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Results for “systems biology”

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bioRxiv2019

A Systems Biology perspective of Dynamical Patterning Modules in the transition to multicellularity: lessons from an aggregative bacteria

Development and evolution are dynamical processes under the continuous control of organismic and environmental factors. Generic physical processes, associated with biological materials and certain genes or molecules, provide a morphological template for the evolution and development of organism forms. Generic dynamical behaviors, associated with recurring network motifs, provide a temporal template for the regulation and coordination of biological processes. The role of generic physical processes and their associated molecules in development is the topic of the Dynamical Patterning Module (DPM) framework. The role of generic dynamical behaviors in biological regulation is studied via the identification of the associated Network Motifs (NM). We propose a joint DPM-NM perspective on the emergence and regulation of multicellularity focusing on a multicellular aggregative bacterium, Myxococcus xanthus. Understanding M. xanthus development as a dynamical process embedded in a physical substrate provides novel insights into the interaction between developmental regulatory networks and generic physical processes in the evolutionary transition to multicellularity.

systems biology↗

bioRxiv2023

BondGraphs.jl: Composable energy-based modelling in systems biology

SummaryBondGraphs.jl is a Julia implementation of bond graphs. Bond graphs provide a modelling framework that describes energy flow through a physical system and by construction enforce thermodynamic constraints. The framework is widely used in engineering and has recently been shown to be a powerful approach for modelling biology. Models are mutable, hierarchical, multi-scale, multi-physics, and BondGraphs.jl is compatible with the Julia modelling ecosystem. Availability and ImplementationBondGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/jedforrest/BondGraphs.jl. Contactpan.m@unimelb.edu.au, mstumpf@unimelb.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2019

Correcting for sparsity and non-independence in glycomic data through a system biology framework

Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the roles of glycans, but it remains challenging to properly analyze large glycomics datasets, since the data are sparse (each sample often has only a few measured glycans) and detected glycans are non-independent (sharing many intermediate biosynthetic steps). We address these challenges with GlyCompare, a glycomic data analysis approach that leverages shared biosynthetic pathway intermediates to correct for sparsity and non-independence in glycomics. Specifically, quantities of measured glycans are propagated to intermediate glycan substructures, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we studied diverse N-glycan profiles from glycoengineered erythropoietin. We obtained biologically meaningful clustering of mutant cell glycoprofiles and identified knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyzed human milk oligosaccharide profiles and identified novel impacts that the mothers secretor-status on fucosylation and sialylation. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data.

systems biology↗

bioRxiv2021

Implementation and acceleration of optimal control for systems biology

Optimal control theory provides insight into complex resource allocation decisions. The forward-backward sweep method (FBSM) is an iterative technique commonly implemented to solve two-point boundary value problems (TPBVPs) arising from the application of Pontryagins Maximum Principle (PMP) in optimal control. In this review we discuss the PMP approach to optimal control and the implementation of the FBSM. By conceptualising the FBSM as a fixed point iteration process, we leverage and adapt existing acceleration techniques to improve its rate of convergence. We show that convergence improvement is attainable without prohibitively costly tuning of the acceleration techniques. Further, we demonstrate that these methods can induce convergence where the underlying FBSM fails to converge. All code used in this work to implement the FBSM and acceleration techniques is available on GitHub at https://github.com/Jesse-Sharp/Sharp2021.

systems biology↗

bioRxiv2022

Free and interfacial boundaries in individual-based models of multicellular biological systems

