulsive at short and attractive at long distances, (iii) dominant forces of spindle poles segregation are interpolar in origin. The important implication of these results is that the pole-pole segregation is robust, largely insensitive to regional variations inside the cell and to complex process of chromosome congression.

biophysics↗

bioRxiv2026

Efficient approximate Bayesian inference for large, heterogeneous biological datasets

Simulation-based inference (SBI) offers a route to connect biological data with mechanistic models. At the frontier of biology, datasets continue to grow in scale and richness, and increasingly record heterogeneity between individual observations that a mechanistic model can take into account. Approximate Bayesian Computation (ABC) remains a popular approach to SBI due to its ease of use, but incorporating this heterogeneity often requires a separate simulation of each observation, which is prohibitively expensive at scale. A previously proposed technique, minibatch Sequential Monte Carlo ABC (SMC ABC), reduces this cost by simulating a fixed-size random subset of observations for each proposal, at the price of sampling noise that limits precision. Here, we present an adaptive minibatch approach that uses small batches during early exploration and increases the batch size as the posterior is refined. The adaptive batch size is set by the target precision and does not grow with the size of the dataset, so the savings over full-data SMC ABC increase for larger datasets. We demonstrate the method on synthetic biological models and on real single-cell gene-expression data that vary in scale and type of heterogeneity, including cell geometry and initial condition. In each case, the adaptive scheme obtains posteriors close to those of full-data SMC ABC at a fraction of the cost. Altogether, adaptive minibatching improves the tradeoff between speed and precision in SMC ABC and opens the door to mechanistic inference on large, heterogeneous biological datasets.

bioinformatics↗

bioRxiv2026

BiLO: Bilevel Local Operator Learning for PDE Inverse Problems

We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to locally approximate the PDE solution operator in the neighborhood of a given set of PDE parameters, which enables an accurate approximation of the descent direction for the upper level optimization problem. The lower level loss function includes the least-square penalty of both the residual and its derivative with respect to the PDE parameters. We apply gradient descent simultaneously on both the upper and lower level optimization problems, leading to an effective and fast algorithm. The method, which we refer to as BiLO (Bilevel Local Operator learning), is also able to efficiently infer unknown functions in the PDEs through the introduction of an auxiliary variable. We provide a theoretical analysis that justifies our approach. Through extensive experiments over multiple PDE systems, we demonstrate that our method enforces strong PDE constraints, is robust to sparse and noisy data, and eliminates the need to balance the residual and the data loss, which is inherent to the soft PDE constraints in many existing methods.

systems biology↗

bioRxiv2026

Bayesian BiLO: Bilevel Local Operator Learning for Efficient Uncertainty Quantification of Bayesian PDE Inverse Problems with Low-Rank Adaptation

Uncertainty quantification in PDE inverse problems is essential in many applications. Scientific machine learning and AI enable data-driven learning of model components while preserving physical structure, and provide the scalability and adaptability needed for emerging imaging technologies and clinical insights. We develop a Bilevel Local Operator Learning framework for Bayesian inference in PDEs (B-BiLO). At the upper level, we sample parameters from the posterior via Hamiltonian Monte Carlo, while at the lower level we fine-tune a neural network via low-rank adaptation (LoRA) to approximate the solution operator locally. B-BiLO enables efficient gradient-based sampling without synthetic data or adjoint equations and avoids sampling in high-dimensional weight space, as in Bayesian neural networks, by optimizing weights deterministically. We analyze errors from approximate lower-level optimization and establish their impact on posterior accuracy. Numerical experiments across PDE models, including tumor growth, demonstrate that B-BiLO achieves accurate and efficient uncertainty quantification. MSC Classification: 65M32, 62F15, 68T07

systems biology↗

bioRxiv2025

The kinetochore corona orchestrates chromosome congression through transient microtubule interactions

