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bioRxiv2025

CASPULE: A computational tool to study sticker spacer polymer condensates

Phase separated condensates are recognized as a ubiquitous mechanism of spatial organization in cell biology. Biophysical modeling of condensates provides critical insights into the dynamics and functions of these subcellular structures that are difficult to extract via experiments. Here we present an efficient computational pipeline, CASPULE (Condensate Analysis of Sticker Spacer Polymers Using the LAMMPS Engine), to simulate and analyze the biological condensates made of sticker-spacer polymers. CASPULE implements a unique force field that combines traditional Langevin dynamics with a "detailed-balance proof" protocol for single-valent bond formation between stickers. This framework allows us to study the nontrivial biophysics that emerge from singlevalent sticker interactions, coupled with the effect of separating the energetic contributions of stickers and spacers. We provide detailed documentation on how to setup the simulation environment, perform simulations and analyze the results. Through case studies, we highlight the utility and efficacy of our pipeline. Importantly, we provide statistical parameters to characterize the cluster size distribution often observed in biological systems. We envision this tool to be broadly useful in decoding the interplay of kinetics and thermodynamics underlying the formation and function of biological condensates. Code and documentation: https://caspule.github.io/caspule/ Author SummaryLiving cells contain droplet-like compartments called biomolecular condensates that organize important molecules and reactions without a surrounding membrane. Understanding how these condensates form and behave helps us learn more about healthy cellular function and diseases. Condensate forming biopolymers (proteins and nucleic acids) often contain a sticker spacer architecture that gives rise to interesting emergent properties when they assemble into a condensate. Here we report an easy-to-use computational pipeline, called CASPULE, that allows us to simulate and analyze the condensation of polymer chains. Unlike previous tools, CASPULE models both the physical motion of these molecules and specific bonding interactions between sticker segments. This user-friendly tool will enable researchers to explore the governing principles underlying the formation and function of biomolecular condensates.

biophysics↗

bioRxiv2026

Task-adapted biological foundation models uncover perturbation-centric representations

Foundation models have emerged as powerful tools for learning transferable representations of biological systems, yet their latent spaces are typically optimized to capture cellular state rather than the effects of perturbations. Here, we demonstrate that a biological foundation model can be repurposed to learn a fundamentally different representation by changing its learning objective. We fine-tuned scGPT, a transformer pre-trained on over 30 million single-cell transcriptomes, on more than three million LINCS L1000 perturbation profiles using a supervised objective that predicts perturbation identity. This transformed the latent space into a perturbation-centric representation that aligned transcriptional responses induced by the same chemical or genetic perturbation across heterogeneous experimental conditions. Fine-tuned embeddings substantially outperformed both gene expression profiles and the original pre-trained model, recovering 85-100% of perturbations within the top 100 nearest neighbors and increasing perturbation classification accuracy from 10-19% to 25-49%. Remarkably, although the model was trained exclusively to recognize perturbation identity, the learned representation spontaneously captured orthogonal biological relationships never provided during training, including chemical similarity (AUROC up to 0.81), mechanisms of action (Hit@10 up to 100%), compound-target relationships (AUROC up to 0.74), and functional relationships between genetic perturbations. The resulting embedding space enabled mechanism-of-action annotation of nearly 12,000 previously uncharacterized compounds, prioritization of target-related chemical-genetic associations, and contextualization of unseen perturbations and external transcriptomic datasets. Together, our results establish objective-driven adaptation as a general strategy for repurposing biological foundation models to learn reusable representations of complex biological phenomena.

bioinformatics↗

bioRxiv2026

Efficient growth factor production stabilizes cooperation in a cellular public goods game

Eukaryotic cells secrete growth factors that facilitate the proliferation and survival of neighboring cells in a coordinated manner essential for tissue development, repair, and homeostasis. However, mutant cells that fail to produce these factors may exploit the benefits provided by neighboring producers, thereby gaining a proliferative advantage that could lead to the extinction of the producer clone. Evolutionary theory predicts that preventing this "tragedy of the commons" requires a fixed spatial structure or other mechanisms that enable cells to interact preferentially with copies of themselves. Contrary to this expectation, we found that, in competition experiments, cells producing the diffusible growth factor interleukin-3 (IL3) resist invasion by non-producing mutant cells even in well-mixed populations, and that non-producers are driven to extinction instead. Using evolutionary game theory, we show that this outcome arises when the cost-to-benefit ratio of growth factor production falls below a critical threshold. We derive this tipping point analytically across the full parameter space of benefit function shape and group size and confirm that IL3 production in our experimental system satisfies this condition with a production cost less than 0.1% of the benefit conferred. These results demonstrate that efficient public good production alone can resolve the tragedy of the commons in populations of cells, and potentially in other biological systems.