Coordination of cell behaviour is key to a myriad of biological processes including tissue morphogenesis, wound healing, and tumour growth. As such, individual-based computational models, which explicitly describe inter-cellular interactions, are commonly used to model collective cell dynamics. However, when using individual-based models, it is unclear how descriptions of cell boundaries affect overall population dynamics. In order to investigate this we define three cell boundary descriptions of varying complexities for each of three widely used off-lattice individual-based models: overlapping spheres, Voronoi tessellation, and vertex models. We apply our models to multiple biological scenarios to investigate how cell boundary description can influence tissue-scale behaviour. We find that the Voronoi tessellation model is most sensitive to changes in the cell boundary description with basic models being inappropriate in many cases. The timescale of tissue evolution when using an overlapping spheres model is coupled to the boundary description. The vertex model is demonstrated to be the most stable to changes in boundary description, though still exhibits timescale sensitivity. When using individual-based computational models one should carefully consider how cell boundaries are defined. To inform future work, we provide an exploration of common individual-based models and cell boundary descriptions in frequently studied biological scenarios and discuss their benefits and disadvantages.

biophysics↗

bioRxiv2025

XomicsDB: a database of paired multi-omics datasets for systems biology

Life science research is primarily driven by data integration on the basis of theoretical development. Broad applications of multiomics (xomics) data calls for efficient integration of xomics data to infer cellular operation and regulation. To that end, it is urgently needed good-quality xomics data should be curated and shared for the community. Here we present XomicsDB, a database curating paired xomics datasets at both bulk and single-cell levels in model organisms. It contains 183 datasets from 13 model organisms, with a total of seven xomics data types. Users can download these datasets individually or in a batch from the ftp server provided. Users can also upload their paired xomics datasets for sharing. Incoporation of xomics data intom GSMs are demonstrated in model organisms Escherichia coli and human. Future efforts will be on collecting more single-cell xomics data and curating non-model oragnisms. The XomicsDB can be found at: http://xomicsdb.com.

systems biology↗

bioRxiv2025

A systems biology approach to evaluate potential probioticcandidates for womens vaginal health

Probiotic supplements are marketed for diverse health benefits, yet species inclusion often lacks functional rationale. We surveyed 352 U.S. probiotic products and found 36 unique microbial species, with most supplements containing only one species and no clear link between species and intended health benefit. To evaluate probiotic function, we developed CoPaPro, a collection of 1,012 genome-scale metabolic models spanning commensal, pathogenic, and probiotic bacteria. Flux balance analysis revealed that current probiotic species fail to capture the metabolic diversity of native commensals. Focusing on vaginal health, we identified commensals with metabolic profiles overlapping Gardnerella vaginalis, a key pathobiont. In vitro spent media assays using 11 vaginal isolates showed variable inhibition of G. vaginalis, primarily driven by D-lactic acid production rather than metabolic similarity. Several non-Lactobacillus species produced inhibitory levels of D-lactate. These findings highlight the need for function-based probiotic design and demonstrate a scalable framework integrating metabolic modeling with experimental validation.

systems biology↗

bioRxiv2024

A systems-biology approach connects aging mechanisms with Alzheimer's disease pathogenesis

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimers disease, the most common neurodegenerative disorder, remain incompletely understood. To define relevant pathways, we integrated the results of an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila with human and Drosophila Alzheimers disease-associated multi-omics. We measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimers disease and identified Alzheimers disease human genetic variants that modify expression in disease-vulnerable neurons. We used a network optimization approach to integrate these data with previously published Alzheimers disease multi-omic data. We computationally predicted and experimentally demonstrated how HNRNPA2B1 and MEPCE enhance tau-mediated neurotoxicity. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimers disease.

systems biology↗

bioRxiv2020

Evolution from adherent to suspension - systems biology of HEK293 cell line development

The need for new safe and efficacious therapies has led to an increased focus on biologics produced in mammalian cells. The human cell line HEK293 has bio-synthetic potential for human-like production and is today used for manufacturing of several therapeutic proteins and viral vectors. Despite this increasing popularity there is still limited knowledge of the detailed genetic and composition of derivatives of this strain. Here we present a genomic, transcriptomic and metabolic gene analysis of six of the most widely used HEK293 cell lines. Changes in gene copy and expression between industrial progeny cell lines and the original HEK293 were associated with cellular component organization, cell motility and cell adhesion. Changes in gene expression between adherent and suspension derivatives highlighted switching in cholesterol biosynthesis and expression of five key genes (RARG, ID1, ZIC1, LOX and DHRS3), a pattern validated in 63 human adherent or suspension cell lines of other origin.