For proper segregation of chromosomes and successful cytokinesis, chromosomes must first congress - gather in a tight plate near the spindle equator. Molecular mechanism(s) of congression are not fully understood. Here we combine live cell microscopy, perturbations of microtubule motor activities, correlative light/electron microscopy, and computational modeling, to quantitatively characterize the early-prometaphase movements that bring the scattered chromosomes to the equator in human RPE1 cells. We find that the early-prometaphase movements are predominantly directed toward the spindle center and not the spindle poles. Centromere velocity of the centripetal movements is not constant with centromeres moving faster at larger distances from the spindle center. We also detect that numerous short microtubules appear at kinetochores at the earliest stages of spindle assembly and prior to chromosome congression. Computational modeling reveals that a mechanism based on brief, stochastic, minus-end directed interactions between the short microtubules protruding from the kinetochores and long appropriately curved microtubules within the spindle accurately predicts the observed distance-velocity function. Further, the model predicts that insufficient numbers of microtubules protruding from the kinetochores decreases the velocity and randomizes directionality of congression movements. These predictions match changes in the chromosome behavior observed in cells with suppressed nucleation of microtubules at the kinetochore corona (RPE1 Rod{Delta}/{Delta}). In contrast, predictions of computational models based on continuous pulling forces at kinetochores differ significantly from the experimental observations. Together, live-cell observations and modeling reveal a novel mechanism that enables the efficient and synchronized arrival of chromosomes to the spindle equator. Significance StatementFor equal segregation into daughter cells, chromosomes, scattered in a large volume at the onset of cell division, must congress into a narrow plate near the equator of the mitotic spindle. Molecular mechanisms of congression remain obscure. Here we use live-cell microscopy and structural analyses to quantitatively characterize chromosome behavior during congression in human cells. From these quantifications we derive a computational model that accurately predicts directionality and velocity of chromosome movements and suggests that the force for these movements arises from stochastic, transient, minus-end directed interactions between short microtubules protruding from the kinetochores and long appropriately shaped microtubules within the spindle. The model also accurately predicts changes in chromosome behavior in cells with functionally deficient kinetochores.

cell biology↗

bioRxiv2025

Scalable Inference and Identifiability of Kinetic Parameters for Transcriptional Bursting from Single Cell Data

MotivationStochastic gene expression and cell-to-cell heterogeneity have attracted increased interest in recent years, enabled by advances in single-cell measurement technologies. These studies are also increasingly complemented by quantitative biophysical modeling, often using the framework of stochastic biochemical kinetic models. However, inferring parameters for such models (that is, the kineticrates of biochemical reactions) remains a technical and computational challenge, particularly doing so in a manner that can leverage high-throughput single-cell sequencing data. ResultsIn this work, we develop a Chemical Master Equation reference library-based computational pipeline to infer kinetic parameters describing noisy mRNA distributions from single cell RNA sequencing data, using the commonly applied stochastic telegraph model. Our pipeline also serves as a tool for comprehensive analysis of parameter identifiability, in both a priori (studying model properties in the absence of data) and a posteriori (in the context of a particular dataset) use cases. The pipeline can perform both of these tasks, i.e. inference and identifiability analysis, in an efficient and scalable manner, and also serves to disentangle contributions to uncertainty in inferred parameters from experimental noise versus structural properties of the model. We found that for the telegraph model, the majority of the parameter space is not practically identifiable from single cell RNA sequencing data, and low experimental capture rates worsen the identifiability. Our methodological framework could be extended to other data types in the fitting of small biochemical network models. AvailabilityAll code relevant to this work is available at https://github.com/Read-Lab-UCI/TelegraphLikelihoodInfer.

biophysics↗

bioRxiv2024

Incorporating spatial diffusion into models of bursty stochastic transcription

The dynamics of gene expression are both stochastic and spatial at the molecular scale. Mechanistic models of mRNA count distributions have revealed countless insights but largely neglect the frontier of subcellular spatial resolution. The spatial distribution of mRNA encodes their dynamics, including inherently spatial processes like transport to the nuclear boundary for export. Due to the technical challenges of spatial stochastic processes, tools for studying these subcellular spatial patterns are still limited. Here, we introduce a spatial stochastic model of nuclear mRNA with telegraph transcriptional dynamics. Observations of the model can be concisely described as following a spatial Cox process driven by a stochastically switching partial differential equation (PDE). We derive analytical solutions for spatial and demographic moments and validate them with simulations. We show that the distribution of mRNA counts can be accurately approximated by a Poisson-Beta distribution with tractable parameters, even with complex spatial dynamics. This observation allows for efficient parameter inference demonstrated on synthetic data. Altogether, our work adds progress toward a new frontier of subcellular spatial resolution in inferring the dynamics of gene expression from static snapshot data.

biophysics↗

bioRxiv2024

Coaching ribosome biogenesis from the nuclear periphery

Severe invagination of the nuclear envelope is a hallmark of cancers, aging, neurodegeneration, and infections. However, the outcomes of nuclear invagination remain unclear. This work identified a new function of nuclear invagination: regulating ribosome biogenesis. With expansion microscopy, we observed frequent physical contact between nuclear invaginations and nucleoli. Surprisingly, the higher the invagination curvature, the more ribosomal RNA and pre-ribosomes are made in the contacted nucleolus. By growing cells on nanopillars that generate nuclear invaginations with desired curvatures, we can increase and decrease ribosome biogenesis. Based on this causation, we repressed the ribosome levels in breast cancer and progeria cells by growing cells on low-curvature nanopillars, indicating that overactivated ribosome biogenesis can be rescued by reshaping nuclei. Mechanistically, high-curvature nuclear invaginations reduce heterochromatin and enrich nuclear pore complexes, which promote ribosome biogenesis. We anticipate that our findings will serve as a foundation for further studies on nuclear deformation. HighlightsNuclear invaginations regulate ribosome biogenesis by physically contacting nucleoli. High-curvature nuclear tunnels increase ribosome biogenesis. Nanopillars reduce ribosome biogenesis by transforming high-curvature nuclear invaginations to low-curvature ones.

