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B-390: Spatialproteomics – an interoperable toolbox for analyzing highly multiplexed fluorescence image data
Matthias Meyer-Bender, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, Germany
Harald Voehringer, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, Germany
Christina Schniederjohann, University Hospital Düsseldorf, Düsseldorf, Germany, Germany
Peter-Martin Bruch, University Hospital Düsseldorf, Düsseldorf, Germany, Germany
Sarah Koziel, University Hospital Düsseldorf, Düsseldorf, Germany, Germany
Erin Chung, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, Germany
Sascha Dietrich, University Hospital Düsseldorf, Düsseldorf, Germany, Germany
Wolfgang Huber, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany, Germany
Highly multiplexed immunofluorescence imaging is a recent method to characterize tissues at single-cell resolution on the protein level, offering low cost, high scalability, and the ability to analyze paraffin-embedded tissue samples. However, the analysis of these data involves a sequence of steps, including segmentation, image processing, marker quantification, cell type classification, and neighborhood analysis, each of which involves a multitude of method and parameter choices that need to be adapted to the data and analytical objective at hand. Moreover, variations in data quality can be high and unpredictable, which necessitates further flexibility and interactivity. While individual components exist, there is an unmet need for a coherent toolbox that offers end-to-end coverage of the workflow, flexibility, and automation.
We present spatialproteomics, a python package that addresses these challenges. Built on top of xarray and dask, spatialproteomics can process images that are larger than the working memory. It supports synchronization of shared coordinates across data modalities such as images, segmentation masks, and expression matrices, which facilitates easy and safe subsetting and transformation.
We demonstrate spatialproteomics on a set of images of reactive lymph nodes or different forms of B cell Non-Hodgkin lymphomas (BNHL) from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of cell type composition and spatial distribution across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process gigapixel whole slide images. Altogether, we propose spatialproteomics as an easy-to-install, easy-to-learn, comprehensive toolbox for constructing powerful end-to-end image analysis solutions for highly multiplexed immunofluorescence imaging.
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B-392: Systems-Level Analysis of Metabolic Dysregulation in Gaucher Disease: Mitochondrial Dysfunction and Disrupted Cholesterol Homeostasis
Yanjun Liu, University of Galway, Ireland, Ireland
Xi Luo, University of Galway, Ireland, Ireland
Samira Ranjbar, University of Galway, Ireland, Ireland
Ronan Fleming, University of Galway, Ireland, Ireland
Gaucher disease (GD), a lysosomal storage disorder caused by GBA1 mutations, is characterized by glucosylceramide accumulation and profound metabolic alterations in macrophages. To investigate these alterations at a systems level, we developed personalized genome-scale metabolic models of GD and healthy macrophages by integrating transcriptomic and exometabolomic data. Model simulations revealed extensive metabolic reprogramming in GD, including a shift from oxidative phosphorylation to glycolysis under energy stress, consistent with impaired mitochondrial function and reduced ATP output. Differential flux and correlation analyses further uncovered dysregulated amino acid metabolism, characterized by elevated uptake and nitrogen waste secretion, suggestive of enhanced catabolism and mitochondrial stress. Beyond primary sphingolipid accumulation, we identified widespread lipidomic disruptions, including impaired mitochondrial β-oxidation and altered fatty acid handling. Notably, cholesterol metabolism consistently emerged as a central axis of dysregulation across all analyses, involving changes in flux, transcriptional control, and co-regulated metabolic modules. These findings align with clinical lipid profile abnormalities in GD and highlight cholesterol homeostasis as a potential therapeutic target. Our study underscores the utility of constraint-based modeling in elucidating complex metabolic phenotypes and offers mechanistic insights into the pathophysiology of Gaucher macrophages.
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B-394: KINEPIK: An integrated data resource for cell signalling research
Venla Heinonen, Queen Mary, University of London, United Kingdom
Magdalena Hübner, Queen Mary, University of London, United Kingdom
Pedro Cutillas, Queen Mary, University of London, United Kingdom
Conrad Bessant, Queen Mary, University of London, United Kingdom
Kinase signalling plays a vital role in many biological processes, including the initiation and progression of disease. In recent years, phosphoproteomics has become an indispensable tool for exploring how kinase activity changes in response to perturbations. By interpreting phosphoproteomics data from perturbed cells in the context of existing knowledge (e.g. known kinase-substrate relationships) researchers are uncovering new insights into cell signalling pathways. However, bringing together the disparate data needed for this type of analysis is a hinderance to such work because the data is distributed across different sources, using inconsistent molecular nomenclature.
To address this problem, we present KINEPIK, an integrated data resource designed to streamline cell signalling research. At its core, KINEPIK features a relational database that links kinases with their known phosphosite targets, perturbations that influence their activity, and other information such as subcellular location. This database is integrated with a robust framework for storing high-quality phosphoproteomic data generated from studies involving genetic or chemical perturbations. Although initially focused on human data, KINEPIK is designed to support multiple species.
