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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 991 records · Page 55Linked to original sources

bioRxiv2022

From toxic waste to beneficial nutrient: acetate boosts Escherichia coli growth at low glycolytic flux

Acetate, a major by-product of glycolytic metabolism in Escherichia coli and many other microorganisms, has long been considered a toxic waste compound that inhibits microbial growth. This counterproductive auto-inhibition represents a major problem in biotechnology and has puzzled the scientific community for decades. Recent studies have revealed that acetate is also a co-substrate of glycolytic nutrients and a global regulator of E. coli metabolism and physiology. Here, we used a systems biology strategy to investigate the mutual regulation of glycolytic and acetate metabolism. Computational and experimental results demonstrate that reducing the glycolytic flux enhances co-utilization of acetate with glucose. Acetate metabolism thus compensates for the reduction in glycolytic flux and eventually buffers carbon uptake so that acetate, far from being toxic, actually enhances E. coli growth under these conditions. We validated this mechanism using three orthogonal strategies: chemical inhibition of glucose uptake, glycolytic mutant strains, and alternative substrates with a natively low glycolytic flux. Acetate makes E. coli more robust to glycolytic perturbations and is a valuable nutrient, with a beneficial effect on microbial growth.

systems biology↗

bioRxiv2026

A Fine-Tuned Phosphatidylinositol Profile Contributes to Colonocyte Differentiation and Malignization: Evidence From Integrated Omics

Membrane lipid composition changes concomitantly with human colonocyte differentiation, a tightly regulated process occurring along the colon crypt. This process is heavily disrupted in colon cancer. Nonetheless, the regulatory mechanisms driving these changes, especially the replacement of arachidonic acid phosphatidylinositol species with monounsaturated fatty acid species, and how they are altered in cancer, remain unknown. To establish the transcriptional networks underlying this remodeling, we integrated transcriptomic and lipidomic profiles of isolated healthy and tumor human colonocytes using system biology approaches; identifying key gene regulatory networks involved in arachidonic acid and eicosanoid metabolism and phosphatidylinositol cycle as significant regulators during differentiation. Consistently, a distinct impact was found on organoid differentiation depending on colonocyte subtype and specific prostaglandin. Remarkably, the shift and associated transcriptomic programs were lost in tumor that heightened phosphoinositide metabolism. Altogether, these results underscore the importance of lipid remodeling in colonocyte stemness maintenance and proper onset of differentiation programs.

systems biology↗

bioRxiv2019

A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks

The metabolism of the causative agent of TB, Mycobacterium tuberculosis (Mtb) has recently re-emerged as an attractive drug target. A powerful approach to study Mtb metabolism is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of the many individual components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between Mtb genotype and phenotype. However, their utility is hampered by the existence of multiple models of varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community.

systems biology↗

bioRxiv2021

BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

As the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop GSMMs for poorly described organisms. However, such models quality can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. The Biological networks constraint-based In Silico Optimization (BioISO) is a computational tool aimed at accelerating the reconstruction of Genome-Scale Metabolic Models. This tool facilitates the manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased using GSMMs available in literature and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). Furthermore, BioISO was used as Menecos gap-finding algorithm to reduce the number of proposed solutions (reaction sets) for filling the gaps. BioISO was implemented as a webserver available at https://bioiso.bio.di.uminho.pt; and integrated into merlin as a plugin. BioISOs implementation as a Python package can also be retrieved from https://github.com/BioSystemsUM/BioISO.

systems biology↗

bioRxiv2026

Systems-level proteomic reprogramming reveals mitochondrial restoration and inhibition of Rho GTPase-mediated cytoskeletal and inflammatory signaling in CKD