evolutionary biology↗

bioRxiv2021

CASowary: CRISPR-Cas13 guide RNA predictor for transcript depletion

Recent discovery of the gene editing system -CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which further limit the scope of effective target sequences. As a result, there currently exists no method to predict whether a specific sgRNA will effectively knockdown a transcript. Here we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from Cas13 characterization experiments for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information on RNA. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 70%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting Cas system -CIRTS, confirming the robustness and reproducibility of our models sgRNA predictions. Utilizing transcriptome wide protein occupancy map generated using POP-seq in Hela cells against publicly available protein-RNA interaction map in Hek293 cells, we show that CASowary can predict high quality guides for numerous transcripts in a cell line specific manner. Application of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.

bioinformatics↗

bioRxiv2023

EMDiffuse: a diffusion-based deep learning method augmenting ultrastructural imaging and volume electron microscopy

Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse demonstrates outstanding denoising and super-resolution performance, generates realistic predictions without unwarranted smoothness, improves predictions resolution by [~]30%, and exhibits excellent transferability by taking only one pair of images to fine-tune. EMDiffuse also pioneers the isotropic vEM reconstruction task, generating isotropic volume similar to that obtained using advanced FIB-SEM even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from six public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate organelle reconstruction, making 3D nanoscale ultrastructure analysis faster and more accessible and extending such capability to larger volumes. More importantly, EMDiffuse features self-assessment functionalities and guarantees reliable predictions for all tasks. We envision EMDiffuse to pave the way for more in-depth investigations into the intricate subcellular nanoscale structures within large areas and volumes of biological systems.

cell biology↗

bioRxiv2024

Metabolite-responsive Control of Transcription by Phase Separation-based Synthetic Organelles

Living natural materials have remarkable sensing abilities that translate external cues into functional changes of the material. The reconstruction of such sensing materials in bottom-up synthetic biology provides the opportunity to develop synthetic materials with life-like sensing and adaptation ability. Key to such functions are material modules that translate specific input signals into a biomolecular response. Here, we engineer a synthetic organelle based on liquid-liquid phase separation that translates a metabolic signal into the regulation of gene transcription. To this aim, we engineer the pyruvate-dependent repressor PdhR to undergo liquid-liquid phase separation in vitro by fusion to intrinsically disordered regions. We demonstrate that the resulting coacervates bind DNA harbouring PdhR-responsive operator sites in a pyruvate dose-dependent and reversible manner. We observed that the activity of transcription units on the DNA was strongly attenuated following recruitment to the coacervates. However, the addition of pyruvate resulted in a reversible and dose-dependent reconstitution of transcriptional activity. The coacervate-based synthetic organelles linking metabolic cues to transcriptional signals represent a materials approach to confer stimulus-responsiveness to minimal bottom-up synthetic biological systems and open opportunities in materials for sensor applications.

synthetic biology↗

bioRxiv2025

A Quantitative Approach to Mapping Mitochondrial Specialization and Plasticity

Mitochondria are a diverse family of organelles with highly specialized functions. While they share common features, their molecular and functional diversity remains underexplored. Here, we introduce a quantitative pipeline to define the degree of molecular specialization among different mitochondrial phenotypes, or mitotypes. By distilling hundreds of validated mitochondrial genes into 149 biologically interpretable MitoPathway scores, this streamlined mitotyping framework enables investigators to quantify and interpret mitochondrial diversity and plasticity from transcriptomic data across a variety of natural and experimental contexts. Using this approach, we show that mouse and human multi-organ mitotypes segregate along two main axes of mitochondrial specialization, characterize robust longitudinal and perturbation-induced metabolic plasticity in cultured human fibroblasts, and resolve cell-type and single-cell mitochondrial recalibrations across the human brain and sperm developmental trajectory. Together, this framework provides a practical and extensible approach for analyzing mitochondrial specialization in complex biological systems.

