SEARCH · bioRxiv Science

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 217 records · Page 12Linked to original sources

bioRxiv2019

Citrusgreening.org: An open access and integrated systems biology portal for the Huanglongbing (HLB) disease complex

We have created an open access web portal with pathosystem-wide resources and bioinformatics tools for the host citrus, the vector Asian citrus psyllid (ACP) and multiple pathogens including Ca. Liberibacter asiaticus. To the best of our knowledge, this is the first example of a database to use the pathosystem as a holistic framework to understand an insect transmitted plant disease. This endeavor integrates and enables the analysis of data sets generated by the community to study the citrus greening disease complex. Users can submit relevant data sets to enable sharing and allow the community to better analyze their data within an integrated system. The portal contains a variety of tools for omics data. Metabolic pathway databases, CitrusCyc and DiaphorinaCyc provide organism specific pathways that can be used to mine metabolomics, transcriptomics and proteomics results to identify pathways and regulatory mechanism involved in disease response. Psyllid Expression Network (PEN) contains expression profiles of ACP genes from multiple life stages, tissues, conditions and hosts. Citrus Expression Network (CEN) contains public expression data from multiple tissues and conditions for various citrus hosts. All tools like Apollo/JBrowse, Biocyc, Blast, CEN and PEN connect to a central database containing gene models for citrus, ACP and multiple Liberibacter pathogens. The portal also includes electrical penetration graph (EPG) recordings of ACP feeding on citrus, information about citrus rootstock trials and metabolomics data in addition to traditional omics data types with a goal of combining and mining all information related to a pathosystem. The portal includes user-friendly manual curation tools to allow the research community to continuously improve this knowledge base as more experimental research is published. Bulk downloads are available for all genome and annotation datasets from the FTP site (ftp://ftp.citrusgreening.org). The portal can be accessed at https://citrusgreening.org/.

bioinformatics↗

bioRxiv2026

tTEscanR: A user-friendly integrative R package for quantifying and visualizing translation efficiency from sequencing data in diverse biological systems

Translation elongation relies on accurate codon-anticodon pairing. Here, we present tTEscanR, an R package designed to investigate this translational interface. By quantifying mRNA codon demand alongside tRNA anticodon availability, tTEscanR provides scalable estimates of translation rates directly from standard transcriptomic and chromatin accessibility count matrices, where genomic features are represented as rows and experimental conditions as columns. tTEscanR seamlessly integrates into existing bulk and single-cell pipelines and provides modular functions for quality control, filtering, normalization, statistical analysis, and visualization. A structured data object centralizes workflow outputs and associated metadata. A built-in multilevel visualization module generates customizable, publication-ready graphical outputs to facilitate data interpretation and reproducibility of the complex translational landscape. We demonstrate the utility of tTEscanR across cancer biology, aging, and neurodegeneration datasets, uncovering critical translational regulatory programs overlooked by conventional analyses. tTEscanR is available in an open-source repository as a standalone tool or workflow plug-in.

genomics↗

bioRxiv2021

DDN2.0: R and Python packages for differential dependency network analysis of biological systems

Data-driven differential dependency network analysis identifies in a complex and often unknown overall molecular circuitry a network of differentially connected molecular entities (pairwise selective coupling or uncoupling depending on the specific phenotypes or experimental conditions) (Herrington, et al. 2018; Zhang, et al., 2009; Zhang and Wang, 2010; Zhang, et al., 2016). Such differential dependency networks are typically used to assist in the inference of potential key pathways. Based on our previously developed Differential Dependency Network (DDN) method, we report here the fully implemented R and Python software tool packages for public use. The DDN2.0 algorithm uses a fused Lasso model and block-wise coordinate descent to estimate both the common and differential edges of dependency networks. The identified DDN can help to provide plausible interpretation of data, gain new insight of disease biology, and generate novel hypotheses for further validation and investigations. To address the imbalanced sample group problem, we propose a sample-size normalized formulation to correct systematic bias. To address high computational complexity, we propose four strategies to accelerate DDN2.0 learning. The experimental results show that new DDN2.0+ learning speed with combined four accelerating strategies is hundreds of times faster than that of DDN2.0 algorithm on medium-sized data (Fu, 2019). To detect intra-omics and inter-omics network rewiring, we propose multiDDN using a multi-layer signaling model to integrate multi-omics data. The simulation study shows that the multiDDN method can achieve higher accuracy of detecting network rewiring (Fu, 2019).

