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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Zone Equalisation Normalisation For Improved Alignment of Epigenetic Signal

High-throughput genomic technologies have transformed our understanding of biological systems, but analysing these complex datasets remains challenging. Existing normalisation methods often struggle to accurately correct for technical variations across different experimental conditions, potentially leading to misleading biological interpretations. We introduce Zone Equalisation Normalisation (ZEN), a novel approach designed to improve signal normalisation across genomic datasets. By developing a flexible method that reduces technical noise whilst preserving biological signals, ZEN addresses critical limitations in current normalisation techniques. By applying ZEN to diverse genomic techniques, we reveal its robust performance in managing technical variations across different experimental platforms. ZEN provides researchers with a robust tool for more accurate and reliable genomic data analysis, potentially revealing biological insights that were previously obscured by technical variations.

bioinformatics↗
bioRxiv2025

A multilevel hierarchical framework for quantification of experimental heterogeneity

Biological systems exhibit substantial heterogeneity: that is, variation in specific characteristics of individuals within a population. As a result, it is of critical importance to appropriately account for biological heterogeneity when calibrating mathematical models to infer cellular processes and predict behaviour. Recent approaches consider ordinary differential equations with random parameters to quantify heterogeneity in dynamical processes of cells. In this setting, statistical inference is performed to characterise the distribution of these random parameters within a cell population. One significant limitation of this approach is the tacit assumption that there are no substantial deviations in these distributions across experimental replicates. In this work, we propose a flexible Bayesian hierarchical differential equation modelling framework that quantifies and distinguishes both inter-experimental heterogeneity (heterogeneity between experimental replicates) and intra-experimental heterogeneity (biological heterogeneity within replicate populations). We consider two recent studies that employ mathematical models to interpret flow cytometry snap-shot data and quantify heterogeneity in nano-particle cell interactions and cell internalisation processes. Using simulation data, we demonstrate that substantial inaccuracy in the inferred dynamics can arise when experimental heterogeneity is not accounted for. By contrast, our hierarchical approach is robust to variability in inter-experimental and intra-experimental heterogeneity and our method simplifies to previous methods when inter-experimental heterogeneity is negligible. Our approach is flexible and widely applicable to applications involving replicate populations and snapshot data.

cell biology↗
bioRxiv2026

Interpretable decoding of cell fate from a snapshot of combinatorial signaling

How combinatorial cell signaling controls cellular decisions in the face of crosstalk is a fundamental problem in biology. A key open question is whether a single snapshot of signaling is sufficient to predict cell fate, especially given substantial evidence that signaling dynamics shape fate decisions. Here, we show that a snapshot of combinatorial signaling accurately predicts cell fate at the single-cell level in a model for human embryonic patterning. To this end, we developed Sig2Fate, a quantitative method integrating iterative immunofluorescence, information theory, and machine learning. Cell fate is encoded by combinatorial yet redundant signaling that reduces to a single angular coordinate in the high-dimensional signaling space, providing a simple interpretation of the signal-to-fate map. This map generalizes across variations in BMP concentration and pharmacological perturbations of ERK, Wnt, and YAP signaling, enabling prediction of drug responses from control data alone when signaling crosstalk is accounted for. Our findings provide a framework for predicting and explaining complex phenotypes from signaling perturbations across biological systems.

developmental biology↗
bioRxiv2026

Tension TRAAKer: a chemigenetic fluorescent membrane tension reporter

Mechanical force transduction is essential to survival, underlying biological processes as fundamental as morphogenesis, somatosensation, audition, and interoception; and driving pathologies as diverse as hypertension and cancer metastasis. Exogenous forces are translated to intracellular signals through transient changes in membrane tension which are currently not possible to directly monitor in situ. To remedy this, we have designed and validated Tension TRAAKer, a chemigenetic fluorescent membrane tension reporter for the visualization of tension induction, propagation, and dissipation in living cells. Tension TRAAKer is derived from inserting a tension-sensitive nonconductive variant of the mechanosensitive potassium ion channel TRAAK into a self-labelling HaloTag. Increasing membrane tensions effect conformational changes in the TRAAK channel that are optically monitored by a HaloTag-conjugated fluorogenic (environment-sensitive) dye. EGFP incorporation C-terminal to the HaloTag enables unambiguous tension reporting in mobile membranes via dual-color ratiometric imaging that controls for variations in sensor density. Tension TRAAKer reports membrane tension changes rapidly, reversibly, and with spatiotemporal precision--its fluorescence scaling to both stimulus magnitude and area, with consistent effect sizes observed between diverse cell types. It better distinguishes among elevated membrane tensions than do available indirect chemical reporters, with the additional advantage of being readily genetically targetable. We thus expect Tension TRAAKer to be a powerful tool for the study of membrane tension across biological systems and disease states.