systems biology↗

bioRxiv2020

Listen to the noise: identifiability analysis for stochastic differential equation models in systems biology

Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly-adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically-motivated synthetic data and Markov-chain Monte Carlo (MCMC) methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github.

systems biology↗

bioRxiv2018

Integrated systems biology and imaging of the smallest free-living eukaryote Ostreococcus tauri

Ostreococcus tauri is an ancient phototrophic microalgae that possesses favorable genetic and cellular characteristics for reductionist studies probing biosystem design and dynamics. Here multimodal bioimaging and multi-omics techniques were combined to interrogate O. tauri cellular changes in response to variations in bioavailable nitrogen and carbon ratios. Confocal microscopy, stimulated Raman scattering, and cryo-soft x-ray tomography revealed whole cell ultrastructural dynamics and composition while proteomic and lipidomic profiling captured changes at the molecular and macromolecular scale.\n\nDespite several energy dense long-chain triacylglycerol lipids showing more than 40-fold higher abundance under N deprivation, only a few proteins directly associated with lipid biogenesis showed significant expression changes. However, the entire pathway for starch granule biosynthesis was highly upregulated suggesting much of the cellular energy is preferentially directed towards starch over lipid accumulation. Additionally, three of the five most downregulated and five of the ten most upregulated proteins during severe nitrogen depletion were unnamed protein products that warrant additional biochemical analysis and functional annotation to control carbon transformation dynamics in this smallest eukaryote.

systems biology↗

bioRxiv2023

Predictive Systems Biology Modeling: Unraveling Host Metabolic Disruptions and Potential Drug Targets in SARS-CoV-2 and Its Variants

BackgroundHost response is critical to the onset, progression, and outcome of viral infections. Since viruses hijack the host cellular metabolism for their replications, we hypothesized that restoring host cell metabolism can efficiently reduce viral production. ResultsHere, we present a viral-host Metabolic Modeling (vhMM) method to systematically evaluate the disturbances in host metabolism in viral infection and computationally identify targets for modulation by integrating genome-wide precision metabolic modeling and cheminformatics. We applied vhMM to SARS-CoV-2 infections and identified consistent changes in host metabolism and gene and endogenous metabolite targets between the original SARS-COV-2 and different variants (Alpha, Delta, and Omicron). Among six compounds predicted for repurposing, methotrexate, cinnamaldehyde, and deferiprone were tested in vitro and effective in inhibiting viral production with IC50 less than 4uM. Further, an analysis of real-world patient data showed that cinnamon usage significantly reduced the SARS-CoV-2 infection rate with an odds ratio of 0.65 [95%CI: 0.55[~]0.75]. ConclusionsThese results demonstrated that vhMM is an efficient method for predicting targets and drugs for viral infections.

systems biology↗

bioRxiv2018

System Biology Modeling with Compositional Microbiome Data Reveals Personalized Gut Microbial Dynamics and Keystone Species

A growing body of literature points to the important roles that different microbial communities play in diverse natural environments and the human body. The dynamics of these communities is driven by a range of microbial interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood, making it hard to predict the response of the community to different perturbations. With the increasing availability of high-throughput sequencing based community composition data, it is now conceivable to directly learn models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is however affected by several experimental limitations, particularly the compositional nature of sequencing data. We present a new computational approach (BEEM) that addresses this key limitation in the inference of generalised Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference in an expectation maximization like algorithm (BEEM). Surprisingly, BEEM outperforms state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEMs application to previously inaccessible public datasets (due to the lack of biomass data) allowed us for the first time to analyse microbial communities in the human gut on a per individual basis, revealing personalised dynamics and keystone species.

systems biology↗

bioRxiv2020

A comprehensive investigation of colorectal cancer progression, from the early to late-stage, a systems biology approach