bioinformatics↗

bioRxiv2021

Identification of Key Ischemic Stroke Genes by Computational Systems Biology

Stroke is one of the leading causes of death in humans. Even if patients survive from stroke, they may suffer sequelae such as disability. Treatment for strokes remains unsatisfactory due to an incomplete understanding of its mechanisms. This study investigates Ischemic Stroke (IS), a primary subtype of stroke, through analyses based on microarray data. Limma (in R)derives differentially expressed genes, and the protein-protein interaction (PPI) network is mapped from the database. Gene co-expression patterns are obtained for clustering gene modules by the Weighted Correlation Network Analysis (WGCNA), and genes with high connectivity in the significantly co-expressed modules are selected as key regulators. Common hubs are identified as Cdkn1a, Nes and Anxa2. Based on our analyses, we hypothesize that these hubs might play a key role in the onset and progression of IS. Result suggests the potential of identifying unexplored key regulators by the systemic method used in this work. Further analyses aim at expanding candidate genes for screening biomarkers for IS, and experimental validation is required on identified potential hubs.

bioinformatics↗

bioRxiv2023

Exploration of the Link Between COVID-19 and Alcoholic Hepatitis from the Perspective of Bioinformatics and Systems Biology

ObjectiveSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been suggested to purpose threats to health of mankind. Alcoholic hepatitis (AH) is a life-threatening acute and chronic liver failure that takes place in sufferers who drink excessively. During the epidemic, AH has an increasing incidence of severe illness and mortality. However, for these two diseases, the intrinsic relationship of molecular pathogenesis, as well as common therapeutic strategies are still poorly understood. MethodsThe transcriptome of the COVID-19 and AH has been compared to obtain the altered genes and hub genes were screened out through protein-protein interaction (PPI) network analysis. Via gene ontology (GO), pathway enrichment and transcription regulator analysis, a deeper appreciation of the interplay mechanism between hub genes were established. ResultsWith 181 common differentially expressed genes (DEGs) of AH and COVID-19 were obtained, 10 hub genes were captured. Follow-up studies located that these 10 genes typically mediated the diseases occurrence by regulating the activities of the immune system. Other results suggest that the common pathways of the two ailments are enriched in regulating the function of immune cells and the release of immune molecules. ConclusionThis study reveals the common pathogenesis of COVID-19 and AH and assist to discover necessary therapeutic targets to combat the ongoing pandemic induced via SARS-CoV-2 infection and acquire promising remedy strategies for the two diseases.

bioinformatics↗

bioRxiv2022

Comparative genomics and integrated system biology approach unveiled undirected phylogeny patterns, mutational hot spots, functional patterns and molecule repurposing for monkey pox virus

Monkeypox is a viral zoonosis with symptoms that are reminiscent to those experienced in previous smallpox cases. GSAID databases (Global Initiative on Sharing Avian Influenza Data) was used to assess 630 genomes of MPXV. Six primary clades were inferred from the phylogenetic study, coupled with a lesser percentage in radiating clades. Individual clades that make up various nationalities may have formed as a result of a particular SNP hotspot type, which may have mutated in a particular population type. The most significant mutation, based on a mutational hotspot analysis, was found at G3729A and G5143A. The gene ORF138, which encodes the Ankyrin repeat (ANK) protein, was found to have the most mutations. This protein is known to mediate molecular recognition via protein-protein interactions. It was shown that 243 host proteins interacted with 10 monkeypox proteins identified as the hub proteins E3, SPI2, C5, K7, E8, G6, N2, B14, CRMB, and A41 through 262 direct connections. The interaction with chemokine system-related proteins provides further evidence that the human protein is being suppressed by the monkey pox virus in order to facilitate its survival against innate immunity. A few FDA-approved molecules were likely used as possible inhibitors after being researched for blocking F13, a significant envelope protein on the membrane of extracellular versions of virus. A total of 2500 putative ligands were docked individually with the F13 protein. The F13 protein and these molecules interaction may help prevent the monkey pox virus from spreading. As a result, after being confirmed by experiments, these putative inhibitors might have an impact on the activity of these proteins and be utilised in monkeypox treatments.

genomics↗

bioRxiv2025

Inverse signal importance in real exposome: How do biological systems dynamically prioritize multiple environmental signals?