Chronic kidney disease (CKD) is a progressive disorder characterized by metabolic dysfunction, mitochondrial impairment, oxidative stress, and chronic inflammation, ultimately leading to irreversible renal damage. Despite advances in understanding CKD pathophysiology, effective therapies targeting these interconnected molecular processes remain limited. In this study, we performed a comprehensive data-independent acquisition (DIA)-based proteomic analysis to investigate the molecular alterations associated with CKD and to evaluate the therapeutic impact of DVA treatment. Using a CKD model with three treatment conditions (DVA, KY, and DVA+KY) alongside disease and healthy controls, we quantified global proteomic changes and applied statistical filtering (fold change [≥]2, p [≤]0.05) followed by K-means clustering (k=10). Distinct protein clusters revealed bidirectional modulation upon DVA treatment. Notably, Cluster 1 comprised proteins downregulated in CKD but significantly restored following DVA administration, while Cluster 2 included proteins elevated in CKD that were suppressed by DVA. Pathway enrichment and network analyses demonstrated that Cluster 1 proteins were predominantly associated with mitochondrial function, oxidative phosphorylation, and metabolic processes, whereas Cluster 2 proteins were enriched in immune signaling, oxidative stress, cytoskeletal remodeling, and proteostasis pathways. At the molecular level, DVA treatment restored key mitochondrial and metabolic regulators, including components of the electron transport chain (e.g., COX5A, NDUFS5, SDHB) and redox homeostasis proteins, indicating recovery of cellular bioenergetics. Concurrently, DVA suppressed inflammatory mediators (STAT2, IFI47, GBP2), oxidative stress-related proteins (CYBB, PRDX5), and cytoskeletal regulators linked to renal injury (ARHGEF12, FMNL2). Network and Reactome analyses further confirmed coordinated modulation of interconnected biological systems rather than isolated protein changes. Collectively, our findings demonstrate that DVA exerts a dual therapeutic effect by restoring essential mitochondrial and metabolic pathways while simultaneously suppressing inflammation, oxidative stress, and cytoskeletal dysregulation in CKD. This systems-level proteomic reprogramming highlights DVA as a promising candidate for CKD intervention and provides mechanistic insights into disease progression and therapeutic targeting.

systems biology↗

bioRxiv2023

Using interactive platforms to encode, manage and explore immune-related adverse outcome pathways

We address the need for modelling and predicting adverse outcomes in immunotoxicology to improve non-clinical assessments of immunomodulatory therapy safety and efficacy. The integrated approach includes, first, the adverse outcome pathway concept established in the toxicology field, and, second, the systems medicine disease map approach for describing molecular mechanisms involved in a particular pathology. The proposed systems immunotoxicology workflow is demonstrated with CAR T cell treatment as a use case. To this end, the linear adverse outcome pathway (AOP) is expanded into a molecular interaction model in standard systems biology formats. Then it is shown how knowledge related to immunotoxic events can be integrated, encoded, managed and explored to benefit the research community. The map is accessible online via the MINERVA Platform for browsing, commenting and data visualisation (https://minerva.pages.uni.lu). Our work transforms a graphical illustration of an AOP into a digitally structured and standardised form, featuring precise and controlled vocabulary and supporting reproducible computational analyses. Because of annotations to source literature and databases, the map can be further expanded to match the evolving knowledge and research questions.

systems biology↗

bioRxiv2019

ETFL: A formulation for flux balance models accounting for expression, thermodynamics, and resource allocation constraints

Since the introduction of metabolic models and flux balance analysis (FBA) in systems biology, several attempts have been made to add expression data. However, directly accounting for enzyme and mRNA production in the mathematical programming formulation is challenging because of macromolecules, which introduces a bilinear term in the mass-balance equations that become harder to solve than linear formulations like FBA. Furthermore, there have been no attempts to include thermodynamic constraints in these formulations, which would yield an even more complex mixed-integer non-linear problem.\n\nWe propose here a new framework, called Expression and Thermodynamics Flux (ETFL), as a new ME-model implementation. ETFL is a top-down model formulation, from metabolism to RNA synthesis, that simulates thermodynamic-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. The formulation results in a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. The proposed formulation is compatible with mainstream MILP solvers and does not require a non-linear solver. It also accounts for growth-dependent parameters, such as relative protein or mRNA content.\n\nWe present here the formulation of ETFL along with its validation using results obtained from a well-characterized E. coli model. We show that ETFL is able to reproduce proteome-limited growth, which FBA cannot. We also subject it to different analyses, including the prediction of feasible mRNA and enzyme concentrations in the cell, and propose ETFL-based adaptations of other common FBA-based procedures.\n\nThe software is available on our public repository at https://github.com/EPFL-LCSB/etfl.\n\nAuthor summaryMetabolic modeling is a useful tool for biochemists who want to tweak biological networks for the direct expression of key products, such as biofuels, specialty chemicals, or drug candidates. To provide more accurate models, several attempts have been made to account for protein expression and growth-dependent parameters, key components of biological networks, though this is computationally challenging, especially when also attempting to include thermodynamics. To the best of our knowledge, there is no published methods integrating these three types of constraints in one model. We propose here a transparent mathematical formulation to model both expression and metabolism of a cell, along with a reformulation that allows a computationally tractable inclusion of growth-dependent parameters and thermodynamics. We demonstrate good performance using community-standard software, and propose ways to adapt classical modeling studies to expression-enabled models. The incorporation of thermodynamics and growth-dependent variables provide a finer modeling of expression because they eliminate thermodynamically unfeasible solutions and consider phenotypic differences in different growth regimens, which are key for accurate modeling.

