Biology subjects

Thomson, M.

Publications and source records attributed to Thomson, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Lineage tracing reveals asynchronous fate restriction in mouse embryos and stem cell-derived embryo models

How a flexible mammalian embryo gives rise to a reproducible body plan remains unclear, partly because cell lineages cannot be followed continuously in vivo. We combined DNA Typewriter lineage recording with tetraploid complementation to reconstruct high-resolution single-cell genealogies across mouse development and built FateVec, an analysis framework that infers fate-bias dynamics and lineage-associated transcriptional programs from reconstructed phylogenies. Loss of developmental potential proved progressive and asynchronous, with multiple lineages acquiring detectable fate bias before morphological diversification at gastrulation. Within the neural crest, most cells remained multipotent over an extended window, while independent founder clades differed markedly in restriction timing. Extending lineage recording to a stem-cell-derived embryo model revealed that cells with shared transcriptional identities can arise from distinct lineage relationships. We further found embryonic-lineage contributions to extraembryonic-like states, exposing an unexplored lineage plasticity. This lineage-resolved view of embryogenesis reveals that cell identity alone predicts neither developmental ancestry nor the timing of fate restriction.

developmental biology↗

bioRxiv2026

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

Computational design has transformed many fields of engineering, where simulators can explore millions of candidate design configurations before experimental development and testing. Therapeutic design in biomedicine has resisted computational design approaches because disease progression and therapeutic response emerge from interactions among many cell types within human tissue, governed by biochemical parameters that are largely unknown and potentially unknowable. Here, we introduce the Cell Interaction Foundation Model (CIFM), a virtual tissue model that forward-simulates the transcriptional dynamics of cells in human tissue under arbitrary therapeutic conditions based upon a spatial transcriptomic seed. CIFM is a geometric graph neural network trained by self-supervised masked-transcriptome prediction on millions of cellular microenvironments spanning human tissue types and disease states; generative, auto-regressive, monte-carlo play-out, then, simulates transcriptional dynamics under combinatorial perturbations from a spatial transcriptomic seed. We validate CIFM by showing accuracy gains in gene expression prediction and imputation, disease classification, recapitulation of perturbation responses in prostate cancer models, and recovery of T cell-tumor signaling measured in cell-cell sequencing experiments. Beyond such conventional tasks, CIFM enables target identification and therapeutic design through generative tissue simulation play-outs. Analyzing over 106 single and combinatorial perturbations, CIFM designs immunotherapy strategies for cancer and autoimmune disease that exploit combinatorial manipulation of signaling pathways to induce or suppress immune activation. Broadly, CIFM shows how generative artificial intelligence methods can be applied to model emergent behavior in highly interacting biological systems, yielding new approaches to fundamental understanding of tissue behavior as well as large-scale therapeutic design.

bioinformatics↗

bioRxiv2026

Energetic gradients emerge in developing motor-microtubule structures

Living matter produces a variety of beautiful spatiotemporal structures and patterns that are not enduringly present in their nonliving counterparts. These ordered, non-equilibrium steady states are often sustained through the consumption of energy. Here, we investigate the energetic cost of assembling an ordered aster from an initially disordered, uniform mixture of cytoskeletal microtubules and kinesin motors. Using a calibrated fluorescent ATP reporter, we measure reproducible radial ATP gradients on scales of tens of microns that establish within, and persist over, tens of minutes, alongside coupled spatial gradients in motor density. These appreciable gradients are predicted by a reaction-diffusion model that acknowledges the localization of ATP consumption to regions where both molecular motors and microtubules are sufficiently abundant to encourage consumption, as confirmed by finite element modeling. With our results, we compare the power per volume required by our cytoskeletal networks with the known power per volume expenditure in cells. Comparison of our measured results with estimates of the dissipative processes available to motor-microtubule mixtures leads to the hypothesis that maintaining spatial motor gradients dominates the energetic demand in this system. Our direct quantification of energetic fluxes across space unlocks future explorations of what steady states are accessible to cells, and how the cytoskeleton drives broad spatial organization. SignificanceHow much energy do organisms pay to form and maintain their organizing biochemical patterns? Existing measurements of cellular metabolism and energy expenditures largely resolve net or supply-side biochemical fluxes, without spatial information, impeding the study of this basic question. Here, we develop an experimental approach to directly measure the distributions of biochemical energy that respond to power expenditures of cytoskeletal motor-microtubule networks as they form aster structures, reminiscent of those found in the mitotic spindle. As these structures self-assemble, calibrated readouts in real molecular units register large, reproducible, and long-lived gradients of ATP. We interpret these measurements by developing theory to account for the functional destinies of energy expenditure. These advances clarify outstanding questions of energy in living matter.