Living organisms integrate multiple signals from their exposome, the totality of environmental influences experienced throughout life, to adapt to complex, non-stationary environments. While organisms are thought to flexibly prioritize relevant signals depending on context, its regulatory mechanisms remain largely unknown. Laboratory studies with precisely controlled conditions fail to capture this adaptability by isolating organisms from the complex exposome. Here, we developed a machine learning framework, Inverse Signal Importance (ISI), to infer how organisms prioritize external signals from time-series data of environmental factors and physiological responses. We applied ISI to analyze gonadal development in medaka fish under natural outdoor conditions, tracking gonadosomatic index alongside environmental signals including water temperature, day length, and solar radiation over two years. Our analysis revealed that signal importance levels exhibit complex dynamics distinct from simple environmental periodicity and correlates significantly with specific gene expression patterns. Notably, genes associated with temperature-related signal importance display differential expression between outdoor and controlled laboratory conditions, suggesting their role in environmental adaptation. These findings indicate that ISI effectively captures latent physiological dynamics in adaptation of exposome. By decomposing biological responses into deterministic and adaptive components, ISI provides a novel approach to uncover mechanisms of organismal adaptation in natural environments.

bioinformatics↗

bioRxiv2021

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic {beta}-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic {beta}-Cell Consortium. Significance StatementCells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic {beta}-Cell Consortium.

cell biology↗

bioRxiv2017

MicrobiomeDB: a systems biology platform for integrating, mining and analyzing microbiome experiments.

MicrobiomeDB (http://microbiomeDB.org) is a data discovery and analysis platform that empowers researchers to fully leverage experimental variables to interrogate microbiome datasets. MicrobiomeDB was developed in collaboration with the Eukaryotic Pathogens Bioinformatics Resource Center (http://EuPathDB.org) and leverages the infrastructure and user interface of EuPathDB, which allows users to construct in silico experiments using an intuitive graphical strategy approach. The current release of the database integrates microbial census data with sample details for nearly 14,000 samples originating from human, animal and environmental sources, including over 9,000 samples from healthy human subjects in the Human Microbiome Project (http://portal.ihmpdcc.org/). Query results can be statistically analyzed and graphically visualized via interactive web applications launched directly in the browser, providing insight into microbial community diversity and allowing users to identify taxa associated with any experimental covariate.

microbiology↗

bioRxiv2020

Application of dielectric spectroscopy in the dynamically changing biological system - case of Neocaridina davidi shrimp starvation

In this work, dielectric studies on Neocaridina davidi shrimps have been presented. The effect of starvation on dielectric properties such as conductivity or permittivity have been shown. It was found that the onset frequency of electrode polarization depends on starvation period, which is probably related to the cytoplasm viscosity. In the dielectric spectra of shrimps two relaxation processes have been identified i.e. α and β process. The α process is probably related to the counter ion polarization while β process to the mobility of macromolecules present in the body of a shrimp, mainly to the amount of lipids. It was also found that there is a difference in dielectric response between control group and the group regenerated after 14 day starvation period. Basing on dielectric response, one can conclude that the viscosity of cytoplasm of regenerated shrimps is higher and the cells are rich in the lipid droplets when compared to control group.View Full Text

physiology↗

bioRxiv2020

Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse-graining had been previously applied to experimental neural recordings, which showed over two decades of scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model the experiment by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally-observed scalings, suggesting that they may arise from coupling the neural population activity to latent dynamic stimuli. Further, parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to non-place stimuli.

biophysics↗

bioRxiv2021

A novel approach to the empirical characterization of learning in biological systems

Learning to execute precise, yet complex, motor actions through practice is a trait shared by most organisms. Here we develop a novel experimental approach for the comprehensive investigation and characterization of the learning dynamics of practiced motion. Following the dynamical systems framework, we consider a high-dimensional behavioral space in which a trial-by-trial sequence of motor outputs defines a trajectory that converges to a fixed point - the desired motor output. In this scenario, details of the internal dynamics and the trial-by-trial learning mechanism cannot be disentangled from behavioral noise for nonlinear systems or even well estimated for linear systems with many parameters. To overcome this problem, we introduce a novel approach: the sporadic application of systematic target perturbations that span the behavioral space and allow us to estimate the linearized dynamics in the vicinity of the fixed point. The steady-state Lyapunov equation then allows us to identify the noise covariance. We illustrate the method by analyzing sequence-generating neural networks with either intrinsic or extrinsic noise, at time resolutions that span from spike timing to spiking rates. We demonstrate the utility of our approach in experimentally plausible and realizable settings and show that this method can fully characterize the linearized between-trials learning dynamics as well as extract meaningful internal properties of the unknown mechanism that drives the motor output within each trial. We then illustrate how the approach can be extended to nonlinear learning dynamics through a flexible choice of the basis and magnitude of perturbations. Significance statementMovement control ties brain activity to measurable external actions in real time, providing a useful tool for both neuroscientists interested in the emergence of stable behavior and biomedical engineers interested in the design of neural prosthesis and brain-machine interfaces. We approach the question of motor skill learning by introducing artificial errors through a novel perturbative scheme amenable to analytic examination in the linearized regime close to the desired behavior. Numerical simulations then demonstrate how to probe the learning dynamics in both linear and nonlinear systems. These findings stress the usefulness of analyzing responses to deliberately induced errors and the importance of properly designing such perturbation experiments. Our approach provides a novel generic tool for monitoring the acquisition of motor skills.