Developed using an API-first approach, KINEPIK offers seamless access to its data from popular environments such as Python and R, enabling smooth integration into analytical workflows. We demonstrate the platform’s capabilities through two common use cases: quantifying kinase activity from phosphoproteomic profiles, and building a cell signalling knowledge graph.
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B-396: An informed deep learning model of the Omicron wave and the impact of vaccination
Elham Shamsara, Methods in Medical Informatics, Department of Computer Science, University of T¨ubingen, 72076 T¨ubingen, Germany, Germany
Florian König, Methods in Medical Informatics, Department of Computer Science, University of T¨ubingen, 72076 T¨ubingen, Germany, Germany
Nico Pfeifer, Methods in Medical Informatics, Department of Computer Science, University of T¨ubingen, 72076 T¨ubingen, Germany, Germany
The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicron in Germany, France, and Italy by employing Physics-Informed Neural Networks to estimate the temporal parameters influencing its spread. We validated the performance of our model using the Root Mean Squared Percent Error (RMSPE).
Our analysis revealed significant correlations between specific viral mutations—S371F, T376A, D405N, and R408S—and increased transmission rates in all three countries. These mutations, prevalent in the Omicron BA.2 and BA.3 sublineages, are linked to immune evasion and heightened transmissibility.
Link to the paper: https://www.sciencedirect.com/science/article/pii/S0010482525003191
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B-398: Advancing diagnostics in inherited metabolic diseases through enhanced metabolic modeling of lipid metabolism
Anastasia Sveshnikova, SIB Swiss Institute of Bioinformatics, Switzerland
Lucila Aimo, SIB Swiss Institute of Bioinformatics, Switzerland
Jerven Bolleman, SIB Swiss Institute of Bioinformatics, Switzerland
Sebastien Moretti, SIB Swiss Institute of Bioinformatics, Switzerland
Anne Niknejad, SIB Swiss Institute of Bioinformatics, Switzerland
Marco Pagni, SIB Swiss Institute of Bioinformatics, Switzerland
Nicole Redaschi, SIB Swiss Institute of Bioinformatics, Switzerland
Alan Bridge, SIB Swiss Institute of Bioinformatics, Switzerland
Inherited metabolic diseases (IMDs) often manifest with diverse, non-specific symptoms that resemble more common conditions, leading to delayed diagnoses and reduced patient quality of life. The Horizon Europe project Recon4IMD seeks to improve IMD diagnosis by advancing computational metabolic modeling. A central effort is the development of Recon4, a human metabolic model integrating Recon3 with key resources such as the Virtual Metabolic Human (VMH), MetaNetX, UniProtKB/Swiss-Prot, and the Rhea reaction database.
Our contribution focuses on chemically and catalytically precise representation of human lipid metabolism. Using SwissLipids and Rhea, we systematically enumerated 2,253,663 unique lipid-specific reactions with RInChIKeys, formatted for integration into MetaNetX. To support this, we developed the Python package pyrheadb, which translates Rhea into a usable network and model, with documentation provided at GitHub.
To address disconnected compounds, 1,849 reaction templates from Rhea generated 1,350 candidate reactions aimed at closing 62 network gaps. Additionally, we extended reaction enumeration to stereospecific variants by detecting undefined stereocenters, generating stereochemical forms, and applying atom mapping, resulting in stereo options for 2,042 Rhea reactions.
Sphingolipid reactions posed unique challenges due to their glycan-based nomenclature. Using SphinGOMAP and curator-supplied data, we translated sugar chain formulas into reaction SMILES, producing 2,000 balanced reactions for model integration.
This work enhances lipid metabolic pathway reconstructions with high chemical specificity, supports the transition to Recon4, and introduces database-independent identifiers to improve reproducibility. It also lays the groundwork for IMD-specific knowledge graphs and sex-specific metabolic models.
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B-400: SPECIMEN and refineGEMs: Open-Source Python Tools for Streamlined Metabolic Modelling
Gwendolyn Olivia Döbel, Martin Luther University Halle-Wittenberg, Germany
Carolin Brune, Martin Luther University Halle-Wittenberg, Germany
Andreas Dräger, Martin Luther University Halle-Wittenberg, Germany
Systems biology aims to understand organisms by analysing them as context-specific
systems. For example, constrained-based metabolic network models are used to picture an organism’s metabolism. These models enable simulations that can guide laboratory
experiments towards specific objectives, such as Biomass production under defined
environmental conditions. The results can be used for drug design and biotechnology
workflows, potentially reducing resource costs.