molecular biology↗
bioRxiv2026

Slow Dynamics Differentially Determine the Robustness of Regular Pacemaking: Distinct Subpopulations of Midbrain Dopamine Neurons Illustrate the Principle

Pacemaking neurons, often found in mammalian nervous systems, integrate their inputs differently than quiescent neurons. Rhythmic single-spike pacemaking that is robust to noise can be achieved with a slow process that enforces a "resting potential" at each point along a ramp-like interspike interval (ISI) coupled with a fast restorative component. To demonstrate this phenomenon, we modeled previously identified distinct subpopulations of midbrain dopamine neurons that differed in projection target and in the regularity of their pacemaking. In the model of the more regularly-firing subpopulation projecting to the dorsomedial striatum, KV4 current was recruited by a deep after-hyperpolarizing potential (AHP) mediated by the SK channel. In the model of the less regularly-firing subpopulation projecting to the medial shell of the nucleus accumbens, the AHP was too shallow to recruit the KV4 current. In the more regularly firing population, the trajectory in the phase space of membrane potential and slow inactivation of KV4 was confined to move slowly through a narrow channel during the ramp-like portion of the ISI. Noisy perturbations from this channel were quickly damped by fast activation of KV4. In contrast, the smaller AHP in the model of the subpopulation projecting to the medial shell of the nucleus accumbens failed to recruit Kv4-mediated current, therefore the narrow channel was never entered, greatly decreasing the regularity in the presence of noise. This mechanism may be broadly applicable to single-spike pacemakers and explains how slow pacemaking with small net currents can be robust to fluctuations in single channel openings. Author SummaryPacemaking cells spike at regular intervals without the need for external input. There are numerous examples of pacemaking cells in the nervous system. We show that a process with slow dynamics relative to the individual spikes can make regular pacemaking robust to the noise that is always present in biological systems.

neuroscience↗
bioRxiv2026

A biological-response compound representation allows chemical perturbation prediction across cell lines

Accurately predicting cellular responses to drugs remains a challenge with the potential to reduce experimental screening costs and accelerate drug discovery. Current computational approaches represent compounds through chemical structures, which carry little information regarding their activity within biological systems. We show that gene expression responses, measured in a reference cell line, can instead serve as transferable representations of chemical perturbations. The proposed framework, BioPert, uses biological representations with a small neural network to predict transcriptional delta responses in cell lines of different lineages. Across the Tahoe-100M and LINCS L1000 datasets, BioPert outperforms molecular fingerprints and embeddings learned from chemical structure, which provide limited improvements over random controls. BioPert's prediction correlations reach 0.79 on Tahoe-100M, corresponding to an improvement of 0.34 over the next-best representation. On LINCS, performance varies with the experimental reproducibility of the test conditions, highlighting the impact of batch effects. Notably, BioPert considerably surpasses predictions obtained by copying the reference response, demonstrating that it captures context-dependent effects. Further analysis of a C32-cobimetinib case shows pathway-level accuracy of the predictions, including when the reference and target responses differ. These results open a new path for chemical perturbation prediction and could ultimately reduce the burden of phenotypic screening.

