SEARCH · Search bioRxiv

Results for “Systems Biology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,783 records · Page 99Linked to original sources

bioRxiv2017

Exploiting General Independence Criteria For Network Inference

Inference of networks representing dependency relationships is a key tool for understanding data derived from biological systems. It has been shown that nonlinear relationships and non-Gaussian noise aid detection of directions of functional dependencies. In this study we explore how far generalised independence criteria for statistical independence proposed in the literature are better suited to the inference of networks compared to standard independence criteria based on linear relationships and Gaussian noise. We compare such criteria within the framework of the PC algorithm, a popular network inference algorithm for directed acyclic dependency graphs. We also propose and evaluate a method to apply unconditional independence criteria to assess conditional independence and a method to simulate data with desired properties from experimental data. Our main finding is that a recently proposed criterion based on distance covariance performs well compared to other independence criteria in terms of error rates, speed of computation, and need of fine-tuning parameters when applied to experimental biological datasets.

bioinformatics↗

bioRxiv2023

High-yield enzymatic synthesis of mono- and trifluorinated alanine enantiomers

Fluorinated amino acids are a promising entry point for incorporating new-to-Nature chemistries in biological systems. Hence, novel methods are needed for the selective synthesis of these building blocks. In this study, we focused on the enzymatic synthesis of fluorinated alanine enantiomers. To this end, the alanine dehydrogenase from Vibrio proteolyticus and the diaminopimelate dehydrogenase from Symbiobacterium thermophilum were applied to the in vitro production of (R)-3-fluoroalanine and (S)-3-fluoroalanine, respectively, using 3-fluoropyruvate as the substrate. Additionally, an alanine racemase from Streptomyces lavendulae, originally selected for setting an alternative enzymatic cascade leading to the production of these non-canonical amino acids, had an unprecedented catalytic efficiency in the {beta}-elimination of fluorine from the monosubstituted fluoroalanine. The in vitro enzymatic cascade based on the dehydrogenases of V. proteolyticus and S. thermophilum included a cofactor recycling system, whereby a formate dehydrogenase from Pseudomonas sp. 101 (either native or engineered) coupled formate oxidation to NAD(P)H formation. Under these conditions, the reaction yields for (R)-3-fluoroalanine and (S)-3-fluoroalanine reached >85% on the fluorinated substrate and proceeded with complete enantiomeric excess. Moreover, the selected dehydrogenases were also able to catalyze the conversion of trifluoropyruvate into trifluorinated alanine, as a first-case example of biocatalysis with amino acids carrying a trifluoromethyl group.

synthetic biology↗

bioRxiv2023

Comparative Analysis of Association Networks Using Single-Cell RNA Sequencing Data Reveals Perturbation-Relevant Gene Signatures

Single-cell RNA sequencing (scRNA-seq) data has elevated our understanding of systemic perturbations to organismal physiology at the individual cell level. However, despite the rich information content of scRNA-seq data, the relevance of genes to a perturbation is still commonly assessed through differential expression analysis. This approach provides a one-dimensional perspective of the transcriptomic landscape, risking the oversight of tightly controlled genes characterized by modest changes in expression but with profound downstream effects. We present GENIX (Gene Expression Network Importance eXamination), a novel platform for constructing gene association networks, equipped with an innovative network-based comparative model to uncover condition-relevant genes. To demonstrate the effectiveness of GENIX, we analyze influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) collected from recovered COVID-19 patients, shedding light on the mechanistic underpinnings of gender differences. Our methodology offers a promising avenue to identify genes relevant to perturbation responses in biological systems, expanding the scope of response signature discovery beyond differential gene expression analysis. HIGHLIGHTSO_LIConventional methods used to identify perturbation-relevant genes in scRNA-seq data rely on differential expression analysis, susceptible to overlooking essential genes. C_LIO_LIGENIX leverages cell-type-specific inferred gene association networks to identify condition-relevant genes and gene programs, irrespective of their specific expression alterations. C_LIO_LIGENIX provides insight into the gene-regulatory response to the influenza vaccine in naive and recovered COVID-19 patients, expanding on previously observed gender-specific differences. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/556872v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1837d3eorg.highwire.dtl.DTLVardef@1937860org.highwire.dtl.DTLVardef@c40114org.highwire.dtl.DTLVardef@22d3b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Unveiling the Power of High-Dimensional Cytometry Data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we developed cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We used cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high dimensionality data from preprocessing to biological interpretation.

