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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

An orthogonal Cro/OR3 binary system for targeted gene expression

Precise spatiotemporal control of gene expression is essential for dissecting complex biological systems. Here, we introduce a phage-derived Cro/OR3 binary system for transcriptional activation that functions robustly in zebrafish and human cells. A Cro repressor fused to the VP16 activation domain efficiently drives expression of OR3 operator-linked genes, and its activity can be targeted to specific cell types using enhancer-trap and promoter-controlled zebrafish lines. As a proof of concept, we manipulate glutamate transporter Glast/Slc1a3b-expressing cells in the central nervous system by Cro/OR3-mediated expression of a human ataxia-associated, uptake-deficient Glast/Slc1a3b variant, causing spastic, uncoordinated movement and axial deformation. Concurrent Gal4/UAS-mediated visualization reveals substantially disrupted positioning of motor neurons, highlighting their selective sensitivity to extracellular glutamate homeostasis during spinal circuit assembly. Together, these results establish the Cro/OR3 system as an orthogonal platform for targeted gene expression, enabling precise mechanistic dissection of how specific cell types and their interactions underlie diverse biological processes.

bioengineering↗

bioRxiv2015

Rapid quantitative pharmacodynamic imaging with Bayesian estimation

We recently described rapid quantitative pharmacodynamic imaging, a novel method for estimating sensitivity of a biological system to a drug. We tested its accuracy in simulated biological signals with varying receptor sensitivity and varying levels of random noise, and presented initial proof-of-concept data from functional MRI (fMRI) studies in primate brain. However, the initial simulation testing used a simple iterative approach to estimate pharmacokinetic-pharmacodynamic (PKPD) parameters, an approach that was computationally efficient but returned parameters only from a small, discrete set of values chosen a priori.\n\nHere we revisit the simulation testing using a Bayesian method to estimate the PKPD parameters. This improved accuracy compared to our previous method, and noise without intentional signal was never interpreted as signal. We also reanalyze the fMRI proof-of-concept data. The success with the simulated data, and with the limited fMRI data, is a necessary first step toward further testing of rapid quantitative pharmacodynamic imaging.

Pharmacology and Toxicology↗

bioRxiv2023

Informing generative replay for continual learning with long-term memory formation in the fruit fly

Continual learning without catastrophic forgetting is a challenge for artificial systems but it is done naturally across a range of biological systems, including in insects. A recurrent circuit has been identified in the fruit fly mushroom body to consolidate long term memories (LTM), but there is not currently an algorithmic understanding of this LTM formation. We hypothesize that generative replay is occurring to consolidate memories in this recurrent circuit, and find anatomical evidence in synapse-level connectivity that supports this hypothesis. Next, we introduce a computational model which combines an initial experience phase and LTM phase to perform generative replay based continual learning. When evaluated on a CIFAR-100 class-incremental continual learning task, the modeled LTM phase increases classification performance by 20% and approaches within 2% of the performance for a non-incremental upper bound baseline. Unique elements of the proposed generative replay model include: 1) coupling high dimensional sparse activation patterns with generative replay and 2) sampling and reconstructing higher level representations for training generative replay (as opposed to reconstructing sensory-level or processed sensory-level representations). Additionally, we make the experimentally testable prediction that a specific set of synapses would need to undergo experience-dependent plasticity during LTM formation to support our generative replay based model.

neuroscience↗

bioRxiv2024

Microgravity and low muon radiation affect viral pathogenesis and physiology in Caenorhabditis elegans