neuroscience↗

bioRxiv2022

A systems biology approach to elucidate the post-translational regulome of coronary artery disease

Coronary Artery Disease is a major killer in India and world at large but the molecular regulators which modulate clinically relevant pathways are not completely understood. This study was aimed at identifying essential post-translational modifications (PTM) regulome network and its master regulator modulating the CAD associated pathways. 995 CAD associated genes were taken from InCardiome database (www.tri-incardiome.org) were analyzed for all possible PTMs. Two important interdependent molecular processes which define the function of a protein are protein-protein interactions and PTMs of which PTMs play regulatory role. Using PTMCode2 we evaluated the co-evolving amino acid residues for important PTMs and found that serine-serine phosphorylation is highly represented combinatorial regulator in these set of proteins. Furthermore, in the CAD associated pathways we again found that serine phosphorylation was dominant player in all the processes of atherosclerosis. In order to identify the master regulator kinase, we further assessed the kinome network associated with CAD and identified 5 most important kinases namely GSK3B, PRKCA, PRKCD, SRC and PRKACA which might modulate clinically important pathways. GSK3B with the highest network parameters (node degree and betweenness centrality) was identified as master regulator and 1 U/l increase of phsophoGSK3B (on a log scale) increased the odds ratio (OR) by 4.07 fold (AUC 0.620) and 6.27 fold (AUC 0.752) upon addition of conventional risk factors (CRFs).

synthetic biology↗

bioRxiv2022

Systems biology illuminates alternative metabolic niches in the human gut microbiome

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem.

microbiology↗

bioRxiv2023

AI-based novel-chemotype GPCRs drugs: introducing ligand type classifiers and systems biology

Identifying the correct chemotype of ligands targeting receptors (i.e., agonist or antagonist) is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinsons disease and cancer. The identified antagonists were tested here in a radioligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310{+/-}23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.

bioinformatics↗

bioRxiv2023

Molecular profiling of high-level athlete skeletal muscle after acute exercise - a systems biology approach

Life-long high-level exercise training leads to improvements in physical performance and multi-tissue adaptation following changes in molecular pathways. While skeletal muscle baseline differences between exercise-trained and untrained individuals have been previously investigated, it remains unclear how acute exercise multi-omics are influenced by training history. We recruited and extensively characterized 24 individuals categorized as endurance athletes, strength athletes or control subjects. Multi-omics profiling was performed from skeletal muscle before and at three time-points after endurance or resistance exercise sessions. Timeseries multi-omics analysis revealed distinct differences in molecular processes such as fatty- and amino acid metabolism and for transcription factors such as HIF1A and the MYF-family between both exercise history and acute form of exercise. Furthermore, we found a "transcriptional specialization effect" by transcriptional narrowing and intensification. Finally, we performed multi-omics network analysis and clustering, providing a novel resource of skeletal muscle transcriptomic and metabolomic profiling in highly trained and untrained individuals.

physiology↗

bioRxiv2024

An analysis of natural variation in Pinus pinaster through the lens of systems biology

Pinus pinaster is a main species in Mediterranean forests, being naturally distributed through the Mediterranean basin, covering regions with a variety of geoclimatic conditions. This distribution in different environments leads, by natural selection, to a natural variation within the species that manifests at phenotypic level in populations with different growth features and overall tree architectures. Studying P. pinaster natural variation is necessary to understand the genetic heritage of the species and can provide valuable information for information-based decision-making regarding forest management and breeding programmes. In this paper, we analyzed the natural variation in needles and buds from three provenances from contrasting geoclimatic locations using a common garden approach and proteomics. The integration of the proteomics data with tree growth-related parameters, geoclimatic features at provenances original locations, and sample-matched metabolomics data previously generated provided novel knowledge on metabolism rearrangements related to secondary metabolism and associated to growth features and the adaptation to light and UV-B radiation intensities.

plant biology↗

bioRxiv2024

Deciphering the Biochemical and Physiological Mechanisms of Mycobacterium Tuberculosis β-Carbonic Anhydrase 3 and Its Modulators through Mechanistic QSAR Model and Integrated Systems Biology Approaches

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↗