systems biology↗

bioRxiv2020

Unraveling Oxidative Stress Resistance: Molecular Properties Govern Proteome Vulnerability

AbstractOxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS-susceptibility essential. The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by {gamma}-irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS-susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS-resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein-intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties. One Sentence SummaryProteins differ in intrinsic susceptibility to oxidation, a mode of evolutionary adaptation for stress tolerance in bacteria.

systems biology↗

bioRxiv2026

Metabolic-Epigenetic Coupling in Cellular Senescence: In Silico Cristae Remodeling Depletes Alpha-Ketoglutarate to Drive KDM4/6 Inhibition and SASP Amplification

Cellular senescence is governed by complex organellar cross-talk, epigenetic remodeling, and non-canonical signaling cascades that drive chronic tissue degeneration. In this work, we formulate a predictive computational systems biology framework to investigate these dynamics. Specifically, Senescence-associated mitochondrial cristae reorganization and metabolic rewiring toward aerobic glycolysis deplete the mitochondrial alpha-ketoglutarate pool relative to 2-hydroxyglutarate and succinate, inhibiting JmjC-domain containing histone demethylases (KDM4/KDM6) and locking chromatin in an open H3K4me3/H3K27ac transcriptional state that autonomously amplifies pro-inflammatory SASP expression independently of persistent DNA damage signaling. By integrating high-dimensional multi-omics cohorts, molecular docking regressions, and dynamic pathway modeling, our results indicate that this signaling axis acts as an autonomous amplification loop of senescence-associated phenotypic decline. Furthermore, targeted in silico screening identifies candidate nodal regulators capable of restoring homeostatic flux. These findings provide an epistemic and computational foundation for selective geroprotective intervention strategies.

systems biology↗

bioRxiv2022

Identifying a developmental transition in honey bees using gene expression data

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.

systems biology↗

bioRxiv2025

DASH: A versatile and high-capacity gene stacking system for plant synthetic biology

DNA assembly systems based on the Golden Gate method are popular in synthetic biology but have several limitations: small insert size, incompatibility with other cloning platforms, DNA domestication requirement, generation of fusion scars, and lack of post-assembly modification. To address these obstacles, we present the DASH assembly toolset, which combines features of Golden Gate-based cloning, recombineering, and site-specific recombinase systems. We developed (1) a set of donor vectors based on the GoldenBraid platform, (2) an acceptor vector derived from the plant transformation-competent artificial chromosome (TAC) vector, pYLTAC17, and (3) a re-engineered recombineering-ready E. coli strain, CZ105, based on SW105. The initial assembly steps are carried out using the donor vectors following standard GoldenBraid assembly procedures. Importantly, existing parts and transcriptional units created using compatible Golden Gate-based systems can be transferred to the DASH donor vectors using standard single-tube restriction/ligation reactions. The cargo DNA from a DASH donor vector is then efficiently transferred in vivo in E. coli into the acceptor vector by the sequential action of a rhamnose-inducible phage-derived PhiC31 integrase and arabinose-inducible yeast-derived Flippase (FLP) recombinase using CZ105. Furthermore, recombineering-based post-assembly modification, including the removal of undesirable scars, is greatly simplified. To demonstrate the utility of the DASH system, a 116 kb DNA construct harboring a 97 kb cargo consisting of 35 transcriptional units was generated. One of the CDSs in the final assembly was replaced through recombineering, and the in planta functionality of the entire construct was tested in both transient and stable transformants.

synthetic biology↗

bioRxiv2024

A global resource constrained model to predict metabolic flux dynamics in fluctuating environments