However, building high-quality models is time-consuming, as many steps require manual input. The open-source Python toolbox refineGEMs provides a variety of functionalities for model curation and a novel, in silico media database for simulating models under different conditions. Building upon this toolbox, SPECIMEN is an open-source Python package containing a collection of primarily automated workflows for model curation. For example, it includes a workflow based on draft model generation via CarveMe (CMPB) and another, where the draft model is generated using a template model and performing a bidirectional BLAST (HQTB).
In addition to the initial model generation, the workflows include further steps, including gap-filling (identifying missing reactions in pathways and adding them to the model), charge curation (balancing the charges in the chemical reactions), biomass objective function (chemical equation describing biomass production) adjustment, the addition of annotations from various databases, and more. Overall, the workflows allow for the efficient generation of new models of higher quality and completeness than the outputs of standard workflows for draft model generation, making constrained-based modelling faster and more accessible.
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B-402: A Co-Evolutionary Multi-Objective Framework for Joint Topology and Parameter Discovery in Gene Regulatory Networks
Tuan Do, N2TP Technology Solutions JSC., Viet Nam
Nhung Duong, Hanoi University of Pharmacy, Viet Nam
Tien Nguyen, Hanoi University of Pharmacy, Viet Nam
Ngoc Do, Hanoi University of Pharmacy, Viet Nam
Anh Truong, Hanoi University of Pharmacy, Viet Nam
Lap Nguyen, Hanoi University of Pharmacy, Viet Nam
Background: Reconstructing Gene Regulatory Networks (GRNs) requires inferring both network topology and kinetic parameters, a persistent challenge in systems biology often tackled separately. Biological realism, however, demands simultaneous optimization under multiple constraints. We aimed to develop a framework for joint topology and parameter discovery that explicitly balances dynamic accuracy, network simplicity, and system robustness.
Methods: We present a co-evolutionary multi-objective optimization framework using two interacting populations for topologies and ODE-based kinetic parameter sets (Hill-type models). The algorithm simultaneously optimizes three objectives: (1) minimizing Mean Squared Error (MSE) against target time-series data, (2) promoting network parsimony (fewer edges), and (3) maximizing robustness to perturbations. Biologically-inspired evolutionary guide the search.
Results: On synthetic GRN benchmarks (N=5 and N=10 genes), our co-evolutionary approach significantly outperformed baseline methods in capturing target dynamics. For instance, it achieved lower MSE than single-objective optimization (e.g., 0.136 vs 0.174 for N=5; 2.172 vs 2.792 for N=10) and often surpassed even methods given the true topology. While exact structural recovery was partial (F1 scores 0.2-0.6 vs. 1 for topology-fixed), the framework consistently identified functionally equivalent networks that accurately reproduced dynamics using alternative, often parsimonious, structures while maintaining high robustness.
Conclusion: Our co-evolutionary multi-objective framework effectively navigates the complex search space of joint GRN inference. By prioritizing functional equivalence and balancing competing objectives, it discovers dynamically accurate and robust network models, even without perfect structural matches. This provides a valuable tool for systems biology analysis and synthetic biol
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B-404: Spatial Flux Balance Analysis reveals region-specific cancer metabolic rewiring and metastatic mimicking
Davide Maspero, Centro Nacional de Análisis Genómico, Barcelona, Spain, Spain
Giovanni Marteletto, Immunobiology Department, Yale School of Medicine, New Haven, USA, United States
Francesco Lapi, Department of Biosciences and Biotecnologies, University of Milano-Bicocca, Milan, IT, Italy
Bruno Giovanni Galuzzi, Institute of bioimaging and complex biological systems (IBSBC), Segrate, IT, Italy
Irene Ruano, Centro Nacional de Análisis Genómico, Barcelona, Spain, Spain
Ben Vandenbosch, Department of Oncology, KU Leuven, Leuven, Belgium, Belgium
Ke Yin, Department of Oncology, KU Leuven, Leuven, Belgium, Belgium
Sabine Tejpar, Department of Oncology, KU Leuven, Leuven, Belgium, Spain
Alex Graudenzi, Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, IT, Italy
Holger Heyn, Universitat de Barcelona (UB), Barcelona, Spain, Spain
Anna Pascual-Reguan, Centro Nacional de Análisis Genómico, Barcelona, Spain, Spain
Chiara Damiani, Department of Biosciences and Biotecnologies, University of Milano-Bicocca, Milan, IT, Italy
To investigate cancer cell metabolism with spatial precision, it is crucial to estimate the metabolic fluxes with spatial resolution. However, current methods for inferring metabolic fluxes lack spatial resolution at the scale now achievable with spatial transcriptomics.
To address this gap, we introduce spatial Flux Balance Analysis (spFBA), a novel framework to process spatial transcriptomics data and compute metabolic fluxes at the spatial spot resolution. The spFBA approach builds upon previous work designed for bulk and single-cell data to return metabolic fluxes, up to the level of single reactions, that can distinguish their preferred directional usage, retaining spatial resolution. spFBA integrates differential constraints on flux boundaries in steady-state metabolic modelling, informed by spatial gene expression, and utilizes a corner-based sampling strategy.