cell biology↗

bioRxiv2023

ADP release can explain spatially-dependent kinesin binding times

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that most but not every motor binding event is limited by their ADP state. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and spatial distances. Significance StatementCytoskeletal-motor assemblies self-organize to achieve cellular functions ranging from delivering intracellular cargoes to generating forces in mitosis. Advancements in single-molecule experiments have revealed immense detail about motor detachment and stepping, but relatively little regarding the attachment process. With newly available spatially parameterized motor binding times from an optical trap, the evaluation of mechanistic models for binding becomes possible. We find that a model limited by both diffusive search and ADP-release best explains the data. The coupled chemo-mechanical nature of this interaction is more malleable than either separately, possibly explaining the rich diversity and regulation observed in cells. More broadly, our study provides a timely vignette on leveraging computations with experiments to understand how geometry and other complexities shape protein-protein interactions.

biophysics↗

bioRxiv2021

Mechanical torque promotes bipolarity of the mitotic spindle through multi-centrosomal clustering

Intracellular forces shape cellular organization and function. One example is the mi-totic spindle, a cellular machine consisting of multiple chromosomes and centrosomes which interact via dynamic microtubule filaments and motor proteins, resulting in complicated spatially dependent forces. For a cell to divide properly, is important for the spindle to be bipolar, with chromosomes at the center and multiple centrosomes clustered into two poles at opposite sides of the chromosomes. Experimental observations show that in unhealthy cells, the spindle can take on a variety of patterns. What forces drive each of these patterns? It is known that attraction between centrosomes is key to bipolarity, but what the prevents the centrosomes from collapsing into a monopolar configuration? Here, we explore the hypothesis that torque rotating chromosome arms into orientations perpendicular to the centrosome-centromere vector promotes spindle bipolarity. To test this hypothesis, we construct a pairwise-interaction model of the spindle. On a continuum version of the model, an integro-PDE system, we perform linear stability analysis and construct numerical solutions which display a variety of spatial patterns. We also simulate a discrete particle model resulting in a phase diagram that confirms that the spindle bipolarity emerges most robustly with torque. Altogether, our results suggest that rotational forces may play an important role in dictating spindle patterning.

biophysics↗

bioRxiv2019

kSHREC ‘Delta’ reflects the shape of kinetochore rather than intrakinetochore tension

Distance between fluorescent spots formed by various kinetochore proteins ( Delta) is proposed to reflect the level of intrakinetochore tension (IKT). However, larger-scale changes in the kinetochore architecture may also affect Delta. To test this possibility, we measure Delta in long kinetochores of Indian muntjac (IM) whose shape, size, and orientation are discernable in conventional light microscopy. We find that architecture of IM kinetochores and the value of Delta change minimally when microtubule-mediated forces are suppressed by Taxol. In contrast, large decreases of Delta observed in Taxol-treated human cells coincide with prominent changes in length and shape of the kinetochore. We also find that inner and outer kinetochore proteins intermix within a common spatial compartment instead of forming separate thin layers. These observations, supported by computational modelling, suggest that changes in Delta reflect changes in the kinetochore shape rather than the level of IKT.

cell biology↗

bioRxiv2019

A unified role for membrane-cortex detachment during cell protrusion initiation

Cell morphogenesis employs a diversity of membrane protrusions. They are discriminated by differences in force generation. Actin polymerization is the best studied mechanism of force generation, but growing interest in how variable molecular conditions and microenvironments alter morphogenesis has revealed other mechanisms, including intracellular pressure. Here, we show that local depletion of membrane cortex links is an essential step in the initiation of both pressure-based and actin-based protrusions. This observation challenges the quarter-century old Brownian ratchet model of actin-driven membrane protrusion, which requires an optimal balance of actin filament growth and membrane tethering. An updated model confirms membrane-filament detachment is necessary to activate the ratchet mechanism. These findings unify the regulation of different protrusion types, explaining how cells generate robust yet flexible strategies of morphogenesis.

cell biology↗