Colorectal cancer is a widespread malignancy with a concerning mortality rate. It could be curable at the first stages, but the progress of the disease and reaching to the stage-4 could make shift the treatments from curative to palliative. In this stage, the survival rate is meager, and therapy options are limited. The question is, what are the hallmarks of this stage and what genes are involved? What mechanism and pathways could drive such a malign shift from stage-1 to stage-4? In this study, first we identified the core modules for both the stage-1 and stage-4 which four of them have a significant role in stage-1 and two of them have a role in stage-4. Then we investigated the gene ontology and hallmarks analysis for each stage. According to the results, the immune-related process, especially interferon-gamma, impacts stage-1 in colorectal cancer. Concerning stage-4, extracellular matrix ontologies, and metastatic hallmarks are in charge. At last, we performed a differentially expressed gene analysis of stage-4 vs. stage-1 and analyzed their pathways which reasonably undergone a hypo/hyperactivity or being abnormally regulated through the cancer progression. We found that lncRNA in canonical WNT signaling and colon cancer has the most significant pathways, followed by WNT signaling, which means that these pathways may be the driver for the development from early-stage to late-stage. Of these lncRNAs, we had two upregulated kind, H19, and HOTAIR, which both can be involved and mediate metastasis and invasion in colorectal cancer.

systems biology↗

bioRxiv2026

Human prion proteins and mutants through the lens of systems biology

Autocatalytic conformational changes of cellular prion proteins (PrPC) to the {beta}-sheet-enriched scrapie isoform (PrPSC) drive prion diseases, yet the precise molecular mechanisms remain unclear. To address this, we employ a multi-pronged framework integrating network-theoretic analyses, molecular dynamics (MD) simulations, and experimental validation to characterize critical residues governing human PrP (huPrP) structural integrity. Residue Interaction Networks (RINs) of: (A) wild-type (WT) huPrP, and, (B) five disease-associated point mutants (Met166Val, Glu220Lys, Glu200Lys, Asp178Asn, and Ser170Asn) were evaluated using three rigorous network methods: centrality measures, Shortest Path Alteration Fraction (SPAF), and Differential Networks (DN). Across all the diverse point mutations, five residues (Met206, Val209, Val210, Met213, and Cys214) consistently exhibit high centrality scores. SPAF and DN analyses demonstrate that alterations at these positions disrupt connectivity and increase the propensity for misfolding and protease resistance. To validate these topological findings, key residues within the C-terminal domain (121-230) were substituted via site-directed mutagenesis. Thermal denaturation monitored by circular dichroism (CD) spectroscopy reveals that the Met213Ala substitution significantly lowers the melting temperature, Tm, relative to the WT, whereas the Val210Ala variant unfolds with a Tm comparable to the WT. Furthermore, MD simulations of de novo AlphaFold-predicted mutant structures corroborate these findings. Mutants exhibit greater structural flexibility and deviation, with average root Mean Square Deviations rising from 0.18 nm (WT) to 0.32 nm (Cys214Ala), 0.29 nm (Met206Ala), and 0.25 nm (Val210Lys). This integration of theoretical and experimental techniques provides a robust framework for identifying critical determinants of protein stability.