Environmental conditions fundamentally shape host-pathogen interactions, yet how multiple extreme abiotic stressors combine to influence infection outcomes remains poorly understood. Organisms evolved under specific gravitational and radiation regimes; deviations from these conditions --whether in extreme terrestrial environments or beyond Earth-- may alter immune function and pathogen replication. In this study, we investigated the effects of reduced gravity and lowered muon flux on Orsay virus infection in the nematode Caenorhabditis elegans. We employed a fully factorial experimental design, examining how each factor, alone and in combination, influences physiological traits and viral load. While below-background radiation radically affected viral accumulation dynamics, reduced gravity had a minor effect. Both factors significantly impacted reproduction and morphology, with some effects magnified by viral infection. These results reveal how even partial modifications of Earth-like gravity and radiation levels can alter pathogen-host interactions. By integrating experimental observations with mathematical modeling, we show that these environmental stressors primarily affect prezygotic reproductive processes and modulate viral replication through distinct and sometimes antagonistic mechanisms. Although this work does not encompass the full complexity of space environments, where cosmic radiation includes high-energy protons and heavy ions, it provides insight into how adjustable models of reduced gravity and radiation can advance our understanding of biological adaptation beyond standard terrestrial conditions. IMPORTANCEUnderstanding how extreme environmental conditions affect host-pathogen interactions is critical both for safeguarding biological systems during spaceflight and for exploring fundamental principles of stress biology. This study demonstrates that reduced gravity and diminished muon radiation flux can significantly alter viral infection dynamics and host physiology in Caenorhabditis elegans. By integrating experimental data with mathematical modeling, we reveal that these abiotic stresses impact prezygotic reproductive processes and modulate viral replication in distinct and sometimes antagonistic ways. Our findings suggest that even partial deviations from Earth-like conditions can reshape infection outcomes and developmental trajectories, highlighting the need for deeper mechanistic insights into biological adaptation beyond terrestrial norms. These results have implications for space biosciences, evolutionary virology, radiation hormesis theory, and the design of countermeasures to preserve organismal health in extreme or non-terrestrial habitats.

microbiology↗

bioRxiv2024

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data Analysis

Single-cell Multi-modal Data Integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline, and evaluate the contributions of data integration. In this manuscript, we revisit the definition and contributions of multi-modal data integration, and propose a novel and scalable method based on probabilistic deep learning with an explainable framework powered by statistical modeling to extract meaningful information after data integration. Our proposed method is capable of integrating different types of omic and sensing data. It offers an approach to discovering important relationships among biological features or cell states. We demonstrate that our method outperforms other baseline models in preserving both local and global structures and perform a comprehensive analysis for mining structural relationships in complex biological systems, including inference of gene regulatory networks, extraction of significant biological linkages, and analysis of differentially regulatory relationships.

bioinformatics↗

bioRxiv2025

TopoTome: Topology-informed unsupervised segmentation and analysis of 3D images

Biological systems are three-dimensional and complex. Today, images of biological structures can be acquired using different imaging technologies and at increasing resolutions. However, identifying relevant structural features in three-dimensional (3D) images remains a significant challenge. 3D image segmentation is usually performed using deep-learning segmentation models. Such models are trained on manually annotated and segmented, dataset-specific images. Consequently, they rarely generalize across datasets. Here, we overcome these limitations with TopoTome, an unsupervised 3D image segmentation and analysis algorithm. TopoTome is based on topological data analysis, and conceptually distinct from standard clustering and deep learning image analysis models. It encodes the 3D image directly in topological space. Then, it performs unsupervised clustering to detect and segment features represented by salient and spatially coherent voxel intensity gradients. We demonstrate on simple, complex, synthetic and real-world 3D image data that it outperforms all 3D clustering algorithms. Its segmentation ranks with or outperforms best-in-class deep learning 3D image segmentation software. Beyond image segmentation, it provides streamlined topological data analysis of 3D images, advancing 3D image analysis from conventional mesh volumetry to structural topology. Owing to its conceptually different topological data analysis core, TopoTome does not need prior information and tuning to generalize across different imaging modalities, including fluorescence microscopy and X-ray computed tomography. We show it also readily generalizes across biological subjects, such as different species, organs and cells. TopoTome is thus one of the most versatile and accurate unsupervised 3D image segmentation algorithms.

bioinformatics↗

bioRxiv2026

MultiGEOmics: Graph-Based Integration of Multi-Omics via Biological Information Flows

