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bioRxiv2018

Mechanistic insights on metabolic dysfunction in PTSD: Role of glucocorticoid receptor sensitivity and energy deficit

PTSD is associated with metabolic comorbidities; however it is not clear how the neuroendocrine disturbances affect metabolism. To analyze this we employed a systems biological approach using an integrated mathematical model of metabolism, HPA axis and inflammation. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD, to characterize the differences in regulatory effects. We used the pattern of fold change in metabolites representing pathway level differences as reference for metabolic control analysis (MCA) using the model. MCA revealed parameters constituting the HPA axis, inflammation and GPCR pathway that yielded metabolic dysfunction consistent with PTSD. To support this, we performed causal analysis between regulatory components and the significantly different metabolites in our sample. Causal inference revealed that the changes in glucocorticoid receptor sensitivity were mechanistically associated with metabolic dysfunction and the effects were jointly mediated by insulin resistance, inflammation, oxidative stress and energy deficit.

systems biology↗

bioRxiv2018

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grown E. coli.

systems biology↗

bioRxiv2019

Systems-level physiology of the human red blood cell is computed from metabolic and macromolecular mechanisms

The human red blood cell has served as a starting point for the application and development of systems biology approaches due to its simplicity, intrinsic experimental accessibility, and importance in human health applications. Here, we present a multi-scale computational model of the human red blood cell that accounts for the full metabolic network, key proteins (>95% of proteome mass fraction), and several macromolecular mechanisms. Proteomics data are used to place quantitative constraints on individual protein complexes that catalyze metabolic reactions, as well as a total proteome capacity constraint. We explicitly describe molecular mechanisms--such as hemoglobin binding and the formation and detoxification of reactive oxygen species--and takes standard hematological variables (e.g., hematocrit, hemoglobin concentration) as input, allowing for personalized physiological predictions. This model is built from first principles and allows for direct computation of physiologically meaningful quantities such as the oxygen dissociation curve and an accurate computation of the flux state of the metabolic network. More broadly, this work represents an important step toward including the proteome and its function in whole-cell models of human cells.

systems biology↗

bioRxiv2019

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.\n\nTo deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.\n\nData available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/\n\nAuthor SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

systems biology↗

bioRxiv2022

scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data

Recent biotechnological advances led to growing numbers of single-cell perturbation studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, and data filtering. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we introduce E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single cell data. Using these datasets, we illustrate the application of E-statistics for quantifying perturbation similarity and efficacy. The data and a package for computing E-statistics is publicly available at scperturb.org. This work provides an information resource and guide for researchers working with single-cell perturbation data, highlights conceptual considerations for new experiments, and makes concrete recommendations for optimal cell counts and read depth.

systems biology↗

bioRxiv2022

BioNetGMMFit: a Parameter Estimation Tool for BioNetGen using Single-Cell Snapshot Data from Cell Populations Evolving over Time

BackgroundMechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. ResultsBioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, timestamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetG-MMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. ConclusionBy streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

systems biology↗

bioRxiv2025

Multi-modal atlas of lifestyle interventions reveals malleability of ageing-linked molecular features

Extending human healthspan requires understanding how lifestyle interventions impact molecular systems across tissues and time. Here, we present the TirolGESUND Lifestyle Atlas (ClinicalTrials.gov: NCT05678426), a longitudinal, multi-modal resource profiling 156 healthy women (aged 30-60 years) undergoing 6-month intermittent fasting (n=114) or smoking cessation (n=42) interventions. Participants were sampled up to four times across seven tissues and fluids, generating >3,450 biospecimens with harmonised DNA methylation, metabolomics, microbiome, and immune profiling, alongside skin histology, barrier measurements, and rich clinical metadata. We demonstrate the utility of this dataset through: (i) multi-omics-wide association studies linking traits to molecular features; (ii) integrative factor modelling revealing coordinated cross-tissue signatures; (iii) epigenetic-biomarker cross-omic associations, and (iv) CpG-level variance decomposition mapping stable, individual-specific, tissue-restricted, and intervention-responsive methylation patterns. We further show that ageing-linked features are selectively malleable: highly compliant intermittent fasting participants exhibited attenuated or even age-opposing molecular trajectories within six months. The atlas enables unprecedented within-cohort comparisons across omic layers and tissues, supporting discovery of context-dependent biomarkers, cross-system coordination, and intervention responsiveness. Data are available via an interactive portal, with sensitive data under controlled access (https://eutops.github.io/lifestyle-atlas/). This resource provides a foundation for exploring biomarker association and multi-tissue epigenetics, enabling hypothesis generation and benchmarking for systems biology and human healthspan research. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/673115v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@fec2d9org.highwire.dtl.DTLVardef@1aa5d13org.highwire.dtl.DTLVardef@1c4a6f2org.highwire.dtl.DTLVardef@1ac62fb_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMulti-tissue multi-modal profiling of two clinically-relevant lifestyle interventions in 156 women aged 30-60 C_LIO_LIIntermittent fasting modifies ageing-linked molecular features in an age-opposing direction C_LIO_LICross-tissue epigenetic biomarker mapping links immunity, metabolism, and microbiome C_LIO_LITissue-specific epigenetic plasticity maps reveal candidate meQTLs, C_LIO_LIData sharing and interactive portal enable broad reuse for hypothesis generation and exploration C_LI