bioinformatics↗

bioRxiv2026

Modularity-dependent storage of dynamic spiking patterns: bridging micro- and mesoscopic representations

Biological systems rely on asynchronous and temporally overlapping dynamics, allowing for the concurrent activation of multiple processes. This principle is particularly evident in brain function, where cognitive tasks engage distributed, interacting regions rather than sequentially isolated ones. To investigate the mechanisms enabling such coordination, we study a modular spiking neural network composed of leaky integrate-and-fire neurons and governed by spike-timing-dependent plasticity (STDP). Our model stores modular spatiotemporal patterns both at the mesoscopic level (sequences of modules) and at the microscopic level (precise spike timings) and includes a parameter, , which regulates the degree of temporal overlap between modules activations. By tuning , the network transitions from sequential to overlapping regimes, ranging from synfire chain-like dynamics to fully co-activated modules. We investigate how the temporal structure influences the networks capacity to encode and selectively retrieve multiple dynamical patterns, while considering biological constraints such as the cost of long-range connectivity. Our results offer insight into how spatiotemporal coding and network organisation support robust, large-scale memory storage and replay.

neuroscience↗

bioRxiv2020

Long-read Individual-molecule Sequencing Reveals CRISPR-induced Genetic Heterogeneity in Human ESCs

Accurately quantifying the genetic heterogeneity of a cell population is essential to understanding of biological systems. We develop a universal method to label individual DNA molecules for analyzing diverse types of rare genetic variants, with frequency as low as 4x10-5, using short- or long-read sequencing. It enables base-resolution haplotype-resolved quantitative characterization of rare variants. It provides the first quantitative evidence of persistent nonrandom large deletions and insertions following DNA repair of double-strand breaks induced by CRISPR-Cas9 in human pluripotent stem cells.

molecular biology↗

bioRxiv2019

Eco-evolutionary spatial dynamics of non-linear social dilemmas

Spatial dynamics can promote the evolution of cooperation. While dispersal processes have been studied in simple evolutionary games, real-world social dilemmas are much more complicated. The public good, in many cases, does not increase linearly as per the investment in it. When the investment is low, for example, every additional unit of the investment may help a lot to increase the public good, but the effect vanishes as the number of investments increase. Such non-linear behaviour is the norm rather than an exception in a variety of social as well as biological systems. We take into account the non-linearity in the payoffs of the public goods game as well as the natural demographic effects of population densities. Population density has also been shown to impact the evolution of co-operation. Coupling these non-linear games and population size effect together with an explicitly defined spatial structure brings us one step closer to the complexity of real eco-evolutionary spatial systems. We show how the non-linearity in payoffs, resulting in synergy or discounting of public goods can alter the effective rate of return on the cooperative investment. Synergy or discounting in public goods accumulation affects the resulting spatial structure, not just quantitatively but in some cases, drastically changing the outcomes. In cases where a linear payoff structure would lead to extinction, synergy can support the coexistence of cooperators and defectors. The combined eco-evolutionary trajectory can thus be qualitatively different in cases on non-linear social dilemmas.

evolutionary biology↗

bioRxiv2026

Learning with Recurrence Geometric AI in Spatial Transcriptomics

Cross-domain analysis of spatial transcriptomics is challenging because tissues from different organs, diseases and experimental platforms exhibit distinct cellular compositions, spatial organisations and technical biases, making direct comparison of tissue states difficult. Existing methods primarily focus on domain integration or batch correction but generally do not explicitly model the intrinsic geometry underlying tissue-state organisation across biological systems. This paper presents recurrence geometric artificial intelligence (RGAI), a geometric deep-learning framework for discovering and aligning latent tissue states across heterogeneous spatial transcriptomic domains. RGAI first learns domain-specific latent representations using variational graph autoencoders while simultaneously estimating a Riemannian metric tensor that captures the local geometry of each latent manifold. Geodesic distances induced by the learned metric are used to construct multiscale recurrence graphs that characterise intrinsic tissue-state organisation independently of the original measurement space. Cross-domain manifold correspondence is then established through entropy-regularised Gromov-Wasserstein alignment, after which fuzzy clustering identifies latent tissue states and optimal transport aligns tissue-state signatures across domains. Evaluation on six human spatial transcriptomic datasets spanning wound healing, periodontitis, oral squamous cell carcinoma, head and neck squamous cell carcinoma, cardiac tissue and colorectal cancer shows that RGAI automatically determines biologically meaningful latent tissue-state complexity and identifies coherent recurrence-based tissue states within each domain. The learned geometric representations enable cross-domain alignment of latent manifolds while preserving biologically interpretable tissue-state correspondences despite substantial differences in cellular composition and tissue architecture, demonstrating that integrating learned Riemannian geometry, recurrence analysis and optimal transport provides a robust and interpretable framework for cross-domain tissue-state discovery and comparison in spatial transcriptomics.