Environmental changes often induce global variations on bacterial gene expression, frequently accompanied by alterations in growth rates. Integrating non-metabolic cellular processes, such as gene expression and macromolecule synthesis, with metabolic modeling remains a significant challenge in systems biology due to the lack of mechanistic representation of gene regulation. Here, we introduce a novel constraint-based modeling framework called dynamic Constrained Allocation Flux Balance Analysis (dCAFBA), which comprehensively integrates metabolism, cellular resource allocation, and gene regulation. We employ a quasi-steady-state assumption, positing that reaction fluxes achieve balance at each time step, adapting more rapidly than protein synthesis and growth dilution. This approach enables the prediction of reaction flux dynamics and protein allocation necessary to achieve cellular objectives, such as optimizing growth during various growth shifts (including carbon, amino acid, and transcriptional signal), without detailing molecular gene regulations. Our model offers a new method for interpreting proteome allocation and cellular metabolism in complex and transient environments, providing mechanistic insights valuable for metabolic engineering.

systems biology↗

bioRxiv2024

Dynamic modelling of signalling pathways when ODEsare not feasible

MotivationMathematical modelling plays a crucial role in understanding inter- and intracellular signalling processes. Currently, ordinary differential equations (ODEs) are the predominant approach in systems biology for modelling such pathways. While ODE models offer mechanistic interpretability, they also suffer from limitations, including the need to consider all relevant compounds, resulting in large models difficult to handle numerically and requiring extensive data. ResultsIn previous work, we introduced the retarded transient function (RTF) as an alternative method for modelling temporal responses of signalling pathways. Here, we extend the RTF approach to integrate concentration or dose-dependencies into the modelling of dynamics. With this advancement, RTF modelling now fully encompasses the application range of ordinary differential equation (ODE) models, which comprises predictions in both time and concentration domains. Moreover, characterizing dose-dependencies provides an intuitive way to investigate and characterize signalling differences between biological conditions or cell-types based on their response to stimulating inputs. To demonstrate the applicability of our extended approach, we employ data from time- and dose-dependent inflammasome activation in bone-marrow derived macrophages (BMDMs) treated with nigericin sodium salt. Our results show the effectiveness of the extended RTF approach as a generic framework for modelling dose-dependent kinetics in cellular signalling. The approach results in intuitively interpretable parameters that describe signal dynamics and enables predictive modelling of time- and dose-dependencies even if only individual cellular components are quantified. AvailabilityThe presented approach is available within the MATLAB-based Data2Dynamics modelling toolbox at https://github.com/Data2Dynamics and https://zenodo.org/records/14008247 and as R code at https://github.com/kreutz-lab/RTF. Contactclemens.kreutz@uniklinik-freiburg.de

systems biology↗

bioRxiv2020

Cooperative molecular networks drive a mammalian cell state transition

In the mammalian embryo, epiblast cells must exit their naive state and acquire formative pluripotency. This cell state transition is recapitulated by mouse embryonic stem cells (ESCs), which undergo pluripotency progression in defined conditions in vitro. However, our understanding of the molecular cascades and gene-networks involved in the exit from naive pluripotency remains fragmented. Here we employed a combination of genetic screens in haploid ESCs, CRISPR/Cas9 gene disruption, large-scale transcriptomics and computational systems-biology to delineate the regulatory circuits governing naive state exit. Transcriptome profiles for 73 knockout ESC lines predominantly manifest delays on the trajectory from naive to formative epiblast. We find that gene networks operative in ESCs are active during transition from pre- to post-implantation epiblast in utero. We identified 374 naive-associated genes tightly connected to epiblast state and largely conserved in human ESCs and primate embryos. Integrated analysis of mutant transcriptomes revealed funneling of multiple gene activities into discrete regulatory modules. Finally, we delineate how intersections with signaling pathways direct this pivotal mammalian cell state transition.