biophysics↗

bioRxiv2026

A low-dimensional transcriptional code enables decoding of BMP/TGFβ signaling from single-cell transcriptomes

Cells interpret complex extracellular environments through signaling pathways that compress diverse ligand inputs into a limited set of intracellular mediators. How this information is encoded in transcriptional responses, and whether it can be decoded to infer the original signaling environment, remains unclear. Here, we systematically map BMP/TGF{beta} transcriptional responses using single-cell RNA sequencing across 48 ligand-concentration conditions, generating a multi-ligand dose-response atlas. We find that, for each ligand, transcriptional responses collapse onto a one-dimensional, highly coordinated program in which concentration modulates response amplitude without altering gene identity. Across ligands, we find a small number of distinct, ligand-dependent transcriptional programs. We leverage this structured encoding to define a quantitative perception score that captures pathway activity at single-cell resolution. Finally, we train a machine learning model to decode extracellular ligand concentrations from single-cell transcriptomes. The model further generalizes to in vivo intestinal epithelium, where it reconstructs the spatial BMP gradient profile. Together, these results reveal a low-dimensional transcriptional code in BMP/TGF{beta} signaling that constrains cellular responses and enables quantitative inference of extracellular signaling environments from gene expression.

systems biology↗

bioRxiv2025

Pretrained protein language models choose between sequence novelty and structural completeness

Protein language models (PLMs) have gained increasing acceptance in tasks ranging from variant effect prediction in disease to optimization and de novo design of proteins with improved stability, target-binding affinity, and catalytic performance. Despite encouraging performance in such applications, little is understood as far as the degree to which PLM-generated sequences - putative novel protein outputs - recapitulate the broad biophysical rules and diversity of sequence, structure, and function that defines natural protein-space, vital knowledge for boosting the design capacity of PLMs in ever-more-complex systems. Towards this end, we computationally profile and characterize the sequence and structure statistics and properties of hundreds of thousands of potential small proteins proposed through free unconstrained generation from architecturally distinct PLMs. We show that although these models exhibit a prodigious latent capacity to access novel amino-acid sequences, they struggle to approach the structural variation that exists on plain display in nature. Moreover, we uncover a stark tradeoff between prioritizing sequence novelty or structural breadth, exemplified by a "helical bundle trap" that dominates model output when aiming outside the comfortable bounds and evolutionary organization of natural sequences. These findings underscore a critical need for strategies that can rapidly guide PLMs into unlocking through generation the full richness of protein sequence, structure, and function that is consistent with governing biophysics but tantalizingly untapped as of yet in design contexts. Author summaryLarge language models (LLMs) like GPT arent just for human text. Spinoff versions that treat protein and DNA sequences as special "languages" of their own, complete with preferred words and grammars, are being used to identify disease-causing genes and mutations and to design new treatments and drugs for clinical testing. But as anyone who has used an LLM chatbot has probably experienced at one time or another, these models can act narrow-minded or nonsensical, reducing their generality and utility. Protein language models are no exception to such flaws of reasoning. We show that protein language models face a fundamental choice between suggesting novel sequences that look nothing like natural ones and capturing the full range of three-dimensional shapes and structures responsible for the diverse functioning of molecular machines. Managing or bypassing this tradeoff is consequently of high import for designing proteins that impart novel functions and activities for therapeutic targeting and beyond.

bioinformatics↗

bioRxiv2025

Rapid discovery of new-to-nature protein domains by novelty-first forcing of language models