bioinformatics↗
bioRxiv2016

Quantitative Modeling and Automated Analysis of Meiotic Recombination

Many morphological features, in both physical and biological systems, exhibit spatial patterns that are specifically characterized by a tendency to occur with even spacing (in one, two or three dimensions). The positions of crossover (CO) recombination events along meiotic chromosomes provides an interesting biological example of such an effect (1-3). In general, mechanisms that explain such patterns may (a) be mechanically-based, (b) occur by a reaction-diffusion mechanism in which macroscopic mechanical effects are irrelevant, or (c) involve a combination of both types of effects. We have proposed that meiotic CO patterns arise by a mechanical mechanism, have developed mathematical expressions for such a process based on a particular physical system with analogous properties (the so-called \"beam-film model\"), and have shown that the beam-film model can very accurately explain experimental CO patterns as a function of the values of specific defined parameters (4-7). Importantly, the mathematical expressions of the beam-film model can apply quite generally to any mechanism, whether it involves mechanical components or not, as long as its logic and component features correspond to those of the beam-film system (3; below). Furthermore, via its various parameters, the beam-film model discretizes the patterning process into specific components. Thus, the model can be used to explore the theoretically predicted effects of various types of changes in the patterning process. Such predictions can expand detailed understanding of the bases for various biological effects (e.g. 2, 5). We present here a new MATLAB program that implements the mathematical expressions of the beam-film model with increased robustness and accessibility as compared to programs presented previously. As in previous versions, the presented program permits both: (i) simulation of predicted CO positions along chromosomes of a test population; and (ii) easy analysis of CO positions, both for experimental data sets and for data sets resulting from simulations. The goal of the current presentation is to make these approaches more readily accessible to a wider audience of researchers. Also, the program is easily modified, and we encourage interested users to make changes to suit their specific needs. A link to the program is available on the Kleckner laboratory web site: http://projects.iq.harvard.edu/kleckner_lab.

genetics↗
bioRxiv2015

Replication is Recursion; or, Lambda: the Biological Imperative

There is a striking visual symmetry between the 'paradoxical combinator' (which implements recursion in the mathematical theory of computation) and the biological replication fork (which implements reproduction in the living cell). Can this mean anything? Living organisms are information processors - the genome encodes instructions for the process of life much as computer programs encode instructions for a computer. Replication in biological systems is intuitively similar to recursion in computational systems. The following discussion will present enough of the mathematics to allow biologists to make sense of the symmetries in the logo. Mathematicians will learn nothing new about biology, but may be encouraged to look at biological processes from a new perspective.\n\nComputational biology: a flight of fancy Computational biology: a flight... Mathematics of computation Semant ...

Bioinformatics↗
bioRxiv2020

Visibility Graph Based Community Detection for Biological Time Series

MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks. ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns. AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984. Contactgmias@msu.edu

bioinformatics↗
bioRxiv2020

Effects of individual base-pairs on in vivo target search and destruction kinetics of small RNA

Base-pairing interactions mediate intermolecular target recognition in many biological systems and applications, including DNA repair, CRISPR, microRNA, small RNA (sRNA) and antisense oligo therapies. Even a single base-pair mismatch can cause a substantial difference in biological activity but presently we do not yet know how the target search kinetics in vivo are influenced by single nucleotide level changes. Here, we used high-throughput sequencing to identify functionally relevant single point mutants of the bacterial sRNA, SgrS, and quantitative super-resolution microscopy to probe the mutational impact on the regulation of its primary target, ptsG mRNA. Our super-resolution imaging and analysis platform allowed us to further dissect mutational effects on SgrS lifetimes, and even subtle changes in the in vivo rates of target association, kon, and dissociation, koff. Mutations that disrupt binding of a chaperone protein, Hfq, and are distal to the mRNA annealing region still decreased kon and increased koff, providing an in vivo demonstration that Hfq directly facilitates sRNA-mRNA annealing. Single base-pair mismatches in the annealing region reduced kon by 24-31% and increased koff by 14-25%, extending the time it takes to find and destroy the target mRNA by about a third, depending on whether an AU or GC base-pair is disrupted. The effects of disrupting contiguous base-pairing are much more modest than that expected from thermodynamics, suggesting that Hfq also buffers base-pair disruptions.

molecular biology↗
bioRxiv2021

Learning the rules of cell competition without prior scientific knowledge

AO_SCPLOWBSTRACTC_SCPLOWDeep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning model capable of learning the rules of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue and during which cell fate is thought to be determined by the local cellular neighborhood over time. To investigate this, we developed a new approach ({tau}-VAE) by coupling a probabilistic encoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the{tau} -VAEs latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate - a conclusion that is in agreement with our current understanding from over a decade of scientific research. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network that, using the predictions of the{tau} -VAE, can identify conditions which deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening.