We validated spFBA using a publicly available renal cancer dataset, including tumor-normal interface samples. spFBA successfully summarises histological structures and detected cancer metabolic hallmarks, such as enhanced glucose uptake, lactate production, and metabolic growth. Yet, it achieved unprecedented resolution, uncovering region-specific features, including increased glutamate consumption at the tumor interface and hypoxic regions within the tumor core.
By applying spFBA to a new stereo-seq colorectal cancer dataset, including paired primary tumor and liver metastasis samples, we provide compelling evidence that metastases mimic the metabolic traits of their tissue of origin. Additionally, we present the first in vivo evidence of lactate-consuming cancer cells.
spFBA stands out as a powerful framework to unravel the spatial metabolic complexity of cancer and beyond, leveraging the expanding landscape of spatial transcriptomics datasets.
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B-406: Modeling differentiation at clonal level in B-cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL)
Adrien Jolly, University Hospital Frankfurt, Germany
Alec Gessner, University Hospital Frankfurt, Germany
Michael Rieger, University Hospital Frankfurt, Germany
Florian Buettner, University Hospital Frankfurt and German Cancer Research Center, Germany
Adult BCP-ALL is a neoplastic disease characterized by phenotypical and functional heterogeneity. However, the cellular organization underlying this heterogeneity is poorly understood. It can arise from both clonal evolution and/or from cell differentiation but so far no result could conclusively determine whether differentiation occurs.
To address this question, we use BCP-ALL patient-derived long-term cultures. We have developed expressible barcode vectors that can be detected alongside the transcriptomes and surface markers following lentiviral transduction. The barcoded cells are transplanted in immuno compromised ,mice to study leukemogenesis in vivo. We performed single-cell CITE-seq at full-blown leukemia which revealed that a majority of clones spanned multiple cell populations, indicating differentiation between phenotypically distinct cell clusters.
To determine the dynamics of the disease, we have designed differential equation-based mathematical models that simulate the development of each barcoded clone starting from a stem cell compartment. Using approximate Bayesian computation, we fitted the models to clone size distributions to test multiple differentiation scenarios and estimate the proliferation and differentiation rates of the clones. By that means, we could identify the most likely cluster of origin and exclude reversible differentiation (plasticity). We could also quantifiy the level of proliferation heterogeneity between clusters and between clones, thus finding that proliferation does not depend on the differentiation stage.
Generally, our modeling approach reveals how single time point lineage data can inform on directionality of differentiation and dynamical parameters in biological systems where only limited prior information is available as is often the case in cancer.
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B-408: Cancer cell-intrinsic programs of alternative splicing in tumour development and progression
Larisa M. Soto, Department of Human Genetics, McGill University, Canada
Hamed S. Najafabadi, Department of Human Genetics, McGill University, Canada
Widespread alternative splicing (AS) alterations have been reported in tumour transcriptomes of all known cancer types, but it is unclear to what extent these alterations reflect cancer cell-intrinsic reprogramming of the splicing landscape as opposed to variations in tumour cellular composition—tumour infiltrating cells have vastly different splicing landscapes, potentially creating a large number of false positive splicing changes. This and other confounding factors have been largely neglected in previous studies, despite the compelling evidence supporting the impact of confounder biases in RNA sequencing analyses.
Using TRex, a novel computational tool to model the impact of multiple variables on the quantification of differential AS from RNA-seq data, we estimated changes between (1) tumour and normal tissues and (2) tumours of different stages using 8,633 samples spanning 24 cancer types. Our systematic analysis revealed that tumour purity explains up to 75% of the variance in splicing profiles between tumours of the same cancer type. Intron retention (RI) emerges as the most common cancer cell-intrinsic program, with some RI events recurrently dysregulated in nearly two-thirds of the cancers. Myogenesis, Mitotic Spindle, and other cancer hallmark pathways are strongly enriched among cancer-cell AS events dysregulated in multiple cancer types, whereas immune-related pathways are overrepresented in impurity-associated splicing changes.
This work presents a comprehensive, unbiased compendium of cancer cell-intrinsic AS programs contributing to different axes of tumour biology. This resource opens the door to studying mechanisms of AS reprogramming in cancer cells and their contribution to disease development and trajectory.