Approximations for the existence and extent of physically permissible protein structures beyond those found in nature vary wildly. As predicted structure databases swell thanks to abundant sequence data and generative protein design models concurrently grow in their power to propose new aspects of protein structure, these questions and those of which essential features (e.g. stability, function, robustness) distinguish natural domains from novel ones have been cast in even sharper relief. We demonstrate that protein language models (PLMs) can simultaneously innovate in sequence and structure to suggest new-to-nature protein domains displaying supersecondary and tertiary elements outside of categorized CATH superfamilies. Developing and applying two orthogonal processes for obtaining compact and globular folds from PLMs without bias from other physicochemical or functional constraints, we discover putative novel domains that emerge parallel to known natural ones at rates far exceeding those obtainable by bioinformatic mining of structure databases. Computational characterization of these domain candidates indicates that many exhibit reasonable folding thermodynamics and kinetics, suggesting that natural protein structure-space is far from biophysically complete. These results point away from stability as the definitive selective force behind the observed landscape of real protein folds, and insinuate that many unrealized folds may be equally consistent with the structural rules of protein-based life.

bioengineering↗

bioRxiv2025

Designing signaling environments to modulate neural progenitor cell differentiation with regulatory network models

During development, combinatorial signals modulate progenitor differentiation to build populations of diverse cell types. Classical deterministic fate-control circuits focus on stable commitment but do not explain how a diverse cell population is generated and tuned by signals. Here, using single-cell RNA-seq profiling of neural progenitor cells (NPCs) upon combinatorial signal inputs, we identify a candidate circuit-level mechanism for controlling cell type compositions through a noise-gated toggle switch. With 40 signaling conditions, we show that combinatorial signal inputs modulate the neuronal-glial population proportions following a simple log-linear model. Regulatory network models built by D-SPIN quantitatively capture cell state distribution shifts induced by signal combinations, and indicate a circuit model for the probabilistic fate specification by a toggle switch with controlled transcriptional heterogeneity. Noise levels drive the system from highly plastic to committed states, allowing the relative level of signal inputs to shift population structures. The model further predicts an early bipotent state expressing lineage-specifying factors of both fates, which we verify in our single-cell profiling experiment and a mouse embryonic visual cortex development dataset. Our work identifies a design principle of noise-driven additive regulation in the logarithmic cell-fate probability space, providing a new strategy for population-level stem cell control.

systems biology↗

bioRxiv2025

Reverse Engineering the Evolutionary Logic of Cytoskeletal Dynamics

Eukaryotic cells generate mechanical forces through cytoskeletal filaments actively reorganized by families of molecular motors. However, how motor sequence specifies filament organization dynamics remains unclear. We developed ActiveDROPS, a cell-free platform that expresses kinesin variants in bacterial lysate droplets and quantitatively maps the resulting microtubule dynamics into a common phenotype space. Across a library of twelve kinesin-1 homologs, we observed three phenotypes: "Slow-Sustained" flows that activate after 8 h, reach peak mean velocities of 24 nm/s and decay after [~]32 h; "Fast-Burst" flows that activate within minutes and dissipate within 2 h, reaching velocities up to 900 nm/s; and a [~]36-h "Multiphase" progression through nematic, rotational, and contractile states. Microtubule gliding assays and molecular dynamics simulations using AlphaFold-predicted structures linked the "Fast-Burst" phenotype to generally faster motility and more favorable motor-tubulin interactions than "Slow-Sustained". By replacing the microtubule-binding region of a "Slow-Sustained" motor with that of a "Fast-Burst" motor, we generated a "Fast-Sustained" chimera with flows that activate within 1 h, peak at 70 nm/s and persist for 16 h, showing that recombination can reprogram the parental relationship between speed and timing of microtubule-motor self-organization. These results reveal a constrained logic through which kinesin sequence shapes cytoskeletal dynamics, providing a framework for dissecting the mechanical repertoire available to cellular systems.

synthetic biology↗

bioRxiv2025

Force Propagation in Active Cytoskeletal Networks

Conventional materials science establishes clear structure-function relationships: grain boundaries modulate strength by blocking dislocations, and defect density limits conductivity through electron scattering. Active matter presents a fundamentally different challenge, components continuously consume energy to generate forces, creating a dual identity as both structural elements and force-generating machines. This material-machine duality raises a fundamental question: which structural parameters govern functional behavior in systems where constituents actively generate forces rather than merely responding to external inputs? Here we demonstrate that percolation is the critical mechanism that transforms active cytoskeletal networks from energy-dissipating materials into work-performing machines. Using light-controlled microtubule-kinesin networks, we show that increasing bundle length from 0.9 {micro}m to 5 {micro}m triggers a percolation transition that enables global force organization. Networks below the threshold remain incapable of performing coordinated work, while networks above it develop correlation lengths exceeding 240 {micro}m, generate 25-fold stronger forces, and extract 1000-fold more mechanical work, powering transport of live cells across 800 {micro}m distances. Network simulations reveal that introducing just 5-10% longer bundles creates a connected component containing 90% of microtubules, establishing the precise threshold that drives this material-to-machine transition. Our findings establish percolation as the fundamental mechanism governing whether active matter systems function as passive materials or as coordinated machines--providing design principles for both synthetic and biological systems. increasing bundle length from 0.9 {micro}m to 5 {micro}m increasing bundle length from 0.9 {micro}m to 5 {micro}m

