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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 235 records · Page 13Linked to original sources
bioRxiv2024

Multimolecular feature-based machine learning: system biology enhanced RF-QSAR modeling for the efficient prediction of the inhibitory potential of diverse SARS CoV-2 3CL Protease inhibitors

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗
bioRxiv2024

Mechanistic modeling of Mycobacterium tuberculosis β-carbonic anhydrase inhibitors using integrated systems biology and the QSAR approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗
bioRxiv2024

Elucidating the molecular mechanism of phytochemicals against Parkinson's disease through an integrated systems biology and molecular modeling approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗
bioRxiv2024

Peptide modified, programmable DNA tetrahedra to modulate autophagy in biological systems

Autophagy is a critical cellular pathway for degrading and recycling damaged components, essential for maintaining cellular homeostasis. Dysregulation of autophagy contributes to various diseases, including neurodegenerative disorders, cancers, and metabolic syndromes, highlighting the therapeutic potential of controlled autophagy induction. However, current autophagy inducers often lack specificity and may inadvertently trigger apoptosis, limiting their clinical utility. Here, we present a DNA tetrahedron-BH3 peptide nanosystem (Tdpep) engineered to selectively induce autophagy by disrupting the Beclin 1-Bcl2 interaction, a pivotal regulatory point in autophagy initiation. Tdpep, functionalized with a BH3 peptide targeting Bcl2, demonstrated efficient cellular uptake and minimal cytotoxicity in HeLa cells at concentrations up to 200nM. Autophagy induction was confirmed by increased LC3B puncta formation and fluorescence intensity comparable to that induced by rapamycin. Autophagy flux analysis of Tdpep with bafilomycin A1 validated enhanced autophagic activity rather than flux inhibition. Furthermore, Tdpep treatment significantly reduced cellular ROS levels, indicating effective autophagic turnover. Apoptosis assays showed that Tdpep did not induce apoptosis, confirming its selective autophagy induction. Furthermore, Tdpep nanosystem also induced autophagy in Danio rerio larvae in vivo model. Thus, this targeted DNA tetrahedron nanosystem provides a precise autophagy modulation platform with minimized off-target effects, offering a promising therapeutic strategy for diseases associated with autophagy dysfunction. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/621781v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@193cde5org.highwire.dtl.DTLVardef@b2a041org.highwire.dtl.DTLVardef@13711eaorg.highwire.dtl.DTLVardef@792bb5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗
bioRxiv2025

Evaluating Mycorrhizal association of Funneliformis mosseae and Glomus macrocarpum with Solanum melongena L.( brinjal) using proteomics and system biology approaches.

Arbuscular mycorrhizal fungi (AMF) symbiotically associate with plant roots and enhance the uptake of inorganic nutrients, increase the ability to withstand the biotic and abiotic stresses. The interaction between plants and microbes can be studied using proteomic. The proteomic profile of an organism can be used to determine the subcellular localization, different chemical environments, potential interaction partners etc. The study was carried out to explore the comparative proteomics of Solanum melongena upon association with Glomus macrocarpum and Funneliformis mosseae. The study revealed that in comparison to the untreated control, mycorrhizal fungi, G.macrocarpum and F. mosseae led to upregulation of 43 and 31 proteins respectively. Of these proteins, 26 proteins were common in both the species while 17 were specific to G. macrocarpum and 5 were specific to F. mosseae. These proteins were predicted to be involved in major physiologically important pathways.

bioinformatics↗
bioRxiv2025

A Systems Biology Approach to Unveil Shared Therapeutic Targets and Pathological Pathways Across Major Human Cancers