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B-410: Inferring Trajectories of Cell State Transition with Real-Time Scaling
Kaitlyn Ramesh, Northeastern University, United States
Benjamin Clauss, Tufts University, United States
Mingyang Lu, Northeastern University, United States
Cell phenotypic transitions are influenced by dynamic changes in gene expression over time. To investigate these dynamics using static single-cell gene expression data, pseudotime algorithms have been developed. However, existing methods often fail to recapitulate a biologically accurate timescale. Here, we present RETRO, a pseudotime algorithm that integrates gene expression data and low-resolution temporal information to accurately infer transcriptional dynamics. We applied RETRO to both synthetic and experimental datasets. Compared to existing methods, RETRO demonstrates improved performance in identifying complex trajectories and timescales of cell state transitions. Further application of RETRO will enable deeper mechanistic insights into cell fate decision.
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B-412: Metabolic Modeling of Anaerobic Digestion Process in Waste Activated Sludge by Transcriptomic Data Integration
Ebru Sarsılmaz, Bezmialem Vakıf University, Turkey
Muhammed Erkan Karabekmez, Istanbul Medeniyet University, Turkey
Anaerobic digestion of waste activated sludge represents a key biotechnological strategy for the sustainable treatment of organic waste and the recovery of bioenergy, particularly in the form of methane. This complex process is mediated by a consortium of microorganisms that coordinate the breakdown of organic matter through syntrophic interactions. Understanding the metabolic activity of individual microbial taxa within this community is essential for optimizing process efficiency and stability.
In this study, we integrate transcriptomic data with genome-scale metabolic models (GEMs) to investigate the metabolic capabilities and interactions of five key microbial genera involved in anaerobic digestion. Due to inconsistencies between transcriptomic gene identifiers and those used in available metabolic models, a gene ID harmonization step was implemented to enable accurate integration.
GEMs were curated and refined by incorporating expression data, removing non-essential reactions, and downscaling reactions with low transcriptional support. Flux Balance Analysis (FBA) was subsequently performed to estimate organism-specific growth rates under anaerobic conditions.
Beyond individual modeling, these curated GEMs are being integrated into a community-scale model to explore interspecies metabolic dependencies and syntrophic interactions. Although results are pending, this systems-level approach is expected to provide mechanistic insights into the metabolic architecture of anaerobic sludge communities and inform strategies to enhance biogas yield and process resilience.
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B-414: Transcriptomic landscape of Olaparib treatment in high-grade serous ovarian cancer
Roxana Andreea Moldovan, Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Spain, Spain
Chiara Battistini, Unit of Gynaecological Oncology Research, European Institute of Oncology IRCSS, Milan, Italy, Italy
Ugo Cavallaro, Unit of Gynaecological Oncology Research, European Institute of Oncology IRCSS, Milan, Italy, Italy
Mirjana Kessler, Department of Obstetrics and Gynaecology , LMU University Hospital, Munich, Germany, Germany
Sophia Geweniger, Department of Obstetrics and Gynaecology , LMU University Hospital, Munich, Germany, Germany
Sonia Tarazona, Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Spain, Spain
High-grade serous ovarian cancer (HGSOC) is the most prevalent and aggressive form of gynaecological cancer, accounting for approximately 75% of cases. Despite advancements in therapeutic interventions, there has been no substantial improvement in overall patient survival over the past few decades. A therapeutic strategy that has been employed involves the inhibition of PARP (Poly ADP-Ribose Polymerase), a key enzyme in DNA repair. Drugs such as Olaparib, a PARP inhibitor, have demonstrated efficacy in patients with BRCA1/2 mutations and/or homologous recombination deficiencies. However, the molecular mechanisms underlying this response remain to be fully elucidated.
The present study aims to elucidate the molecular mechanisms underlying the response and resistance to Olaparib treatment by analysing transcriptional changes using in vitro models derived from HGSOC patients. Samples were treated with DMSO or Olaparib and subsequently analysed using RNA-Seq. The bioinformatic processing comprised the following steps: quality control (FastQC, FastP), alignment to the reference genome (STAR), quantification (FeatureCounts), and analysis in R, including filtering (NoiSeq), normalization (Conditional Quantile Normalization), batch effect correction (MultiBaC), differential expression analysis (limma), and functional analysis (GSEA, ORA).
The results revealed alterations in key genes, highlighting a decrease in ASPM expression, which is involved in DNA repair and cell proliferation. Furthermore, at functional level, the study identified a significant alteration regarding the transcriptional regulation of RUNX, a critical transcription factor in HGSOC. These findings contribute to the understanding of PARP inhibition mechanism and could offer new perspectives in the search for biomarkers and therapeutic strategies in HGSOC.
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B-416: Computational Prediction of Targeted Nutritional Interventions for Host–Microbiome Modulation in C. elegans
Rupinder Kaur, UKSH Campus Kiel, Germany
Prof. Dr. Christoph Kaleta, UKSH Kiel, Germany
Rational dietary modulation of host-associated microbiota demands approaches that translate multi-omics data into mechanistic, testable hypotheses. We present a computational framework combining dual RNA-sequencing, genome-scale metabolic modelling, and spatially explicit agent-based simulation to prioritise nutritional interventions in the Caenorhabditis elegans gut ecosystem.