systems biology↗

bioRxiv2020

Anti-cancer Potential of Moringa oleifera on BRCA1 Gene: Systems Biology

Breast Cancer has always been a global challenge that is prevalent among women. There is a continuous increase in the high number of women mortality rates as a result of breast cancer and affecting countries at all levels of modernization. Women with high-risk factors including hereditary, obesity, and menopause have the possibility of developing breast cancer. With the advent of radiotherapy, chemotherapy, hormone therapy, and surgery in the treatment of breast cancer, there has an increased number of breast cancer survivors. Also, the design and development of drugs targeting therapeutic enzymes are helping to effectively treat the tumor cells at an early stage. However, long term use of anti-cancer drugs has been linked to severe side effects. This research aims to develop potential drug candidates from Moringa oleifera which could serve as anti-cancer agents. In silico analysis using Schrodinger Molecular Drug Discovery Suite and SWISS ADME was employed to determine the therapeutic potential of phytochemicals from M. oleifera against breast cancer via molecular docking, pharmacokinetic parameters, and drug-like properties. The result shows that Rutin, Vicenin-2, and Quercetin-3-O-glucoside have the highest binding energy of -7.522, -6.808, -6.635kcal/mol respectively in the active site of BRCA1. The essential amino acids involved in the protein-ligand interaction following active site analysis are ASN 1678, ASN 1774, GLY 1656, LEU 1657, GLN 1779, LYS 1702, SER 1655, PHE 1662, ARG 1699, GLU 1698, and VAL 1654. Thus, we propose that bioactive compounds from M. oleifera may be potential hit drug candidates against breast cancer.

systems biology↗

bioRxiv2022

A Systems Biology Approach to Pathogenesis of Gastric Cancer: Gene Network Modeling and Pathway Analysis

IntroductionGastric cancer (GC) ranks among the most common malignancies worldwide. In our previous study, we found overexpressed genes in GC clinical samples. The goal of the current study was to find critical genes and key pathways involved in the pathogenesis of GC. MethodsGene interactions were analyzed using STRING, and Cytoscape was used to visualize the molecular interaction network. CytoHubba was used for drawing the PPI network and identifying hub proteins. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) at STRING were used for the enrichment analysis of the hub genes. Cluster analysis of the network was done using CytoCluster. MEME Suite was used for promoter analysis of the hub genes using Tomtom and GoMo tools. Results and DiscussionOur results showed that the most affected processes in GC are the metabolic processes. The OXPHOS pathway was also considerably enriched in our analyses. These results showed the significant role of mitochondria in GC pathogenesis. Although many investigations have focused on the mitochondrial role in the pathogenesis of various cancers, the characteristics of respiratory and metabolic changes in GC have not been fully elucidated. Our results also showed that most of the affected pathways in GC were the pathways also involved in neurodegenerative diseases. Also, promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. The results of this study might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic or prognostic biomarkers or potential therapeutic targets for GC. Nonetheless, these results were obtained by bioinformatics analysis and require further clinical validation.

systems biology↗

bioRxiv2024

Evolutionary Systems Biology Identifies Genetic Trade-offs In Rice Defense Against Above- and Belowground Attackers

Like other plants, wild and domesticated rice species (Oryza nivara, O. rufipogon, and O. sativa) evolve in environments with various biotic and abiotic stresses that fluctuate in intensity through space and time. Microbial pathogens and invertebrate herbivores such as plant-parasitic nematodes and caterpillars show geographical and temporal variation in activity patterns and may respond differently to certain plant defensive mechanisms. As such, plant interactions with multiple community members may result in conflicting selection pressures on genetic polymorphisms. Here, through assays with different above- and belowground herbivores, the fall armyworm (Spodoptera frugiperda) and the southern root-knot nematode (Meloidogyne incognita), respectively, and comparison with rice responses to microbial pathogens, we identify potential genetic trade-offs at the KSL8 and MG1 loci on chromosome 11. KSL8 encodes the first committed step towards biosynthesis of either stemarane- or stemodane-type diterpenoids through the japonica (KSL8-jap) or indica (KSL8-ind) allele. Knocking out KSL8-jap and CPS4, encoding an enzyme that acts upstream in diterpenoid synthesis, in japonica rice cultivars increased resistance to S. frugiperda and decreased resistance to M. incognita. Furthermore, MG1 resides in a haplotype that provided resistance to M. incognita, while alternative haplotypes are involved in mediating resistance to the rice blast fungus Magnaporthe oryzae and other pests and pathogens. Finally, KSL8 and MG1 alleles are located within trans-species haplotypes and may be evolving under long-term balancing selection. Our data are consistent with a hypothesis that polymorphisms at KSL8 and MG1 may be maintained through complex and diffuse community interactions.

systems biology↗