Cancer is a leading global cause of mortality, responsible for nearly 10 million deaths annually, with breast, lung, colorectal, and prostate cancers among the most prevalent. Despite extensive research on individual cancer types, identifying shared molecular signatures could unlock pan-cancer diagnostic tools and therapeutic targets. This study leveraged RNA-seq data from The Cancer Genome Atlas (TCGA) to analyze our selected cancer types (SCTs)--breast, lung, colorectal, and prostate, employing a multi-step bioinformatics pipeline. Differentially expressed genes (DEGs) between tumors and normal tissues were identified and validated using an Elastic Net regression model. Weighted Gene Co-expression Network Analysis (WGCNA) revealed highly correlated gene modules linked to clinical traits, pinpointing 179 shared signature genes across the SCTs. Protein-protein interaction (PPI) network and clustering analyses further refined these to 26 hub genes, enriched in cancer hallmark pathways. Nine hub genes--KIF18B, RRM2, MYBL2, IQGAP3, TPX2, SLC7A11, RHPN1, HJURP, and SKA3--stood out due to consistent upregulation in metastatic tumors (breast, colorectal, prostate) and their high expression across more than 18 different cancer types, suggesting roles as oncogenes, prognostic markers, or therapeutic targets. The expression patterns of hub genes were further validated across larger cancer patient cohorts of SCTs, confirming relevance across multiple datasets, and their prognostic significance was assessed by their influence on overall survival (OS). Notably, these hub genes also correlated with immune-related functions, potentially influencing tumor microenvironment modulation. This integrative approach provides a strong framework for identifying cross-cancer potential biomarkers, advancing pan-cancer insights, and supporting improved diagnosis, prognosis, and therapy development.

bioinformatics↗
bioRxiv2025

Identifying Molecular Determinants and Therapeutic Targets in Luminal B Breast Cancer: A Systems Biology Approach

Luminal B breast cancer (LBBC) is on the rise worldwide, with both incidence and mortality rates steadily increasing. Early detection proves difficult due to its aggressive characteristics, most notably its heightened proliferation rate and the complex interplay of molecular biomarkers, particularly in more advanced stages. In the present study, we conducted an in silico analysis of LBBC cell lines using the Gene Expression Omnibus (GEO) microarray dataset, which includes 30 LBBC and 11 normal samples. Differentially expressed genes (DEGs) were identified using RStudio. A series of analyses, including cancer data interrogation via pan-cancer analysis, eXpression2Kinases (X2K), and the Cancer Dependency Map (DepMAP), was carried out to elucidate the underlying signaling pathways, transcription factors (TFs), and kinases, as well as to perform stemformatics analysis. Protein-protein interaction (PPI) networks were reconstructed using STRING and Cytoscape, enabling the identification of co-expressed and hub genes through the cytoHubba plug-in. Of note, FGF2, EGFR, ADIPOQ, LIPE, MET, IGF1, FGF1, EGF, FGF7, and PPARG were identified as the top 10 driver genes; RELA, PPARG, CTCF, EGR1, and NFE2L2 were detected as predominant TFs in LBBC; and CDK1, CDK2, MAPK3, CSNK2A1, and MAPK14 were identified as potential biomarkers through hub gene clustering. Further analysis indicated hsa-mir-221-3p and hsa-mir-29a-3p as key miRNAs targeting LBBC-related genes. Collectively, our findings highlighted LBBC-associated genes, TFs, miRNAs, and pathways as prospective biomarkers, providing insights into LBBC diagnosis and therapeutic approaches.

bioinformatics↗
bioRxiv2018

A systems biology approach to explore the impact of maple tree dormancy release on sap variation and maple syrup quality

Maple sap is a complex nutrient matrix collected during Spring to produce maple syrup. The characteristics of sap change over the production period and its composition directly impacts syrup quality. This variability could in part be attributed to changes in tree metabolism following dormancy release, but little is known about these changes in deciduous trees. Therefore, understanding the variation in sap composition associated with dormancy release could help pinpoint the causes of some defects in maple syrup. In particular, a defect known as \"buddy\", is an increasing concern for the industry. This off-flavor appears around the time of bud burst, hence its name. To investigate sap variation related to bud burst and the buddy defect, we monitored sap variation with respect to a dormancy release index (Sbb) and syrup quality. First, we looked at variation in amino acid content during this period. We observed a shift in amino acid relative proportions associated with dormancy release and found that most of them increase rapidly near the point of bud burst, correlating with changes in syrup quality. Second, we identified biological processes that respond to variation in maple sap by performing a competition assay using the barcoded Saccharomyces cerevisiae prototroph deletion collection. This untargeted approach revealed that the organic sulfur content may be responsible for the development of the buddy off-flavor, and that dormancy release is necessary for the appearance of the defect, but other factors such as microbial activity may also be contributing.