cell biology↗

bioRxiv2025

Harnessing Adaptive Chaos: Exploring the role of oxygen-ozone therapy in modulating complexity and repair in spine disorders via a bio-informatic model

Adaptive chaos represents a paradigm shift in understanding biological systems, where mild chaotic dynamics sustain homeostasis and resilience. This study delves into oxygen-ozone therapys ability to leverage adaptive chaos in treating spinal musculoskeletal disorders, particularly intervertebral disc degeneration (IVDD). Employing mathematical modelling and bioinformatic tools, the study evaluates chaos, complexity, and entropy across varying ozone doses to identify optimal therapeutic windows within a hormetic range. The results reveal that lower ozone doses (20-40 {micro}g/ml O3) promote adaptive chaos, enhancing systemic complexity, reducing inflammatory cytokines, and fostering structural repair. Conversely, higher doses (60-80 {micro}g/ml O3) induce excessive oxidative stress, exacerbating pathological chaos and impairing recovery. The integration of Shannon entropy, Lyapunov exponents, and fractal dimensions provides a novel framework for quantifying therapeutic outcomes. Findings highlight the role of controlled perturbations in stabilizing pathological systems, offering a dose-dependent roadmap for balancing flexibility and order. By correlating modelled insights with clinical evidence, the study underscores the significance of precision dosing, as the optimal range facilitates pain reduction and inflammatory control, correlating with improved complexity and reduced turbulence. This research pioneers the concept of adaptive chaos in medical interventions, emphasizing its translational potential for personalized and systemic medicine. Beyond IVDD, the findings establish a broader framework for adaptive chaos as a cornerstone of health restoration, advocating for therapies that optimize biological complexity. This work tries to bridge mathematical modelling and clinical evidence, offering a cutting-edge perspective on regenerative medicine and chaos-driven therapeutic strategies.

bioinformatics↗

bioRxiv2025

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Building predictive models of the cell requires systematically mapping how perturbations reshape each cells state, function, and behavior. Here, we present Tahoe-100M, a giga-scale single-cell atlas of 100 million transcriptomic profiles measuring how each of 1,100 small-molecule perturbations impact cells across 50 cancer cell lines. Our high-throughput Mosaic platform, composed of a highly diverse and optimally balanced "cell village", reduces batch effects and enables parallel profiling of thousands of conditions at single-cell resolution at an unprecedented scale. As the largest single-cell dataset to date, Tahoe-100M enables artificial-intelligence (AI)-driven models to learn context-dependent functions, capturing fundamental principles of gene regulation and network dynamics. Although we leverage cancer models and pharmacological compounds to create this resource, Tahoe-100M is fundamentally designed as a broadly applicable perturbation atlas and supports deeper insights into cell biology across multiple tissues and contexts. By publicly releasing this atlas, we aim to accelerate the creation and development of robust AI frameworks for systems biology, ultimately improving our ability to predict and manipulate cellular behaviors across a wide range of applications.

genomics↗

bioRxiv2025

Electric field intensity modulates keratocyte migration without altering turning dynamics

Cell migration is a cornerstone of biological systems, enabling organisms to adapt to environmental stimuli and maintain homeostasis. Disruptions in this process can lead to functional impairment or system failure. In many cases, cells do not move randomly; instead, they migrate directionally in response to external cues, allowing them to perform essential biological functions. This directed movement is especially important in processes such as morphogenesis, cancer invasion, and wound healing. To unravel the complexities of directional cell migration, investigating natural guiding stimuli is crucial. Among these, electrical fields stand out as precise and physiologically relevant stimulus. Using a platform designed to apply programmable electric fields, the SCHEEPDOG device; we applied controlled electric field of varying intensities to keratocytes and quantitatively analyzed their migratory behavior. Our findings reveal that electric field stimulation not only induces robust directional migration but also enhances migration speed in an intensity-dependent manner. Additionally, cells initially moving in random directions gradually align with the field vector, with higher intensities accelerating the alignment. Intriguingly, while both speed and alignment time can be modulated through stimulation, the overall shape of migration trajectories remains unchanged. In other terms, for cells initially moving to the opposite direction of the field, the alignment is accompanied by making a turn and the size and shape of this turn is not affected by the magnitude of the electrical stimulation. Together, these results demonstrate that electrical stimulation can tune the speed and directional alignment of keratocyte migration without altering turning dynamics. These findings contribute to a deeper understanding of electrotaxis and offers new insights into how biophysical cues regulate cell migration in both physiological and pathological contexts.