cell biology↗
bioRxiv2022

spARC Recovers Human Glioma Spatial SignalingNetworks with Graph Filtering

Biological networks operate within architectural frameworks that influence the state and function of cells through niche-specific factors such as exposure to nutrients and metabolites, soluble signaling molecules, and direct cognate cell-cell communication. Spatial omics technologies incorporate environmental information into the study of biological systems, where the spatial coordinates of cells may directly or indirectly encode these micro-anatomical features. However, they suffer from technical artifacts, such as dropout, that impede biological discovery. Current methods that attempt to correct for this fail to adequately integrate highly informative spatial information when recovering gene expression and modelling cell-cell dynamics in situ. To address this oversight, we developed spatial Affinity-graph Recovery of Counts (spARC), a data diffusion-based filtration method that shares information between neighboring cells in tissue and related cells in expression space, to recover gene dynamics and simulate signalling interactions in spatial transcriptomics data. Following validation, we applied spARC to 10 IDH-mutant surgically resected human gliomas across WHO grades II-IV in order to study signaling networks across disease progression. This analysis revealed co-expressed genes that border the interface between tumor and tumor-infiltrated brain, allowing us to characterize global and local structure of glioma. By simulating paracrine signaling in silico, we identified an Osteopontin-CD44 interaction enriched in grade IV relative to grade II and grade III astrocytomas, and validated the clinical relevance of this signaling axis using TCGA.

bioinformatics↗
bioRxiv2024

Harnessing full-text publications for deep insights into C. elegans and Drosophila connectomes

In the rapidly expanding domain of scientific research, tracking and synthesizing information from the rapidly increasing volume of publications pose significant challenges. To address this, we introduce a novel high-throughput pipeline that employs ChatGPT to systematically extract and analyze connectivity information from the full-texts and abstracts of 24,237 and 150,538 research publications concerning Caenorhabditis elegans and Drosophila melanogaster, respectively. This approach has effectively identified 200,219 and 1,194,587 interactions within the C. elegans and Drosophila connectomes, respectively. Utilizing Cytoscape Web, we have developed comprehensive, searchable online connectomes that link relevant keywords to their corresponding PubMed IDs, thus providing seamless access to an extensive knowledge network encompassing C. elegans and Drosophila. Our work highlights the transformative potential of integrating artificial intelligence with bioinformatics to deepen our understanding of complex biological systems. By revealing the intricate web of relationships among key entities in C. elegans and Drosophila, we offer invaluable insights that promise to propel advancements in genetics, developmental biology, neuroscience, longevity, and beyond. We also provide details and discuss significant nodes within both connectomes, including the insulin/IGF-1 signaling (IIS) and the notch pathways. Our innovative methodology sets a robust foundation for future research aimed at unravelling complex biological networks across diverse organisms.

bioinformatics↗
bioRxiv2025

Regulation Flow Analysis discovers molecular mechanisms of action from large knowledge databases

Drug development is a long and expensive process, with only a small fraction of potential drugs being finally approved. The challenge of drug development is rooted in our limited understanding of biological systems and the disease processes that drugs are trying to modulate. We propose a novel method, called Regulation Flow Analysis (RFA), which is based on the principles of biological regulation, causal graphs, and graph flow. RFA is able to generate causal hypotheses of mechanism of action, using large Knowledge Graphs (KG) of molecular regulation. Our numerical experiments demonstrate that the generated hypotheses, in the form of regulation pathways, often summarize mechanisms of drug action in a manner both understandable and actionable. Thus, RFA can greatly improve our understanding of the biological processes underlying health and disease, and therefore substantially facilitate drug development. Our examples illustrate how RFA recovers known mechanisms of action and can be used for target and biomarker discovery and validation.

bioinformatics↗
bioRxiv2023

Large language models are universal biomedical simulators

Computational simulation of biological processes can be a valuable tool in accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Recently, large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks by generating human language at a very large scale. Here we explore the potential of leveraging LLMs as simulators of biological systems. We establish proof-of-concept of a text-based simulator, SimulateGPT, that uses LLM reasoning. We demonstrate good prediction performance for various biomedical applications, without requiring explicit domain knowledge or manual tuning. LLMs thus enable a new class of versatile and broadly applicable biological simulators. This text-based simulation paradigm is well-suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulation, but for which extensive knowledge and context is available as written text.

bioinformatics↗
bioRxiv2026

The bromodomain protein Bdf3 is sufficient to activate expression of a procyclin gene in bloodstream stage African trypanosomes