systems biology↗

bioRxiv2026

KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs

Logical gene regulatory network (GRN) models provide interpretable, mechanistic representations of cellular regulation and are widely used in systems biology. However, most existing models remain incomplete, context-specific, and difficult to extend to comprehensive GRNs, limiting their broader applicability to tasks such as drug-response prediction and precision medicine. Here, add some ideas about virtual twins: With AML patients, we can use comprehensive GRNs to predict drug response. We present KGBN (Knowledge Graph-augmented Boolean Network modeling), a computational workflow for systematically augmenting logical GRN models. KGBN incorporates regulatory interactions derived from curated knowledge graphs as alternative logical rules while preserving the validated structure of existing models. Rule probabilities are optimized against experimental data to represent regulatory uncertainty and achieve data-driven calibration. Applying KGBN to acute myeloid leukemia, we show that extending an existing GRN with drug-target pathways and training against ex vivo drug-response data yields mutation-specific models that recapitulate known therapeutic sensitivities and signaling dependencies, demonstrating the utility of KGBN for interpretable, context-aware GRN modeling. (Should be predict)

systems biology↗

bioRxiv2022

Logical modelling of myelofibrotic microenvironment predicts dysregulated progenitor stem cell crosstalk

Primary myelofibrosis is an untreatable age-related disorder of haematopoiesis in which a break in the crosstalk between progenitor Haematopoietic Stem Cells (HSCs) and neighbouring mesenchymal stem cells causes HSCs to rapidly proliferate and migrate out of the bone marrow. 90% of patients harbour mutations in driver genes that all converge to overactivate hematopoietic JAK-STAT signalling, which is thought to be critical for disease progression, as well as microenvironment modification induced by chronic inflammation. The trigger to the initial event is unknown but dysregulated thrombopoietin (TPO) and Toll-Like Receptor (TLR) signalling are hypothesised to initiate chronic inflammation which then disrupts stem cell crosstalk. Using a systems biology approach, we have constructed an inter and intracellular logical model that captures JAK-STAT signalling and key crosstalk channels between haematopoietic and mesenchymal stem cells. The aim of the model is to decipher how TPO and TLR stimulation can perturb the bone marrow microenvironment and dysregulate stem cell crosstalk. The model predicted conditions in which the disease was averted and established for both wildtype and ectopically JAK mutated simulations. The presence of TPO and TLR are both required to disturb stem cell crosstalk and result in the disease for wildtype. TLR signalling alone was sufficient to perturb the crosstalk and drive disease progression for JAK mutated simulations. Furthermore, the model predicts the probability of disease onset for wildtype simulations that matches clinical data. These predictions might explain why patients who test negative for the JAK mutation can still be diagnosed with PMF, in which continual exposure to TPO and TLR receptor activation may trigger the initial inflammatory event that perturbs the bone marrow microenvironment and induce disease onset.

systems biology↗

bioRxiv2023

Analysis of the genome-scale metabolic model of Bacillus subtilis to design novel in-silico strategies for native and recombinant L-asparaginase overproduction

L-asparaginase is an enzyme with widescale use in the food and medicine industry. It is used as a chemotherapeutic drug in the treatment of acute lymphoblastic leukemia. Limitations of side effects associated with commercially available L-asparaginase necessitate the search for alternative sources. Bacillus subtilis is an emerging host for the production of chemicals and therapeutic products. This study deals with L-asparaginase production in Bacillus subtilis using systems metabolic engineering approach. System biology offers a detailed understanding of organism metabolism at the network level unlike the conventional molecular approach of metabolic engineering allowing one to study the effects of metabolite production on growth. Metabolism of Bacillus subtilis is studied using genome-scale metabolic model iYO844 which consists of relationships between the genes and proteins present in Bacillus subtilis. Also, the model contains information about all the metabolic reactions and pathways allowing convenient metabolic engineering methods. Computational methods like flux balance analysis, flux variability analysis, robustness analysis, etc. are carried out to study the metabolic capabilities of Bacillus subtilis. The model predicted a specific growth rate of 0.6242 h-1, which was comparable to the experimental value. Further, the model is used to simulate recombinant L-asparaginase production generating a maximum production rate of 0.4028 mmol gDW-1 h-1. Flux scanning based on enforced objective flux and OptKnock design strategies are used for strain development of Bacillus subtilis for higher production of both native and recombinant L-asparaginase.