biophysics↗

bioRxiv2020

Root system influence on high dimensional leaf phenotypes over the grapevine growing season

O_LIIn many perennial crops, grafting the root system of one individual to the shoot system of another individual has become an integral part of propagation performed at industrial scales to enhance pest, disease, and stress tolerance and to regulate yield and vigor. Grafted plants offer important experimental systems for understanding the extent and seasonality of root system effects on shoot system biology. C_LIO_LIUsing an experimental vineyard where a common scion Chambourcin is growing ungrafted and grafted to three different rootstocks, we explore associations between root system genotype and leaf phenotypes in grafted grapevines across a growing season. We quantified five high-dimensional leaf phenotyping modalities: ionomics, metabolomics, transcriptomics, morphometrics, and physiology and show that rootstock influence is subtle but ubiquitous across modalities. C_LIO_LIWe find strong signatures of rootstock influence on the leaf ionome, with unique signatures detected at each phenological stage. Moreover, all phenotypes and patterns of phenotypic covariation were highly dynamic across the season. C_LIO_LIThese findings expand upon previously identified patterns to suggest that the influence of root system on shoot system phenotypes is complex and broad understanding necessitates volumes of high-dimensional, multi-scale data previously unmet. C_LI

plant biology↗

bioRxiv2019

A modular RNA interference system for gene regulation and robust dynamic feedback control

The rational design and realisation of simple-to-use genetic control elements that are modular, orthogonal and robust is essential to the construction of predictable and reliable biological systems of increasing complexity. To this effect, we introduce modular Artificial RNA interference (mARi), a rational, modular and extensible design framework that enables robust, portable and multiplexed post-transcriptional regulation of gene expression in Escherichia coli. The regulatory function of mARi was characterised in a range of relevant genetic contexts, demonstrating its independence from other genetic control elements and the gene of interest, and providing new insight into the design rules of RNA based regulation in E. coli, while a range of cellular contexts also demonstrated it to be independent of growth-phase and strain type. Importantly, the extensibility and orthogonality of mARi enables the simultaneous post-transcriptional regulation of multi-gene systems as both single-gene cassettes and poly-cistronic operons. To facilitate adoption, mARi was designed to be directly integrated into the modular BASIC DNA assembly framework. We anticipate that mARi-based genetic control within an extensible DNA assembly framework will facilitate metabolic engineering, layered genetic control, and advanced genetic circuit applications.

synthetic biology↗

bioRxiv2018

Embryo timelapses can be compiled and quantified to understand canonical histone dynamics across multiple cell cycles.

In the last decade, computational analysis of big datasets has facilitated the processing of unprecedented quantities of collected biological data. Thus, automations and big data analyses have been revolutionary in detecting and quantifying subtle phenotypes in cell biological contexts. Analyzing similar quantities of data in larger and more complicated biological systems such as live embryos has been more challenging due to experimental necessities impeding both compilations of data collection and informative analysis. Here we present a streamlined workflow that can quantify cell cycle dynamics in early developing embryos using fluorescently labeled proteins. We benchmark this pipeline using Caenorhabditis elegans (nematode) embryonic development and a fluorescently labeled histone. Using our pipeline, we find that histone proteins are broadly stable in early embryonic development. In sum, we have utilized the large biological and experimental variation associated with quantification of fluorescent proteins in embryonic systems, to quantify nuclear accumulation rate, chromatin incorporation, and turnover/stability of canonical histones during early development.

developmental biology↗

bioRxiv2022

Inherent evolutionary unpredictability in cancer model system

Despite the advent of personalized medicine, it is still difficult to predict how a cancer develops over time at the level of the individual patient or even in cancer model systems which begs the question whether certain aspects of cancer can ever be predicted or if there is an inherent unpredictability in cancer, similar to other complex biological systems, We demonstrate by a combination of agent-based mathematical modelling and analysis of data from patient-derived xenograft systems from multiple cancer types that certain conditions may invoke chaotic fluctuations in the clonal landscape of cancer cells. Our findings indicate that under those conditions, the cancer genome behaves as a complex dynamic system, making its long-term evolution inherently unpredictable.