bioinformatics↗

bioRxiv2025

The Interplay Between Cell-cell Signaling and Negative Feedback Reduces Population Noise While Enhancing Single-cell Variability

Negative feedback is a well-known mechanism for attenuating noise and enhancing robustness in biological systems. When coupled with cell-cell signaling, it provides a strategy for achieving population-level control in multicellular systems. While cell-cell signaling alone tends to reduce cell-to-cell variability by averaging fluctuations across cells, its interplay with negative feedback can produce contrasting effects on noise regulation at single-cell and population levels. Therefore, to design population-level controllers that achieve robust behaviors with attenuated noise, a systematically understanding of how different noise sources impact cell gene expressions at these levels become a critical challenge. Here, we investigate noise regulation in a quorum sensing-based negative feedback system, focusing on two extrinsic noise sources: process noise from target gene dynamics and measurement noise from quorum sensing dynamics. Our results reveal that signal-based negative feedback significantly reduces process noise at the population level, especially for dynamic noise. However, at the single-cell level, it enhances variability, with increased noise levels under faster signal diffusion and higher population density. In contrast, measurement noise is consistently attenuated at both single-cell and population levels through the coupled cell-cell signaling and negative feedback, under conditions of faster diffusion and higher density.

synthetic biology↗

bioRxiv2025

Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery

Bimodal protein expression, characterized by the distribution of protein expression with two modes, is linked to phenotypic variation across various biological systems. Whereas previous studies focused on RNA expression data, we developed a bimodality model tailored for proteomics to enhance the identification of cancer-associated biomarkers and targets, facilitating precision oncology. We analyzed proteomics data from various cancer types and identified 2401 tumor-associated bimodal proteins. These proteins were evaluated for pathway enrichment, revealing significant associations with critical cancer pathways, such as metabolism of non-essential amino acids, interaction between the extracellular matrix and its receptors on the cell surface, and central carbon metabolism in cancer. Utilizing an AI-enhanced knowledge graph, we further delineated common patterns among pan-cancer tumor-associated bimodal proteins. A case study on the bimodal expression of TROP2 in colon adenocarcinoma highlighted upregulation of MYC and WNT/{beta}-catenin signaling pathways and down-regulation of inflammatory and interferon-related pathways in the TROP2-high group. The biological difference between TROP2-high and TROP2-low groups underscored its significance in determining cancer heterogeneity and differences in cancer vulnerability, which can inform treatment decisions. Our findings show the value of proteomics in uncovering novel biomarkers and advancing precision medicine, setting a precedent for further multi-omics integration and clinical validation.

bioinformatics↗

bioRxiv2020

Constructing Graphs from Genetic Encodings

Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erd[o]s-Renyi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a nodes preferred connectivity.

neuroscience↗

bioRxiv2024

Bio informatics: Integrate negative controls to get the good data

High-throughput datasets, like any experimental output, can be full of noise. Negative controls, i.e. mock experiments not providing information concerning the biological system under study, visualise background. Overlooking this training set of wrong examples in publicly available datasets can seriously undermine validity of bioinformatics analyses. We present a program, COALISPR, for explicit and transparent application of negative control data in the comparison of high-throughput sequencing results. This yields mapping coordinates that guide fast counting of reads, bypassing the need for a reference file, and is especially relevant when small RNA sequencing libraries contaminated with breakdown products are analysed for poorly annotated organisms. We have re-analysed small RNA datasets for mouse and fungus Cryptococcus neoformans, leading to consistent identification of miRNAs and of fungal transcripts targeted by siRNAs. Cryptococcal Argonautes are directed to spliced transcripts indicating that RNAi must be triggered by events downstream of intron removal. Negative control datasets contain large amounts of ribosomal RNA (rRNA) fragments (rRFs). These differ from small RNAs associated with RNAi, making a biological role for rRFs in association with Argonautes unlikely. Background signals enabled identification of cryptococcal genes for RNase P, U1 snRNA, 37 H/ACA and 63 Box C/D snoRNAs, including U3 and U14 essential for pre-rRNA processing. To gain meaning, high-throughput RNA-Seq analyses need to incorporate negative data. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/617225v4_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@c44bdcorg.highwire.dtl.DTLVardef@1509468org.highwire.dtl.DTLVardef@13f398borg.highwire.dtl.DTLVardef@1dae4b3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