Wild-type C. elegans were exposed to eight bacterial strains, including pathogens (Pseudomonas aeruginosa, Enterococcus faecalis) and commensals (Sphingobacterium sp., Ochrobactrum sp., and four additional isolates). Paired-end reads are analysed through both co-mapping (concatenated reference) and sequential host-to-microbe alignments; resulting gene-level counts constrain condition-specific genome-scale metabolic models (GEMs) for C. elegans and each bacterium. Flux-consistent pruning removes thermodynamically infeasible loops, yielding context-specific metabolic networks.
An automated nutrient-scanner systematically perturbs single or paired dietary substrates, and flux balance analysis predicts effects on bacterial biomass, cross-feeding interactions, and proxies for host fitness. To incorporate spatial heterogeneity, GEMs are embedded into WormColon, an agent-based spatial module adapted from BacArena’s virtual-colon concept, simulating microbial colonisation and nutrient diffusion within the gut lumen.
Current milestones include completion of all condition-specific GEMs and the nutrient-scanner implementation; spatial simulations are ongoing. The framework outputs a ranked catalogue of candidate supplements annotated with mechanistic reaction signatures and spatial growth dynamics. Experimental validation will combine controlled feeding assays, follow-up dual RNA-seq, and lifespan analyses, enabling iterative model refinement.
This approach provides a scalable, mechanistic route from dual-omics data to actionable dietary strategies, advancing precision microbiome engineering in simple animal models.
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B-418: Transcriptome Analysis and Genome‑Scale Metabolic Reconstruction of Bos taurus for Optimizing Cultured Meat Production
Junkyu Lee, KAIST, South Korea
Jaehyun Kim, KAIST, South Korea
Sofia Mun, KAIST, Kazakhstan
Hyun Uk Kim, KAIST, South Korea
Minji Kim, DTU, South Korea
Hyun Won Bae, CJ BIO Research Institute, South Korea
Byung Kwon Jung, CJ BIO Research Institute, South Korea
Juyeon Kim, CJ BIO Research Institute, South Korea
Sunggun Lee, CJ BIO Research Institute, South Korea
Cultured meat industrialization has attracted significant attention as a sustainable alternative to conventional meat production. Bos taurus, a key cattle species, is regarded as a promising source for the cultured meat industry. However, the metabolism of B. taurus muscle cells remains relatively unknown in the context of cultured meat. In this study, transcriptome analysis and genome-scale metabolic reconstruction were performed using bovine satellite cells (BSCs) derived from the semimembranosus muscle of Korean Hanwoo cattle. Differential gene expression and gene set enrichment analyses of RNA-seq data revealed pivotal pathways involved in muscle cell proliferation (e.g., ‘Cell cycle’ and ‘RNA polymerase’) and differentiation (e.g., ‘Cytoskeleton in muscle cells’ and ‘Tryptophan metabolism’). Using the Human1 GEM as a template, we constructed the first B. taurus-specific genome-scale metabolic model, named BtaSBML2986, comprising 2,986 genes, 13,278 reactions, and 8,652 metabolites to perform various metabolic simulations. The model’s predictive accuracy was validated using flux balance analysis (FBA) by comparing simulated growth with experimentally measured growth rates, revealing a strong correlation. BtaSBML2986 was subsequently integrated with the RNA-seq data to examine key pathways such as the TCA cycle and glycolysis associated with cell proliferation. This integrated approach offers insights into critical metabolic pathways for cultured meat production, highlighting the potential of BtaSBML2986 to guide future efforts in optimizing culture processes.
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B-420: Reconstructing a genome-scale metabolic model of Haemophilus influenzae strain Rd KW20 to mirror growth within the human nasal environment
Reihaneh Mostolizadeh, Bioinformatics and Systems Biology, Justus Liebig University Giessen, 35392 Giessen, Germany, Germany
Niklas Henle, Department of Computer Science, Eberhard Karl University of Tübingen, 72076 Tübingen, Germany, Germany
Oskar Wacker, Department of Computer Science, Eberhard Karl University of Tübingen, 72076 Tübingen, Germany, Germany
Andreas Dräger, Data Analytics and Bioinformatics, Martin Luther University Halle-Wittenberg, Halle (Saale) 06120, Germany, Germany
Background: Haemophilus influenzae is a pathogenic bacterium that can cause various diseases, including respiratory tract infections, bloodstream infections, and meningitis. Most of its strains are opportunistic, i.e., they asymptomatically colonize healthy individuals but can cause severe disease in immunocompromised hosts. While antibiotics represent the first-line therapy against H. influenzae infections, their effectiveness is severely undermined by this organism’s spread and diversification of antibiotic resistance.