The early branching eukaryotic parasite, Trypanosoma brucei, lives extracellularly in the mammalian host, where it expresses variant surface glycoprotein (VSG). Antigenic variation of VSGs allows the parasite to evade the host immune response while in the bloodstream. Upon transition to the tsetse fly vector, the parasite remodels its surface to express procyclin protein from a limited set of EP and GPEET genes. While the environmental signals that trigger the switch from expressing VSG genes to expressing EP and GPEET genes are known, the molecular mechanism that initiates transcription of the EP and GPEET genes during parasite differentiation to the procyclic stage within the tsetse is not well understood. Previous work has shown that the chromatin interacting bromodomain protein Bdf3 is absent from the pol I promoter of the EP locus in bloodstream parasites, but appears there shortly after initiation of differentiation from the bloodstream to the procyclic stage. Here, we show that tethering of Bdf3 to the EP promoter using a dCas9-Bdf3 fusion protein and a guide RNA is sufficient to increase transcript levels of EP1 in bloodstream parasites, where the gene is normally silenced. This result supports the model that Bdf3 appears at the EP locus during differentiation to facilitate initiation of transcription, which is consistent with the role for Bdf3 as a transcriptional activator in other systems. Understanding gene regulatory mechanisms in this early branching eukaryote may help build a more complete picture for the conserved and unique features of gene regulatory proteins across diverse biological systems. ImportanceThe Trypanosoma brucei parasite is transmitted to a mammalian host via the tsetse fly vector, where it causes African trypanosomiasis, a fatal disease that causes a severe human and economic burden for people living in in areas of sub-Saharan African. While drug treatments have improved for some strains, some infections still require treatment with drugs that have serious side effects. One avenue for drug development is to try and manipulate the lifecycle of the parasite to make it poorly adapted to the mammalian host. However, this requires detailed knowledge of the mechanisms by which parasites regulate their genes as they progress through the lifecycle. Here, we show that the DNA interacting bromodomain protein Bdf3 may be a key driver for turning on insect-specific genes that code for insect-stage surface proteins. This finding could aid in the development of lifecycle manipulating drugs, and shed light on how gene regulatory mechanisms evolved.

molecular biology↗
bioRxiv2021

Development and Characterization of PAR-Trackers: New Tools for Detecting Poly(ADP-ribose)

AO_SCPLOWBSTRACTC_SCPLOWADP-ribosylation (ADPRylation) is a reversible post-translation modification resulting in the covalent attachment of ADP-ribose (ADPR) moieties on substrate proteins. Naturally-occurring protein motifs and domains, including WWEs, PBZs, and macrodomains, act as "readers" for protein-linked ADPR. Although recombinant, antibody-like ADPR detection reagents containing these readers have facilitated the detection of ADPR, they are limited in their ability to capture the dynamic nature of ADPRylation. Herein, we describe and characterize a set of poly(ADP-ribose) (PAR) Trackers (PAR-Ts) - optimized dimerization-dependent or split-protein reassembly PAR sensors in which a naturally occurring PAR binding domain, WWE, was fused to both halves of dimerization-dependent GFP (ddGFP) or split Nano Luciferase (NanoLuc), respectively. We demonstrate that these new tools allow the detection and quantification of PAR levels in extracts, living cells, and living tissues with greater sensitivity, as well as temporal and spatial precision. Importantly, these sensors detect changes in cellular ADPR levels in response to physiological cues (e.g., hormone-dependent induction of adipogenesis without DNA damage), as well as xenograft tumor tissues in living mice. Our results indicate that PAR Trackers have broad utility for detecting ADPR in many different experimental and biological systems.

synthetic biology↗
bioRxiv2022

Evolutionarily conserved midbody reorganization precedes ring canal formation during gametogenesis

How canonical cytokinesis is altered during germ cell division to produce stable intercellular bridges called ring canals is poorly under-stood. Here, using time-lapse imaging in Drosophila, we observe that ring canal formation occurs via reorganization of the germline mid-body, a structure classically associated with its function in recruiting abscission-regulating proteins in complete cytokinesis. Germline midbodies reorganize from a focus into a ring rather than being discarded, and this transition is accompanied by changes in centralspindlin dynamics. The midbody-to-ring canal transformation is conserved in the Drosophila male and female germlines and during mouse and Hydra spermatogenesis. In Drosophila, ring canal formation depends on Citron kinase function in stark contrast to its role in abscission during somatic cell cytokinesis. Our results challenge existing models of ring canal formation, and provide important insights into broader functions of incomplete cytokinesis events across biological systems, such as those observed during development and disease states.

developmental biology↗