plant biology↗
bioRxiv2019

A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease

BackgroundLate-onset Alzheimers disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimers have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes.\n\nResultsThis resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter(R) Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of three mouse models based on LOAD genetics, carrying APOE4 and TREM2*R47H alleles, demonstrated overlaps with distinct human AD modules that, in turn, are functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq shows strong correlation between gene expression changes independent of experimental platform.\n\nConclusionsTaken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.

neuroscience↗
bioRxiv2019

Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after the transition from autotrophic to heterotrophic growth condition in the microalga

Heterotrophic cultures are among the most promising strategies put forth to overcome the low biomass and secondary metabolites productivity challenge. To shedding light on the underlying molecular mechanisms, weighted gene co-expression network analysis (WGCNA) was integrated with transcriptome meta-analysis, connectivity analysis, functional enrichment, and hub genes identification. Meta-analysis and Functional enrichment analysis demonstrated that with the progress to developmental phases at heterotrophic growth condition, most of the biological processes are up-regulated, which leads to the change of genetic architectures and phenotypic outcomes. WGNCA analysis of meta-genes also created four significant functional modules across logarithmic (LG), transition (TR), and production peak (PR) phases. The expression pattern and connectivity characteristics of the brown module as a non-preserved module vary across LG, TR, and PR phases. Functionally analysis identified "Carotenoid biosynthesis", "Fatty acid metabolism", and "Methane metabolism" as enriched pathways in the non-preserved module. Moreover, the integrated approach was applied here, identified some hub genes, such as serine hydroxymethyltransferase (SHMT1) for development of metabolites accumulating strains in microalgae. Our study provided a new insight into underlying mechanisms of metabolite accumulation and opens new avenue for the future applied studies in the microalgae field.

bioinformatics↗
bioRxiv2019

Robustness and parameter geography in post-translational modification systems

Biological systems are acknowledged to be robust to perturbations but a rigorous understanding of this has been elusive. In a mathematical model, perturbations often exert their effect through parameters, so sizes and shapes of parametric regions offer an integrated global estimate of robustness. Here, we explore this "parameter geography" for bistability in post-translational modification (PTM) systems. We use the previously developed "linear framework" for timescale separation to describe the steady-states of a two-site PTM system as the solutions of two polynomial equations in two variables, with eight non-dimensional parameters. Importantly, this approach allows us to accommodate enzyme mechanisms of arbitrary complexity beyond the conventional Michaelis-Menten scheme, which unrealistically forbids product rebinding. We further use the numerical algebraic geometry tools Bertini, Paramotopy, and alphaCertified to statistically assess the solutions to these equations at [~]109 parameter points in total. Subject to sampling limitations, we find no bistability when substrate amount is below a threshold relative to enzyme amounts. As substrate increases, the bistable region acquires 8-dimensional volume which increases in an apparently monotonic and sigmoidal manner towards saturation. The region remains connected but not convex, albeit with a high visibility ratio. Surprisingly, the saturating bistable region occupies a much smaller proportion of the sampling domain under mechanistic assumptions more realistic than the Michaelis-Menten scheme. We find that bistability is compromised by product rebinding and that unrealistic assumptions on enzyme mechanisms have obscured its parametric rarity. The apparent monotonic increase in volume of the bistable region remains perplexing because the region itself does not grow monotonically: parameter points can move back and forth between monostability and bistability. We suggest mathematical conjectures and questions arising from these findings. Advances in theory and software now permit insights into parameter geography to be uncovered by high-dimensional, data-centric analysis. Author SummaryBiological organisms are often said to have robust properties but it is difficult to understand how such robustness arises from molecular interactions. Here, we use a mathematical model to study how the molecular mechanism of protein modification exhibits the property of multiple internal states, which has been suggested to underlie memory and decision making. The robustness of this property is revealed by the size and shape, or "geography," of the parametric region in which the property holds. We use advances in reducing model complexity and in rapidly solving the underlying equations, to extensively sample parameter points in an 8-dimensional space. We find that under realistic molecular assumptions the size of the region is surprisingly small, suggesting that generating multiple internal states with such a mechanism is much harder than expected. While the shape of the region appears straightforward, we find surprising complexity in how the region grows with increasing amounts of the modified substrate. Our approach uses statistical analysis of data generated from a model, rather than from experiments, but leads to precise mathematical conjectures about parameter geography and biological robustness.