Multi-omics datasets capture complementary aspects of biological systems and are central to modern machine learning applications in biology and medicine. Existing graph-based integration methods typically construct separate graphs for each omics type and focus primarily on intra-omic relationships. As a result, they often overlook cross-omics regulatory signals--bidirectional interactions across omics layers--that are critical for modeling complex cellular processes. A second major challenge is missing or incomplete omics data; many current approaches degrade substantially in performance or exclude patients lacking one or more omics modalities. To address these limitations, we introduce MultiGEOmics, an intermediate-level graph integration framework that explicitly incorporates regulatory signals across omics types during graph representation learning and models biologically inspired omics-specific and cross-omics dependencies. MultiGEOmics learns robust cross-omics embeddings that remain reliable even when some modalities are partially missing. We evaluated MultiGEOmics across eleven datasets spanning cancer and Alzheimers disease, under zero, moderate, and high missing-rate scenarios. MultiGEOmics consistently maintains strong predictive performance across all missing-data conditions while offering interpretability by identifying the most influential omics types and features for each prediction task. The source code and the documentation of MultiGEOmics are available at https://github.com/bozdaglab/MultiGEOmics.

bioinformatics↗

bioRxiv2016

MAMMOTh: a new database for curated MAthematical Models of bioMOlecular sysTems

Living systems have a complex hierarchical organization that can be viewed as a set of dynamically interacting subsystems. Thus, to simulate the internal nature and dynamics of the whole biological system we should use the iterative way for a model reconstruction, which is a consistent composition and combination of its elementary subsystems. In accordance with this bottom-up approach, we have developed MAMMOTh (MAthematical Models of bioMOlecular sysTems) database that allows integrating manually curated mathematical models of biomolecular systems, which are fit to the experimental data. The database entries are organized as building blocks in a way that the model parts can be used in different combinations to describe systems with higher organizational level (metabolic pathways and/or transcription regulatory networks). The database supports export of single model or their combinations in SBML or Mathematica standards. The database currently contains more than 100 mathematical models for Escherichia coli elementary subsystems (enzymatic reactions and gene expression regulatory processes) that can be combined in at least 5100 complex/sophisticated models concerning such biological processes as: de novo nucleotide biosynthesis, aerobic/anaerobic respiration, and nitrate/nitrite utilization in E. coli. All current models are functionally interconnected and sufficiently complement public model resources.\n\nDatabase URL: http://mammoth.biomodelsgroup.ru

Systems Biology↗

bioRxiv2024

Hierarchical Bayesian inference to model continuous phenotypical progression in Alzheimer's Disease

Throughout an organisms life, a multitude of complex and interdependent biological systems transition through biophysical processes that serve as indicators of the underlying biological states. Inferring these latent, unobserved states is a goal of modern biology and neuroscience. However, in many experimental setups, we can at best obtain discrete snapshots of the system at different times and for different individuals. This challenge is particularly relevant in the study of Alzheimers Disease (AD) progression, where we observe the aggregation of pathology in brain donors, but the underlying disease state is unknown. This paper proposes a biophysically motivated Bayesian framework (B-BIND: Biophysical Bayesian Inference for Neurode-generative Dynamics), where the disease state is modeled and continuously inferred from observed quantifications of multiple AD pathological proteins. Inspired by biophysical models, we describe pathological burden as an exponential process. The progression of AD is modeled by assigning a latent score, termed pseudotime, to each pathological state, creating a pseudotemporal order of donors based on their pathological burden. We study the theoretical properties of the model using linearization to reveal convergence and identifiability properties. We provide Markov chain Monte Carlo estimation algorithms, illustrating the effectiveness of our approach with multiple simulation studies across various data conditions. Applying this methodology to data from the Seattle Alzheimers Disease Brain Cell Atlas, we infer the pseudotime ordering of donors. Finally, we analyze the information within each pathological feature to refine the model, focusing on the most informative pathologies. This framework lays the groundwork for continuous pseudotime modeling in the analysis of neurodegenerative diseases.

neuroscience↗

bioRxiv2023

Methodology for inference of intercellular gene interactions

To govern organ size, shape, and function, cell-secreted diffusible molecules called morphogens spatially pattern cell differentiation, gene expression, and proliferation. Local morphogen concentration governs cell differentiation through gene regulatory networks (GRN). Previous inference methodologies tackle intercellular GRN inference between cells of one type. This is insufficient, as many developmental systems consist of cells of different types interacting with each other. Inference methodologies of GRNs between different cell types assume knowledge of diffusible morphogen identity and concentration. This makes their applicability limited in real biological systems. Here, we develop a computational methodology to infer the intercellular GRN derived from experiments that use fluorescence from reporter proteins for gene expression measurements. For validation, we demonstrate the methodology in silico using three case studies based on developmental and synthetic biology. The results show that, barring practical identifiability limitations, the methodology successfully infers the intercellular GRNs.