systems biology↗

bioRxiv2022

Functional Decomposition of Metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions

Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the biosynthesis of metabolic building blocks such as amino acids. This allows us to obtain a plethora of results for E. coli growing in different conditions. A detailed quantification of energetic costs for biosynthesis and biomass growth on glucose shows that ATP generated during de novo biosynthesis of building blocks almost balances the ATP costs of peptide chain polymerization, the single largest energy expenditure for growing cells. This leaves the bulk of energy generated by fermentation and respiration (consuming 1/3 of the glucose intake) during aerobic growth unaccounted for. FDM also enabled the quantification of protein allocated towards each metabolic function, unveiling linear enzyme-flux relations for biosynthesis. These results led us to derive a function-based coarse-grained model to capture global protein allocation and overflow metabolism, without relying on curated pathway annotation or clustering of gene expression data.

systems biology↗

bioRxiv2026

Single-cell informed metabolic modeling reveals organ-specific metabolic adaptations in breast cancer organotropism

Breast cancer organotropism is driven by interactions between tumor cells and organ-specific microenvironments that support metastatic growth. To better understand the metabolic basis of organ-specific metastasis, we integrated single-cell transcriptomics with constraint-based systems biology to generate context-specific metabolic models of breast cancer metastasis to the liver, bone, and brain. Our analysis identified both common and organ-specific metabolic changes, suggesting that metastatic cells share a core metabolic program while also adapting to the metabolic environment of each target organ. Primary tumors with metastatic potential showed early alterations in nucleotide metabolism, transport reactions, and energy-related pathways, indicating metabolic changes before metastatic spread. Metabolic transformation analysis identified key metabolic regulators involved in the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, redox balance, and metabolite transport. Integration with CRISPR gene essentiality data further highlighted metabolically important genes as potential therapeutic targets. In addition, analysis of organ-specific secreted metabolites revealed distinct metabolic signatures associated with metastatic colonization of the liver, bone, and brain. Overall, our single-cell-informed metabolic modeling approach shows that breast cancer organotropism is associated with both shared and organ-specific metabolic adaptations. The study provides a framework for identifying potential metabolic vulnerabilities that could be targeted to treat metastatic breast cancer.

systems biology↗

bioRxiv2025

Comprehensive Network of Signaling Pathways in Hepatocellular Carcinoma and Network Analysis Reveals PTK2, GSK3β, and β-catenin to be Crucial for Cancer Progression

Hepatocellular carcinoma (HCC) is a highly aggressive primary liver cancer with significant morbidity and mortality. The development and progression of HCC involves several genetic mutations, alterations in signaling pathways leading to dysregulation of cellular processes. Several investigations have demonstrated the involvement of diverse regulators and their complex signaling cascades in progression of HCC. Understanding these crucial signaling pathways, including receptor tyrosine pathways, PI3K/AKT/mTOR, Wnt/{beta}-catenin, Ras/Raf/MAPK, TGF-{beta}, Hedgehog, JAK/STAT, and Hippo signaling, offers potential avenues for further experimental explorations and system level studies. The present study aims to construct a comprehensive molecular map of the signaling pathways involved in the development and progression of HCC using network editor Cell Designer. The assembled network, consisting of 193 distinct regulators and 362 reactions, provides a holistic representation of HCC associated pathways and adheres to Systems Biology Graphical Notation (SBGN) format. Through subsequent analysis the crucial regulators such as PTK2, GSK3{beta}, {beta}-catenin, and their pivotal roles in driving HCC progression were illuminated. These findings underscore the potential prognostic and clinical significance of regulators influencing the patient outcomes in HCC. Similar workflow can provide further insights and comprehensive understanding of the disease mechanisms. HighlightsO_LIA detailed molecular interaction map of pathways and regulators involved in HCC was developed. C_LIO_LITo illustrate one of the applications of the map developed, topological analysis using Cytoscape was performed to identify the significant hubs. Subsequently, the prognostic relevance of these identified hubs was evaluated using the transcriptome-based analysis. C_LIO_LIThese analysis revealed the crucial roles of regulators like PTK2, GSK3{beta}, and {beta}-catenin offering new prospects for potential prognostic and clinical significance of regulators in influencing HCC. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/666714v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@a0714forg.highwire.dtl.DTLVardef@16f18a1org.highwire.dtl.DTLVardef@ba17b1org.highwire.dtl.DTLVardef@1697432_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Revealing Shared Proteins and Pathways in Cardiovascular and Cognitive Diseases Using Protein Interaction Network Analysis