evolutionary biology↗

bioRxiv2020

Characterization and safety profile of Imuno TF(R), a nutritional supplement for immune system regulation containing transfer factors

Imuno TF(R) is a nutritional supplement composed of isolated transfer factors (TF) from porcine spleen. It is composed of a specific mixture of molecules that impact functions of the biological systems, and historically is linked to the immune system regulation. In this study, we demonstrate for the first time its proteomic analysis, nutritional composition, and safety profile in terms of mutagenic potential and acute oral dose (LD50). The obtained analysis indicated the product is a complex set of oligo- and polypeptides constituted of 163 different peptides which can potentially act on multiple mechanisms on the immune system pathways. The chemical composition showed low fat and low sugar content, saturated fatty acids-free and the presence of 10 vitamins and 11 minerals. No mutagenic effect was observed, and the LD50 was 5,000 mg kg-1 body weight. This accounts for a safe product to be used by oral route, with potential benefits for the immune system.

immunology↗

bioRxiv2024

Leveraging Transfer Learning for Predicting Protein-Small Molecule Interactions

A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: [~]104 proteins in a typical human cell offer a plausible 108 interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete datasets (103-104 protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small datasets. Yuel 2 is pre-trained on a large-scale dataset to learn intricate structural features and then fine-tuned on specialized datasets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics - Kd, Ki, and IC50 - between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.

bioinformatics↗

bioRxiv2018

Linear-double-stranded DNA (ldsDNA) based AND logic computation in mammalian cells

Synthetic biology employs engineering principles to redesign biological system for clinical or industrial purposes. The development and application of novel genetic devices for genetic circuits construction will facilitate the rapid development of synthetic biology. Here we demonstrate that mammalian cells could perform two- and three-input linear-double-stranded DNA (ldsDNA) based Boolean AND logic computation. Through hydrodynamic ldsDNA delivery, two-input ldsDNA-base AND-gate computation could be achieved in vivo. Inhibition of DNA-PKcs expression, a key enzyme in non-homologous end joining (NHEJ), could significantly downregulate the intensity of output signals from ldsDNA-based AND-gate. We further reveal that in mammalian cells ldsDNAs could undergo end processing and then perform AND-gate calculation to generate in-frame output proteins. Moreover, we show that ldsDNAs or plasmids with identical overlapping sequences could also serve as inputs of AND-gate computation. Our work establishes novel genetic devices and principles for genetic circuits construction, thus may open a new gate for the development of new disease targeting strategies and new protein genesis methodologies.

synthetic biology↗

bioRxiv2018

Quantitative network organization of interactions emerging from the evolution of sequence and structure space of antibodies: the RADARS model

Adaptive immunity in vertebrates represents a complex self-organizing network of protein interactions that develops throughout the lifetime of an individual. While deep sequencing of the immune-receptor repertoire may reveal clonal relationships, functional interpretation of such data is hampered by the inherent limitations of converting sequence to structure to function. In this paper a novel model of antibody interaction space and network, termed radial adjustment of system resolution, RADARS, is proposed. The model is based on the radial growth of interaction affinity of antibodies towards an infinity of directions in structure space, each direction representing particular shapes of antigen epitopes. Levels of interaction affinity appear as free energy shells of the system, where hierarchical B-cell development and differentiation takes place. Equilibrium in this immunological thermodynamic system can be described by a power-law distribution of antibody free energies with an ideal network degree exponent of phi square, representing a scale-free fractal network of antibody interactions. Plasma cells are network hubs, memory B cells are nodes with intermediate degrees and B1 cells represent nodes with minimal degree. Thus, the RADARS model implies that antibody structure space develops against an infinite antigen structure space via interactions that are individually immunologically controlled, but on a systems level are organized by thermodynamic probability distributions. The network of interactions, which control B-cell development and differentiation, represent pathways of antigen removal on systems level. Understanding such quantitative network properties of the system should help the organization of sequence-derived structural data, offering the possibility to relate sequence to function in a complex, self-organizing biological system. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/438804v7_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@883eb7org.highwire.dtl.DTLVardef@cd7d5forg.highwire.dtl.DTLVardef@e225eeorg.highwire.dtl.DTLVardef@1286b49_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

immunology↗