Results: Comprehensive and reliable genome-scale metabolic models (GEMs) have demonstrated their ability to facilitate our understanding of many organisms. Thus, such a model could help identify new drug targets against the virulence mechanisms across various H. influenzae strains. Here, we present an updated, high-quality H. influenzae strain Rd KW20 model based on more accurate and expanded gene annotations, several biochemical databases, and findable, accessible, interoperable, reproducible (FAIR) data principles. The updated model is a comprehensive knowledge base that fulfills systems biology standards and is available in Systems Biology Markup Language (SBML) format. To match the conditions of the nasal environment, our model mimics the growth conditions of the human nasal mucosa by simulating in silico growth within a modified version of the synthetic nasal medium (SNM). Besides, to give an overview of all available GEMs of H. influenzae, we also examined all previously published models’ growth capabilities and similarities. Furthermore, the open-source quality-control tool MEMOTE was applied to evaluate standardized metabolic tests.
Conclusion: This comparison will play a vital role in the more accurate application of GEMs for drug targeting and modeling interactions among multiple organisms.
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B-422: Simulating Tumor Evolution with Functional Stochastic Branching Models: A Framework for Testing Evolutionary Hypotheses
Daniela Volpatto, Department of Computer Science, University of Turin, Italy
Simone Pernice, Department of Computer Science, University of Turin, Italy
Sandro Gepiro Contaldo, Department of Computer Science, University of Turin, Italy
Daniela Conticelli, University of Turin, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
Simona Corso, University of Turin, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
Silvia Giordano, University of Turin, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
Marco Beccuti, Department of Computer Science, University of Turin, Italy
Francesca Cordero, Department of Computer Science, University of Turin, Italy
Roberta Sirovich, Department of Mathematics, University of Turin, Italy
We present a synthetic tumor simulator based on stochastic branching processes that captures the dynamics of clonal evolution under biologically interpretable principles. The model tracks cell counts over time for each emerging subclone, shaped by selective pressures and competitive interactions. Clonal fitness is allowed to emerge through five functional mechanisms: (I) proliferative advantage, (II) genomic instability, (III) ecological interactions, (IV) space expansion beyond standard carrying capacity, and (V) passenger mutations. Clones are defined by their genotype, but behave according to phenotype alone i.e. these functional capabilities that are associated with their mutations, minimizing the number of required parameters without losing subclonal resolution. Carrying capacity constraints and clone-specific competition rules shape non-linear dynamics, generating realistic intratumor heterogeneity. Stochasticity is central, allowing rare but impactful events to steer the evolutionary trajectory beyond deterministic expectations.
The simulation outputs include complete clonal genealogies and can be summarized in synthetic VCFs, enabling comparison with deep-sequencing data. We explored different scenarios deriving from different combinations of functional effects with literature data getting insights on what the explanation behind neutral, linear, punctual and parallel evolution might be. Furthermore, we recreated a synthetic counterpart of an in vitro experiment involving MLH1 knockout in mouse cell lines, tailoring the model to mimic the specific conditions of the biological system. By exploring a range of functional configurations, we identified the combination of selective advantages most likely responsible for the observed tumorigenic behavior. This approach systematically tests evolutionary hypotheses and functional mechanisms in a controlled, fully traceable digital environment.
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B-424: A query-driven framework for constructing Boolean networks from disease maps: application to Parkinson’s disease
Adrien Rougny, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
Ahmed Abdelmonem Hemedan, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
Marek Ostaszewski, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
Venkata Satagopam, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg
Complex diseases generally involve several interconnected pathways. The understanding of how their cross-talks lead to specific phenotypes is key to deciphering the molecular basis of diseases. To this end, disease maps encode disease-related pathways following graphical and computational systems biology standards. Diagrams of disease maps may support the construction of dynamical models that are necessary to predict effects of perturbations on the molecular mechanisms they describe. Current methods build dynamical models from single diagrams, and are therefore not well suited to capturing the interconnections between pathways. Moreover, they do not support the fine-grained selection of parts of interest from the input diagrams or the output models, which remains a manual task that is difficult to reproduce. Here, we present a novel query-driven framework for the construction of dynamical models from disease maps that overcome these issues. Specifically, our framework allows users to (i) integrate diagrams of one or more disease maps into a graph database; (ii) query parts of interest from these integrated diagrams; and (iii) transform the output of the query into a Boolean network whose dynamics can then be analyzed using state-of-the-art tools. We apply our framework to the Parkinson’s Disease Map, a comprehensive resource capturing key Parkinson’s disease-specific mechanisms. In particular, we show how our framework may be used to study critical pathways leading to disease phenotypes such as neuronal survival, as well as inter-pathway regulations. Finally, we discuss the advantages and challenges of query-based modeling for transparent and reproducible research.