systems biology↗

bioRxiv2024

Extracellular Respiration is a Latent Energy Metabolism in Escherichia coli

Diverse microbial species utilize redox shuttles to exchange electrons with their environment through mediated extracellular electron transfer (EET). This process maintains redox homeostasis and supports anaerobic survival across diverse microbial communities. Although mediated EET has been extensively leveraged for bioelectrocatalysis and bioelectronics for decades, fundamental questions remain about how these redox shuttles are reduced within cells and their bioenergetic implications. This knowledge gap limits our understanding of the physiological roles of mediated EET in various microbes and hampers the development of efficient microbial electrochemical technologies. To address this, we developed a methodology integrating genome editing, electrochemistry, and systems biology to investigate the mechanism and bioenergetic implications of mediated EET in bacteria. Using this approach, we uncovered a mediated EET mechanism in Escherichia coli. In the absence of alternative electron sinks, the redox cycling of 2-hydroxy-1,4-naphthoquinone (HNQ) via a cytoplasmic nitroreductase enabled E. coli to respire and grow on an extracellular electrode. Genome-scale metabolic modeling suggested that HNQ- mediated EET offers a more energetically favorable route for supporting anaerobic growth than canonical fermentation. Transcriptome analysis revealed redox perturbations in response to HNQ and identified rapid metabolic adaptations that support growth. This work demonstrates that E. coli can grow independently of classical electron transport chains and fermentative pathways, unveiling a new type of anaerobic energy metabolism.

systems biology↗

bioRxiv2023

Repository of logically consistent real-world Boolean network models

Recent developments in both computational analysis and data-driven synthesis enable a new era of automated reasoning with logical models (Boolean networks in particular) in systems biology. However, these advancements also motivate an increased focus on quality control and performance comparisons between tools. At the moment, to illustrate real-world applicability, authors typically test their approaches on small sets of manually curated models that are inherently limited in scope. This further complicates reuse and comparisons, because benchmark models often contain ad hoc modifications or are outright not available. In this paper, we describe a new, comprehensive, open source dataset of 210+ Boolean network models compiled from available databases and a literature survey. The models are available in a wide range of formats. Furthermore, the dataset is accompanied by a validation pipeline that ensures the integrity and logical consistency of each model. Using this pipeline, we identified and repaired 400+ potential problems in a number of widely used models.

systems biology↗

bioRxiv2024

Modelling the spatiotemporal dynamics of senescent cells in wound healing, chronic wounds, and fibrosis

Cellular senescence is known to drive age-related pathology through the senescence-associated secretory phenotype (SASP). However, it also plays important physiological roles such as cancer suppression, embryogenesis and wound healing. Wound healing is a tightly regulated process which when disrupted results in conditions such as fibrosis and chronic wounds. Senescent cells appear during the proliferation phase of the healing process where the SASP is involved in maintaining tissue homeostasis after damage. Interestingly, SASP composition and functionality was recently found to be temporally regulated, with distinct SASP profiles involved: a fibrogenic, followed by a fibrolytic SASP, which could have important implications for the role of senescent cells in wound healing. Given the number of factors at play a full understanding requires addressing the multiple levels of complexity, pertaining to the various cell behaviours, individually followed by investigating the interactions and influence each of these elements have on each other and the system as a whole. Here, a systems biology approach was adopted whereby a multi-scale model of wound healing that includes the dynamics of senescent cell behaviour and corresponding SASP composition within the wound microenvironment was developed. The model was built using the software CompuCell3D, which is based on a Cellular Potts modelling framework. We used an existing body of data on healthy wound healing to calibrate the model and validation was done on known disease conditions. The model provides understanding of the spatiotemporal dynamics of different senescent cell phenotypes and the roles they play within the wound healing process. The model also shows how an overall disruption of tissue-level coordination due to age-related changes results in different disease states including fibrosis and chronic wounds. Further specific data to increase model confidence could be used to explore senolytic treatments in wound disorders.

systems biology↗

bioRxiv2021

Network- and Enrichment-based Inference of Phenotypes and Targets from large-scale Disease Maps

Disease maps have emerged as computational knowledge bases for exploring and modeling diseasespecific molecular processes. By capturing molecular interactions, disease-associated processes, and phenotypes in standardized representations, disease maps provide a platform for applying bioinformatics and systems biology approaches. Applications range from simple map exploration to algorithm-driven target discovery and network perturbation. The web-based MINERVA environment for disease maps provides a platform to develop tools not only for mapping experimental data but also to identify, analyze and simulate disease-specific regulatory networks. We have developed a MINERVA plugin suite based on network topology and enrichment analyses that facilitate multi-omics data integration and enable in silico perturbation experiments on disease maps. We demonstrate workflows by analyzing two RNA-seq datasets on the Atlas of Inflammation Resolution (AIR). Our approach improves usability and increases the functionality of disease maps by providing easy access to available data and integration of selfgenerated data. It supports efficient and intuitive analysis of omics data, with a focus on disease maps.

systems biology↗