biophysics↗

bioRxiv2025

A guide to D-SPIN: constructing regulatory network models from single-cell RNA-seq perturbation data

D-SPIN (Dimension-scalable Single-cell Perturbation Integration Network) is a computational framework for constructing regulatory network models from large-scale single-cell perturbation data. D-SPIN models the transcriptional state of cells as a set of individual genes or gene programs that interact through a condition-dependent spin network model or Markov random field. A single unified network is learned across multiple conditions, where the perturbations modulate the activity of individual network nodes. The architecture allows information integration between different perturbation conditions and supports efficient inference algorithms that scale to thousands of genes, thousands of conditions, and millions of single cells. For interpretability, D-SPIN can also operate at the level of programs of co-expressing gene sets, which are extracted via unsupervised orthogonal non-negative matrix factorization (oNMF). Here we present the theory and detailed procedures for applying D-SPIN to build program-level or gene-level network models from single-cell data. With a public immune dictionary dataset, we illustrate multiple applications of D-SPIN, including identifying gene programs and network modules, classifying perturbation responses of cytokines, and nominating key regulators that mediate cytokine responses.

bioinformatics↗

bioRxiv2025

Revealing a coherent cell state landscape across single cell datasets with CONCORD

Revealing the underlying cell-state landscape from single-cell data requires overcoming the critical obstacles of batch integration, denoising, and dimensionality reduction. Here, we present CONCORD, a unified framework that simultaneously addresses these challenges within a single self-supervised model. At its core, CONCORD implements a unified probabilistic sampling strategy that corrects batch effects via dataset-aware sampling and enhances biological resolution through hard-negative sampling. Remarkably, using only a minimalist neural network with a single hidden layer and contrastive learning, CONCORD surpasses state-of-the-art performance without relying on deep architectures, auxiliary losses, or external supervision. It seamlessly integrates data across batches, technologies, and even species to generate high-resolution cell atlases. The resulting latent representations are denoised and biologically meaningful--capturing gene co-expression programs, revealing detailed lineage trajectories, and preserving both local geometric relationships and global topological structures. We demonstrate CONCORDs broad applicability across diverse datasets, establishing it as a general-purpose framework for learning unified, high-fidelity representations of cellular identity and dynamics.

bioinformatics↗

bioRxiv2025

A cellular solution to a robotics problem

Seeking a signal source in unstructured environments is a fundamental challenge in robotics. Similarly, cells in tissues track signal sources using noisy, fragmented molecular gradients, shaped by fluid flow and extracellular matrix interactions. However, the precise algorithm cells use for source seeking is unknown. We show that cells can perform source seeking using a biophysical implementation of a computational algorithm called Bayes filtering. Specifically, the spatial distribution of molecules within the cell encodes a probability distribution over source location, and intracellular transport processes update this distribution. Live-cell imaging and spatial proteomics reveal that receptor dynamics in vivo matches the evolution of belief distributions under Bayes filtering. Unlike standard Bayes filtering, the cellular implementation adapts to fluctuating measurement noise without explicitly estimating noise statistics. When translated to traditional robotics algorithms, this cell-inspired adaptation enables robust navigation without continuously estimating signal statistics. Our results show that cells can leverage spatial organization to implement probabilistic algorithms, bridging cellular behavior and engineered systems.