Tree Shape-based approaches for the Comparative study of Cophylogeny

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long-term interaction, such as between host and pathogen species. Discordance between host and pathogen phylogenies may occur due to pathogen host-switch events, pathogen speciation within a host species, and extinction. Here, we investigated the use of tree shape distance measures to quantify the degree of cophylogeny for the comparative analysis of host-pathogen interactions across taxonomic groups.\n\nWe firstly implemented a coalescent model to simulate pathogen phylogenies within a fixed host tree, given the cospeciation probability, migration rate between hosts, and pathogen speciation rate within hosts.\n\nNext, we used simulations from this model to evaluate 13 distance metrics between these trees and the host tree, including Robinson-Foulds distance and two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and can accommodate trees of different sizes. Finally, we used these distance metrics to revisit actual datasets from published cophylogenetic studies across all taxonomic groups, where authors described the observed associations as representing a high or low degree of cophylogeny.\n\nOur simulation analyses demonstrated that some metrics are more informative than others with respect to specific coevolution parameters. For example, the Sim metric was the most responsive to variation in coalescence rates, whereas the unlabeled kernel metric was the most responsive to cospeciation probabilities. We also determined that distance metrics were more informative about the model parameters when the underlying parameter values did not assume extreme values, e.g., rapid host switching. When applied to real datasets, projection of these trees associations into a parameter space defined by the 13 distance metrics revealed some clustering of studies reporting low concordance. This suggested that different investigators are describing concordance in a consistent way across biological systems, and that these expert subjective assessments can be at least partly quantified using distance metrics.\n\nOur results support the hypothesis that tree distance measures can be useful for quantifying host and pathogen cophylogeny. This motivates the usage of distance metrics in the field of coevolution and supports the development of simulation-based methods, i.e., approximate Bayesian computation, to estimate coevolutionary parameters from the discordant shapes of host and pathogen trees. [tree shape; cophylogeny; codivergence; coevolution; host switching; tree metrics; kernel]

evolutionary biology↗

bioRxiv2026

Cellquant: a vibecoder's guide to image analysis

Quantitative fluorescence microscopy is central to modern cell biology, yet extracting reproducible measurements from images remains a bottleneck for biologists without programming experience. Here we present cellquant, a single-script command line pipeline for multi-channel fluorescence images that performs cell segmentation, puncta quantification, colocalization analysis, and spatial proximity measurements. Because the interface is entirely text based, the exact command used to generate any result can be recorded and re-executed. We validate cellquant on two biological systems. In human HCT116 cells, the pipeline quantified arsenite-induced stress granule formation. In budding yeast, simultaneous measurement of nucleolar morphology, colocalization, and spatial proximity across a temperature gradient revealed a coordinated sequence of nucleolar reorganization. Applying PCA and UMAP to the multi-parameter output of cellquant resolved a continuous cell state transition across the temperature gradient, with condensate redistribution and nucleolar morphology defining orthogonal axes. The pipeline produces publication-ready quantification with visual quality control and statistically rigorous replicate analysis. All code, documentation, and example datasets are freely available.

cell biology↗

bioRxiv2023

Probabilistic Graphical Modeling under Heterogeneity

Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad applicability to cases where sample-specific heterogeneity exists e.g. tumor heterogeneity. We propose a flexible Bayesian approach called Graphical Regression (GraphR) which (a) allows direct incorporation of intrinsic factors of sample heterogeneity at different scales through a regression-based formulation, (b) enables sparse network estimation at a sample-specific level, (c) allows identification and uncertainty quantification of potential effects of heterogeneity on network structures, and (d) is computationally efficient through the use of variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing methods in terms of graph structure recovery and computational cost across multiple realistic simulation settings. We use GraphR to analyze four diverse multi-omics and spatial transcriptomics datasets to study inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R-package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

genomics↗

bioRxiv2021

Specialization of actin isoforms derived from the loss of key interactions with regulatory factors