systems biology↗

bioRxiv2026

Global epistasis in ecosystems arises from resource constraints

Global epistasis refers to the observation that the effect of a mutation or modification depends on the state of a biological system, not its detailed composition. Such patterns have been reported across biological scales, from proteins to organisms and ecosystems. In its simplest form, global epistasis appears as a linear relationship between the change in function or fitness due to a perturbation, and the background level of function or fitness. The mechanistic basis of global epistasis, particularly in ecological systems, remains unresolved. Here, we propose that in microbial communities, global epistasis describing the impact of adding a species to a community on function arises generically from constraints imposed by shared resource pools. We illustrate this mechanism in a single-species system growing on multiple substitutable resources, where global epistasis follows directly from nutrient limitation by an essential non-substitutable resource. We then extend this framework to multi-species communities competing for a single resource and show that the marginal effect of adding a species depends linearly on background community function, with a slope determined by the fraction of the resource claimed by the added species. We show that global epistasis persists in trophic cascades, but that facilitation and niche partitioning qualitatively break the linear dependence. This study provides a simple explanation for the appearance of global epistasis in ecosystems, and suggests that global epistasis should be a null expectation in ecosystems governed by competition. Our results propose that coupling between perturbations and shared resource pools might also help explain global epistasis at the organismal level.

ecology↗

bioRxiv2022

AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease

Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimers Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets. Author SummaryFor the last 20 years the field of Alzheimers Disease (AD) is marked by a series of continuous failures to deliver demonstrably effective medications to patients. This is also highlighted by the highly controversial recent approval of Aduhelm (Biogen) by the FDA, which is now investigated internally due to the lack of clear efficacy. One of the reasons for the continuous failure of trials in AD is the choice of the wrong target mechanism. In essence there is a lack of understanding, how targeting a certain molecule would affect cognitive impairment in human. One way to address this issue is the development of quantitative system level models connecting the molecular level with the phenotype. However, formulating such models in the field of Alzheimers Disease is challenged by a lack of detailed knowledge of relevant biochemical processes and the connection of molecular mechanisms to cognitive impairment. Additionally, fitting of differential equation systems, which are often used in systems biology, requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders due to the lack of realistic model systems. Our work addresses these challenges by employing a novel hybrid Artificial Intelligence (AI) approach combining variational autoencoders with Bayesian Networks. Our proposed approach, named Integrative Variational Autoencoder Modular Bayesian Networks (iVAMBN), was trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our method resulted in an interpretable, quantitative model. It showed connections between various biological mechanisms playing a role in AD. Furthermore, iVAMBN directly connected the molecular level to the disease phenotype. Our model allowed us to simulate a down-expression of the putative drug target CD33. Results showed a significantly increased cognition and predicted perturbation of a number of biological mechanisms. We experimentally validated these predictions using gene expression data from a knock-out THP-1 monocyte cell line. This experiment confirmed our model predictions up to a very high extend. To our knowledge we thus developed the first experimentally validated, quantitative, multi-scale model connecting molecular mechanisms with clinical outcomes in the AD field.

systems biology↗

bioRxiv2023

Sensitivity Analysis of Biochemical Systems Using Bond Graphs

The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearised with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

systems biology↗

bioRxiv2024

Analyzing RNA-Seq Data from Chlamydia with Super Broad Transcriptomic Activation: Challenges, Solutions, and Implications for Other Systems

MotivationRNA sequencing (RNA-Seq) offers profound insights into the complex transcriptomes of diverse biological systems. However, standard differential expression analysis pipelines based on DESeq2 and edgeR encounter challenges when applied to the immediate early transcriptomes of Chlamydia spp., obligate intracellular bacteria. These challenges arise from their reliance on assumptions that do not hold in scenarios characterized by extensive transcriptomic activation and limited repression. Standard analyses using unique chlamydial RNA-Seq reads alone identify nearly 300 upregulated and about 300 downregulated genes, significantly deviating from actual RNA-Seq read trends. ResultsBy incorporating both chlamydial and host reads or adjusting for total sequencing depth, the revised normalization methods each detected over 700 upregulated genes and 30 or fewer downregulated genes, closely aligned with observed RNA-Seq data. Further validation through qRT-PCR analysis confirmed the effectiveness of these adjusted approaches in capturing the true extent of transcriptomic activation during the immediate early phase of chlamydial infection. While the strategies employed are developed in the context of Chlamydia, the principles of flexible and context-aware normalization may inform adjustments in other systems with unbalanced gene expression dynamics, such as bacterial spore germination. Availability and implementationThe code for reproducing the presented bioinformatic analysis is available at https://zenodo.org/records/11201379.