One of the primary goals of systems medicine is detecting putative proteins and pathways involved in disease progression and pathological phenotypes. Vascular Cognitive Impairment (VCI) is a heterogeneous condition manifesting as cognitive impairment resulting from vascular factors. The precise mechanisms underlying this relationship remain unclear, which poses challenges for experimental research. Here, we applied computational approaches like systems biology to unveil and select relevant proteins and pathways related to VCI by studying the crosstalk between cardiovascular and cognitive diseases. In addition, we specifically included signals related to oxidative stress, a common etiologic factor tightly linked to aging, a major determinant of VCI. Our results show that pathways associated with oxidative stress are quite relevant, as most of the prioritized vascular-cognitive genes/proteins were enriched in these pathways. Our analysis provided a short list of proteins that could be contributing to VCI: DOLK, TSC1, ATP1A1, MAPK14, YWHAZ, CREB3, HSPB1, PRDX6, and LMNA. Moreover, our experimental results suggest a high implication of glycative stress, generating oxidative processes and post-translational protein modifications through advanced glycation end-products (AGEs). We propose that these products interact with their specific receptors (RAGE) and Notch signaling to contribute to the etiology of VCI.

systems biology↗

bioRxiv2021

Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data

Approximate Bayesian Computation (ABC) is a likelihood-free parameter inference method for complex stochastic models in systems biology and other research areas. While conceptually simple, its practical performance relies on the ability to efficiently compare relevant features in simulated and observed data via distance functions. Complications can arise particularly from the presence of outliers in the data, which can severely impair the inference. Thus, robust methods are required that provide reliable estimates also from outlier-corrupted data. We illustrate how established ABC distance functions are highly sensitive to outliers, and can in practice yield erroneous or highly uncertain parameter estimates and model predictions. We introduce self-tuned outlier-insensitive distance functions, based on a popular adaptive distance weighting concept, complemented by a simulation-based online outlier detection and downweighting routine. We evaluate and compare the presented methods on six test models covering different model types, problem features, and outlier scenarios. Our evaluation demonstrates substantial improvements on outlier-corrupted data, while giving at least comparable performance on outlier-free data. The developed methods have been made available as part of the open-source Python package pyABC (https://github.com/icb-dcm/pyabc).

systems biology↗

bioRxiv2022

CRI-SPA, a mating based CRISPR-Cas9 assisted method for high-throughput genetic modification of yeast strain libraries

AbstractBiological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and the identification of phenotypic maximas would be of great benefit to synthetic biology. Thus, new research tools allowing simple and rapid mapping of sequence to function are required to forward these research fields. Here, we describe CRI-SPA, a method allowing the transfer of a chromosomal genetic feature from a donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion and Selective Ploidy Ablation and is executed within a week. We demonstrate the power of CRI-SPA by transferring four genes responsible for the production of betaxanthin, a yellow biosensor for the morphine precursor L-DOPA, into each strain of the yeast knock-out collection ({approx}4800 strains), providing a genome-wide overview of the genetic requirements for betaxanthin production. CRI-SPA is fast, highly reproducible, can be massively parallelized with automation and does not require selection for the transferred genetic feature.

systems biology↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2025

Structural and Boolean Network Modeling of the Levan Biosynthetic Pathway in Bacillus subtilis.

MotivationLevan is a fructose polymer with applications in the creation of hydrogels for drug delivery and wound healing. In industrial biotechnology. Bacillus subtilis is the key organism for producing levan. However, the metabolic models of Bacillus subtilis available do not include the biosynthesis of levan. To understand levan biosynthesis in B. subtilis, we employed structural systems biology integrating known structural details of proteins in the B. subtilis metabolic pathway to create a structure-annotated genome-scale metabolic model (GEM). To fill gaps in structural information about the enzymes, AlphaFold2 was used. Thus, this study enhances the metabolic model of B. subtilis by incorporating the biosynthesis of levan and including structural information about the proteins involved. ResultsThe manually curated model links proteins and reactions to protein data bank (PDB) entries, providing structural perspectives previously overlooked in GEMs. We mapped 508 PDB structures to 168 UniProt IDs to unravel 331 out of 1250 reactions (26.5%) in B. subtilis with focused coverage of sacB, sacC, sacX/Y, levD/E/F/G, and sacP. The structural layer does not alter stoichiometry or constraints unless explicitly parameterized. This structure-annotated resource enables the systematic testing of phenotype predictions and design strategies. Our structure-based metabolic model advances the understanding of levan production and microbial metabolism, facilitating sustainable and efficient biotechnological processes for industrial applications. Availability and implementationData available at the github page (https://github.com/raghuyennamalli/levan_ssbio)

systems biology↗