systems biology↗

bioRxiv2025

Designing biochemical circuits with tree search

Discovering biochemical circuits that exhibit a desired behavior is an outstanding problem in biological engineering. The traditional approach of enumerating every possible circuit topology becomes intractable for circuits with more than four components due to combinatorial scaling of the search space. Here, we use Monte Carlo Tree Search (MCTS), a reinforcement learning (RL) algorithm, to optimize circuit topology for a target phenotype by approaching circuit design as a sequence of assembly decisions. Our RL-based design framework, which we call CircuiTree, efficiently and comprehensively finds robust designs for three-component oscillators by prioritizing sparsity. CircuiTree can also infer candidate network motifs from its search results, producing similar results to enumeration. Using parallel MCTS, we scale this workflow up to five components and find that highly fault-tolerant designs use a novel strategy, which we call motif multiplexing. Multiplexed circuits contain many overlapping network motifs that each activate in different mutational scenarios, in one case lending robustness to four out of five single-gene deletions. Overall, CircuiTree provides the first scalable computational platform for designing biochemical circuits.

systems biology↗

bioRxiv2025

Building Foundation Models to Characterize Cellular Interactions via Geometric Self-Supervised Learning on Spatial Genomics

Cellular interactions form the fundamental/core circuits that drive development, physiology, and disease within tissues. Advances in spatial genomics (SG) and artificial intelligence (AI) offer unprecedented opportunities to computationally analyze and predict the behavior of cell intricate networks, and to identify interactions that drive disease states. However, challenges arise in both methodology and scalability: (i) how to computationally characterize complicated cellular interactions of multi-scale nature where chemical genes/circuits in individual cells process information and drive interactions among large numbers of diverse cell types, and (ii) how to scale up the pipeline to accommodate the increasing volumes of SG data that map transcriptome-scale gene expression and spatial proximity across millions of cells. In this paper, we introduce the Cellular Interaction Foundation Model (CI-FM), an AI foundation model functioning to analyze and simulate cellular interactions within living tissues. In the CI-FM pipeline, we explicitly capture and embed cellular interactions within microenvironments by leveraging the powerful and scalable geometric graph neural network model, and optimize the characterization of cellular interactions with a novel self-supervised learning objective - we train it to infer gene expressions of cells based upon their interacting microenvironment. As a result, we construct CI-FM with 100 million parameters by consuming SG data of 23 million cells. Our benchmarking experiments show CI-FM effectively infers gene expressions conditional on the microenvironmental contexts: we achieve a high correlation and a low mismatch error (MSE of 1.1% relative to the square median expression), with 79.4% of cells on average being annotated as the similar cell type based on their predicted and actual expressions. We demonstrate the downstream utility of CI-FM by: (i) applying CI-FM to embed tumor samples to capture cellular interactions within tumor microenvironments (ROC-AUC score of 0.76 on classifying sample conditions via linear probing on embeddings), and identifying shared signatures across samples; and (ii) using CI-FM to simulate changes in microenvironmental composition in response to T cell infiltration, which highlights how CI-FM can be leveraged to model cellular responses to tissue perturbations - an essential step toward constructing "AI virtual tissues". Our model is open source and publicly accessible at https://huggingface.co/ynyou/CIFM.

bioinformatics↗

bioRxiv2024

Protein CREATE enables closed-loop design of de novo synthetic protein binders

Proteins have proven to be useful agents in a variety of fields, from serving as potent therapeutics to enabling complex catalysis for chemical manufacture. However, they remain difficult to design and are instead typically selected for using extensive screens or directed evolution. Recent developments in protein large language models have enabled fast generation of diverse protein sequences in unexplored regions of protein space predicted to fold into varied structures, bind relevant targets, and catalyze novel reactions. Nevertheless, we lack methods to characterize these proteins experimentally at scale and update generative models based on those results. We describe Protein CREATE (Computational Redesign via an Experiment-Augmented Training Engine), an integrated computational and experimental pipeline that incorporates an experimental workflow leveraging next generation sequencing and phage display with single-molecule readouts to collect vast amounts of quantitative binding data for updating protein large language models. We use Protein CREATE to generate and assay thousands of designed binders to IL-7 receptor and insulin receptor with parallel positive and negative selections to identify on-target binders. We discover not only individual novel binders but also features of ligand-receptor binding, including preservation of the IL7R - ligand hydrophobic interface specifically and existence of multiple approaches to contact the insulin receptor. We also demonstrate the importance of structural features, such as the lack of unpaired cysteine residues, toward design fidelity and find computational pre-screening metrics, such as interchain predicted TM scoring (iPTM), while useful, are imperfect predictors as they neither guarantee experimental binding nor rule it out. We use the data collected from Protein CREATE to score designs from the initial generative models. Globally, Protein CREATE will power future closed-loop design-build-test cycles to enable fine-grained design of protein binders.