A paradox of eukaryotic cells is that while some species assemble a complex actin cytoskeleton from a single ortholog, other species utilize a greater diversity of actin isoforms. The physiological consequences of using different actin isoforms, and the molecular mechanisms by which highly conserved actin isoforms are segregated into distinct networks, are poorly known. Here, we sought to understand how a simple biological system, composed of a unique actin and a limited set of actin-binding proteins, reacts to a switch to heterologous actin expression. Using yeast as a model system and biomimetic assays, we show that such perturbation causes drastic reorganization of the actin cytoskeleton. Our results indicate that defective interaction of a heterologous actin for important regulators of actin assembly limits certain actin assembly pathways while reinforcing others. Expression of two heterologous actin variants, each specialized in assembling a different network, rescues cytoskeletal organization and confers resistance to external perturbation. Hence, while species using a unique actin have homeostatic actin networks, actin assembly pathways in species using several actin isoforms may act more independently.

cell biology↗

bioRxiv2023

Analysis of ageing-dependent thiol oxidation reveals early oxidation of proteins involved in core proteostasis functions

Oxidants have a profound impact on biological systems in physiology and under pathological conditions. Oxidative post-translational modifications of protein thiols are well-recognized as a readily occurring alteration of proteins. Changes in protein thiol redox state can modify the function of proteins and thus can control cellular processes. However, chronic oxidative stress causes oxidative damage to proteins with detrimental consequences for cellular function and organismal health. The development of techniques enabling the site-specific and quantitative assessment of protein thiol oxidation on a proteome-wide scale significantly expanded the number of known oxidation-sensitive protein thiols. However, lacking behind are large-scale data on the redox state of proteins during ageing, a physiological process accompanied by increased levels of endogenous oxidants. Here, we present the landscape of protein thiol oxidation in chronologically aged wild-type Saccharomyces cerevisiae in a time-dependent manner. Our data determine early oxidation targets in key biological processes governing the de novo production of proteins, folding, and protein degradation. Comparison to existing datasets reveals evolutionary conservation of early oxidation targets. To facilitate accessibility and cross-species comparison of the experimental data obtained, we created the OxiAge Database, a free online tool for the research community that integrates current datasets on thiol redoxomes in aged yeast, nematode Caenorhabditis elegans, fruit fly Drosophila melanogaster, and mouse Mus musculus. The database can be accessed through an interactive web application at http://oxiage.ibb.waw.pl.

molecular biology↗

bioRxiv2026

CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

Understanding how different cell types assemble into tissues and organs, as well as how they interact to transmit and receive biological signals, is essential for advancing biomedical and biological research. Recent advancements in spatial transcriptomics (ST) technologies have opened new avenues for investigating biological systems by achieving subcellular spatial resolution. Since cells are the fundamental units of life, extracting single-cell information from high-resolution ST data is crucial. However, existing ST platforms often capture sparse transcript counts per spot or measure only a limited number of genes, complicating the extraction of comprehensive single-cell information. In this study, we introduce CellART, a unified framework designed to extract single-cell information across diverse high-resolution ST platforms, including VisiumHD, Xenium, MERFISH, and Stereo-seq. By leveraging multimodal data, such as staining images, spatial transcriptomics data, and single-cell RNA sequencing references, CellART simultaneously performs cell segmentation and cell type annotation through a seamless integration of deep learning and probabilistic modeling. We demonstrate the efficiency, generalizability, and robustness of CellART across various high-resolution spatial transcriptomics platforms, capable of processing datasets containing millions of spots. Comprehensive experiments validate the biological relevance and accuracy of the recovered cellular information within spatial configurations. Notably, we highlight the utility of CellART in breast and colorectal cancer datasets, showcasing its ability to fully leverage high-resolution ST data. By enhancing cellular resolution, CellART facilitates the identification of transient cancer cell states and immune cell subtypes. Furthermore, CellART enables investigations into cancer-immune cell communication, uncovering both established interactions and novel ligand-receptor pairs. The outputs of CellART are compatible with widely used community tools, facilitating a variety of downstream analyses.

bioinformatics↗