systems biology↗
bioRxiv2022

Stochastic Spatiotemporal Simulation of a General Reaction System

Biological systems frequently contain biochemical species present as small numbers of slowly diffusing molecules, leading to fluctuations that invalidate deterministic analyses of system dynamics. The development of mathematical tools that account for the spatial distribution and discrete number of reacting molecules is vital for understanding cellular behavior and engineering biological circuits. Here we present an algorithm for an event-driven stochastic spatiotemporal simulation of a general reaction process that bridges well-mixed and unmixed systems. The algorithm is based on time-varying particle probability density functions whose overlap in time and space is proportional to reactive propensity. We show this to be mathematically equivalent to the Gillespie algorithm in the specific case of fast diffusion. We develop a computational implementation of this algorithm and provide a Fourier transformation-based approach which allows for near constant computational complexity with respect to the number of individual particles of a given species. To test this simulation method, we examine reaction and diffusion limited regimes of a bimolecular association-dissociation reaction. In the reaction limited regime where mixing occurs between individual reactions, equilibrium numbers of components match the expected values from mean field methods. In the diffusion limited regime, however, spatial correlations between newly dissociated species persist, leading to rebinding events and a shift the in the observed molecular counts. In the final part of this work, we examine how changes in enzyme efficiency can emerge from changes in diffusive mobility alone, as may result from protein complex formation.

biophysics↗
bioRxiv2020

Aster repulsion drives local ordering in an active system

Biological systems are a form of active matter, which often undergo rapid changes in their material state, e.g. liquid to solid transitions. Yet, such systems often also display remarkably ordered structures. It remains an open question as to how local ordering occurs within active systems. Here, we utilise the rapid early development of Drosophila melanogaster embryos to uncover the mechanisms driving short-ranged order. During syncytial stage, nuclei synchronously divide (within a single cell defined by the ellipsoidal eggshell) for nine cycles after which most of the nuclei reach the cell cortex. Despite the rapid nuclear division and repositioning, the spatial pattern of nuclei at the cortex is highly regular. Such precision is important for subsequent cellularisation and morphological transformations. We utilise ex vivo explants and mutant embryos to reveal that microtubule asters ensure the regular distribution and maintenance of nuclear positions in the embryo. For large networks of nuclei, such as in the embryo, we predict - and experimentally verify - the formation of force chains. The ex vivo extracts enabled us to deduce the force potential between single asters. We use this to predict how the nuclear division axis orientation in small ex vivo systems depend on aster number. Finally, we demonstrate that, upon nucleus removal from the cortex, microtubule force potentials can reorient subsequent nuclear divisions to minimise the size of pattern defects. Overall, we show that short-ranged microtubule-mediated repulsive interactions between asters can drive ordering within an active system.

biophysics↗
bioRxiv2023

Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems

Biological systems often create materials with intricate structures to achieve specialized functions. In comparison, precise control of structures in man-made materials has been challenging. Here, we report a serendipitous discovery of insect cuticle peptides (ICPs) spontaneously forming nanocapsules through a single-step solvent exchange process, where the concentration gradient resulting from mixing of water and aceton drives the localization and self-assembly of the peptides into hollow nanocapsules. The underlying driving force is the intrinsic affinity of the peptides for a particular solvent concentration, while the diffusion of water and acetone creates a gradient interface that triggers peptide localization and self-assembly. This gradient-mediated self-assembly offers a transformative pathway towards next-generation drug delivery systems based on peptide nanocapsules.