bioinformatics↗

bioRxiv2025

Interpretable gene network inference with nonlinear causality

Interactions among genes orchestrate the growth and survival of all living systems. Such interactions are mediated by vast networks of time-dependent couplings, yet existing strategies for inferring gene networks from omics data do not account for the strong causal constraints acting on nonlinear dynamical systems. As a result, we show here that current gene network inference approaches fail when applied to time series from physical systems. We thus introduce RiCE, a physics-based algorithm for inferring causal interaction graphs using geometric information embedded within time series. We benchmark RiCE against 30 other gene network inference methods, including recent probabilistic machine learning methods, and obtain leading results across 15 datasets spanning diverse experimental modalities and tissue types. RiCE learns physically-interpretable parameters of complex biological systems, requiring low overhead to achieve leading performance. We show diverse applications of RiCE: identifying transition states during the epithelial-mesenchymal transition, classifying cell subtypes during immune cell activation, and determining transcriptional dynamics during endocrinogenesis. Our work demonstrates nonlinearity as a critical inductive bias to accurately infer gene-gene interactions, as well as a key quantitative metric for understanding the dynamic biology of the cell.

systems biology↗

bioRxiv2017

Towards physical principles of biological evolution

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in \"emergent phenomena\". We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice for adequate modeling of the biological level of complexity, and new developments within physics itself are likely to be required.

evolutionary biology↗

bioRxiv2022

Synthetic Mechanotransduction Using Engineered SynNotch Receptors

Cells can sense and interpret mechanical stimuli from their environments and neighbors, but the ability to engineer customized mechanosensing capabilities has remained a synthetic and mechanobiology challenge. Here, we introduce tension-tuned synthetic Notch (SynNotch) receptors that can be used to convert extracellular and intercellular forces into specifiable gene expression changes. By elevating the tension requirements of SynNotch activation, in combination with structure-guided mutagenesis, we designed a set of receptors with mechanical sensitivities spanning the physiologically relevant picoNewton (pN) range. Cells expressing these receptors can distinguish between varying tensile forces and respond by enacting customizable transcriptional programs. The synthetic utility of these tools is demonstrated by designing a decision-making circuit, through which fibroblasts can be made to differentiate into myoblasts upon stimulation with distinct tension magnitudes. Mechanobiological utility is also demonstrated by characterizing cell-generated forces transmitted between cells during Notch signaling. Overall, this work provides insight regarding how mechanically induced changes in protein structure can be used to transduce physical forces into biochemical signals. The system should facilitate the further programming and dissection of force-related phenomena in biological systems.

synthetic biology↗

bioRxiv2019

Exploring the Dynamics of Nonlinear Biochemical Systems using Control-Based Continuation

AO_SCPLOWBSTRACTC_SCPLOWMathematical modelling is routinely used in Systems Biology to understand the mechanisms causing nonlinear phenomena in gene expression, such as switch-like behaviours and temporal oscillations. The reliability of model predictions and bifurcation analysis depend on modelling assumptions and specific choices of model parameters; however, the identification of models is highly challenging due to the complexity of biochemical interactions and noise in experimental data. This paper numerically investigates the use of control-based continuation (CBC) for tracking dynamical features of biochemical systems and, in particular, the bistable dynamics of a gene regulating pluripotency in embryonic stem cells. CBC is a method that exploits feedback control and path following algorithms to explore the dynamic features of a nonlinear physical system directly during experimental tests. CBC applications have so far been limited to non-living (i.e. electro-mechanical) systems. Our numerical simulations show that, in principle, CBC could also be applied to biological experiments to characterise the switch-like dynamics of genes that are important for cell decision making.

systems biology↗