bioengineering↗

bioRxiv2024

Design of Novel Dehalogenases using Protein Large Language Models

Per- and polyfluoroalkyl substances (PFAS) are toxic compounds linked to cancers, infertility, and vaccine resistance at concentrations above 1 part per trillion. Owing to strong carbon-fluorine bonds, they are persistent in the environment, taking centuries to millennia to degrade. As part of the 2024 iGEM competition, several high school students local to the Pasadena area leveraged recently-developed bioinformatic tools and large language models to discover and design novel reductive dehalogenases predicted to degrade perfluorooctanoic acid (PFOA), a long chain PFAS whose manufacture is prohibited, yet still persists in the environment and drinking water. The team identified 68 enzymes with structural similarity to Acidimicrobium sp. Strain A6 RdhA, the only specific known PFAS degrading enzyme in nature, expressed and refolded 5 of these enzymes in addition to 1 rational, and 3 large language model designs. These designs all have diverse sequences yet all are predicted to retain key substrate and cofactor binding pockets. These enzymes will be assayed on the ability to defluorinate PFOA.

bioengineering↗

bioRxiv2024

Benchmarking Protein Language Models for Protein Crystallization

The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for the predicting the crystallization propensities of proteins based on their sequences. In this work, we benchmark the power of open protein language models (PLMs) through the TRILL platform, a bespoke framework democratizing the usage of PLMs for the task of predicting crystallization propensities of proteins. By comparing LightGBM / XGBoost classifiers built on the embedding representations learned by different PLMs, such as ESM2, Ankh, ProtT5-XL, ProstT5, with the performance of state-of-the-art sequence-based methods like DeepCrystal, ATTCrys and CLPred, we identify the most effective methods for predicting crystallization outcomes. The LightGBM classifiers utilizing embeddings from ESM2 model with 30 and 36 transformer layers and 150 and 3, 000 million parameters respectively have performance gains by 3 - 5% then all compared models for various evaluation metrics, including AUPR (Area Under Precision-Recall Curve), AUC (Area Under the Receiver Operating Characteristic Curve), and F1 score on independent test sets. Furthermore, we fine-tune the ProtGPT2 model available via TRILL to generate crystallizable proteins. Starting with 3, 000 generated proteins and through a step of filtration processes including consensus of all open PLM-based classifiers, sequence identity through CD-HIT, secondary structure compatibility, aggregation screening, homology search and foldability evaluation, we identified a set of 5 novel proteins as potentially crystallizable.

bioinformatics↗

bioRxiv2024

Motor-driven microtubule diffusion in a photobleached dynamical coordinate system

Motor-driven cytoskeletal remodeling in cellular systems can often be accompanied by a diffusive-like effect at local scales, but distinguishing the contributions of the ordering process, such as active contraction of a network, from this active diffusion is difficult to achieve. Using light-dimerizable kinesin motors to spatially control the formation and contraction of a microtubule network, we deliberately photobleach a grid pattern onto the filament network serving as a transient and dynamic coordinate system to observe the deformation and translation of the remaining fluorescent squares of microtubules. We find that the network contracts at a rate set by motor speed but is accompanied by a diffusive-like spread throughout the bulk of the contracting network with effective diffusion constant two orders of magnitude lower than that for a freely-diffusing microtubule. We further find that on micron scales, the diffusive timescale is only a factor of {approx} 3 slower than that of advection regardless of conditions, showing that the global contraction and long-time relaxation from this diffusive behavior are both motor-driven but exhibit local competition within the network bulk. O_TEXTBOXSignificance StatementCytoskeletal reorganization can come at the cost of local diffusive-like disordering effects from the same active elements, but distinguishing these processes can be challenging. By photobleaching an actively contracting microtubule network, we show that the bulk redistribution of filaments exhibit a diffusion-like reorganization that can be tuned by the effective motor speed. By tuning these parameters, we show a conserved relationship between active contraction rates and effective diffusion constants, suggesting that while advection of the cytoskeletal network dominates over scales of tens to hundreds of microns, motors additionally induce a diffusive-like effect that begins to compete with advection at micron scales. C_TEXTBOX

biophysics↗