bioengineering↗
bioRxiv2021

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model demonstrates that even otherwise rigorous studies can be hard to reproduce due to missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing rationales behind equations. Many of these issues seem similar to problems that have been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow is independent of the model and can be translated to SBML, CellML, and other languages. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. We found the additional design and documentation effort well justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature. Author summaryReproducibility is one of the cornerstones of the scientific method. In order to draw reliable conclusions, an experiment must yield the same results when it is repeated using the same methods. However, biological systems are complex, which makes experiments cumbersome. It is therefore desirable to build a mathematical representation of the biological system, which captures its essential behavior in a set of variables and equations and allows for easier and faster experimentation. Unfortunately, recent studies have shown that half of the published mathematical models are not immediately reproducible due to missing information, mathematical errors, and incomplete documentation. These issues are similar to those faced in software engineering: A single missing file or a buggy line of code can render any kind of software useless. Software engineering has turned to rigorous software testing, automated development pipelines, and version control systems to overcome these challenges, but these techniques are not yet widely applied to mathematical modeling. In this paper we demonstrate their benefit for the reproducibility of a large mathematical model of the atrioventricular node. The software engineering solutions that we employ can be applied to any mathematical model and could therefore facilitate scientific progress by encouraging and simplifying model reuse.

systems biology↗
bioRxiv2023

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

Biological function in protein complexes emerges from more than just the sum of their parts: Molecules interact in a range of different subcomplexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. Here we present the Bioconductor package BioNAR which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the networks vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a proteins impact within multiple complexes, and enables estimation of the co-occurrence of meta-data, i.e., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. The package is available from Bioconductor release 3.16: https://bioconductor.org/packages/release/bioc/html/BioNAR.html Author BiographiesColin McLean holds a PhD in Experimental Particle Physics from the University of Edinburgh. He is currently a Senior Research Fellow in Health Economics and Data Science at the Institute for Genetics and Cancer at the University of Edinburgh. His research interests include applied network and data science in the biomedical domain. Anatoly Sorokin holds PhD Degree in Biophysics and is a Senior computational biologist in the Biological Systems Unit, Okinawa Institute of Science and Technology. His research interests include graph-based analysis, constraint-base, dynamics and rule-based modelling and application areas include systems biology, bioinformatics and microbiomics. Ian Simpson has a DPhil in Genetics (Oxford 2000) and is currently Director of the UKRI Centre for Doctoral Training in Biomedical Artificial Intelligence and a Reader in Biomedical Informatics in the School of Informatics at The University of Edinburgh. His research interests lie at the boundary between Informatics and Biomedicine and focus on jointly modelling molecular and clinical data to improve our understanding of genetic disease. J Douglas Armstrong holds a PhD in Molecular Genetics (Glasgow 1995) and is currently Professor of Systems Neurobiology at the School of Informatics at Edinburgh University. His research interests focus on structure/function mapping in the brains of model organisms. Oksana Sorokina holds a PhD in Systems Biology (Edinburgh 2010) and is a Senior Researcher at the School of Informatics at Edinburgh University. Her expertise is in the computational analysis of complex datasets primarily proteomics and the integration of genetic and other omic data types to understand molecular complexes at the systems biology level.

bioinformatics↗
bioRxiv2025

Bootstrap-based criteria for identifying differences between learned Bayesian networks

Bayesian networks provide a powerful framework for learning dependencies from data, and they are widely used to probe structure in biological systems. Biological systems are governed by complex networks of interactions, and uncovering these interactions and comparing them across conditions is central to understanding biological mechanisms. However, when comparing Bayesian networks, it can be difficult to determine whether observed differences are substantial enough to reflect genuine differences in the underlying systems generating the data. Here, we address this by developing bootstrap-based criteria for identifying such differences and demonstrate their performance using simulated data from synthetic Bayesian networks. Both edge-level and whole-network connectivity comparisons reliably identified when underlying networks differed, even when this involved only 5% of edges, while distinguishing these differences from sampling variation. However, even with large datasets, the criteria were unable to recover specific edge differences. Thus distinguishing that networks differed was possible, but not the specific ways they differed. These criteria establish a framework for more robust and standardised Bayesian network comparisons, with broad potential for real-world applications.

systems biology↗