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B-426: Computational Membrane Lipid Structure Inspector (CoMLiSI): a tool to verify lipid structural elements in PDB files
Graeme McDowell, National Research Council of Canada, Canada
Anuradha Surendra, National Research Council of Canada, Canada
Qassim Alkassir, National Research Council of Canada, Canada
Steffany Bennett, University of Ottawa, Canada
Miroslava Čuperlović-Culf, National Research Council of Canada, Canada
Biological lipid membranes provide structure for cells and organelles, and either directly or through interaction or modulation of signalling proteins, drive a number of biological processes in both healthy and diseased states. Computational modeling of membranes allows for calculation of properties such as membrane fluidity or curvature, as well as analysis of protein-lipid interactions. Generation of membrane structures requires that the lipid molecules are individually built and combined within the model yet requires manual curation to ensure structural accuracy is maintained in the model. This type of curation is extremely taxing and error prone.
Here, we present Computational Membrane Lipid Structure Inspector (CoMLiSI), a novel web-based application designed to verify structural features of lipid molecules within computationally or experimentally derived membranes, providing users information about potential structural errors in individual lipids added to the models, and suggesting required correction before further application. CoMLiSI accepts PDB files containing all-atom lipid structures, analyzes these models, and returns information to the user for each lipid, including locations and configuration of double bonds, as well as flagging structural abnormalities such as biologically incorrect atomic bonding. CoMLiSI provides visualization of either all the lipids in the dataset or of specific molecules to outline structural characteristics, generates downloadable adjacency matrices for each lipid with information about all bonds, and provides reports about observed issues.
Using publicly available and in-house generated lipid membrane models, we will demonstrate how CoMLiSI will assist the research community in ensuring lipid structures within membrane models are correct for more accurate simulations.
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B-428: Interpretable Markov Models Reveal Distinct Transport Pathways and Bottlenecks in the Nuclear Pore Complex
Roi Eliasian, The Hebrew University of Jerusalem, Israel
Barak Raveh, The Hebrew University of Jerusalem, Israel
The Nuclear Pore Complex (NPC) forms a selective barrier between the nucleus and cytoplasm, efficiently transporting diverse molecular cargos while restricting passive diffusion based on cargo size. Despite its critical biological importance, fundamental questions persist regarding how actively transported molecules navigate the layers of disordered FG repeats lining the NPC’s central channel. To elucidate these transport mechanisms, we developed interpretable Markov models derived from coarse-grained Brownian dynamics simulations, capturing detailed transition dynamics between distinct FG-repeat domains of various FG nucleoporins (FG Nups). Consistent with recent experimental imaging, our analysis reveals transport predominantly occurring through eight spatially distinct conduits, characterized by minimal lateral diffusion between NPC spokes, supporting a parallel-channel transport model. Ongoing analyses examine whether this vertical transport preference remains consistent across varying cargo sizes and transport conditions. By mapping specific transport sequences, we identified characteristic “relay race” patterns, whereby cargos sequentially transfer between critical nucleoporins during transit. Systematic perturbation studies involving targeted deletion of key FG Nups pinpointed essential transport bottlenecks and assessed robustness against structural disruptions. Our Markov-based framework facilitates rapid hypothesis testing of NPC transport dynamics, overcoming computational limitations inherent to direct simulation, and providing crucial insights into how this essential cellular gateway achieves selectivity amid diverse molecular traffic. The suggested approach can describe diverse networks of dynamic protein-protein interactions, including those in biomolecular condensates.
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B-430: Network-centric analysis of a post-traumatic stress disorder regulatory model
Fabien Romano, INSERM, France
Clémence Réda, Universität Rostock, France
Andrée Delahaye-Duriez, INSERM U1141, France
Post-traumatic stress disorder (PTSD) is a complex neuropsychiatric condition involving widespread dysregulation, notably in neuronal and immune pathways. Understanding its molecular mechanisms remains a major challenge due to the absence of mechanistic regulatory models and robust biomarkers of sensitivity or resilience. Network-based approaches offer promising perspectives to characterize disease-specific transcriptional signatures and integrate interactions between relevant functional pathways.
First, we identified a subset of 524 marker genes by cross-referencing sources of differentially expressed genes from PTSD patients blood transcriptomes. Second, we enriched this set with whole pathways using curated regulatory interactions from the STRING database. Third, we built a boolean network model on the regulation of this set of genes by leveraging gene knockdown perturbation experiments from the LINCS L1000 database. Our model of PTSD comprises 782 genes and 1,605 interactions. We exploited this network to predict key gene regulators whose perturbation shifted attractor states away from PTSD profiles, reversing pathological expression profiles in silico.
Then, we partially validated our network by observing that 45.3% of its edges were experimentally confirmed as physical protein-protein interactions. We identified 31 key regulator genes, including IL6, a pro-inflammatory cytokine consistently upregulated in PTSD and correlated with symptom severity. Functional enrichment analysis on these 31 genes revealed strong associations with inflammation, stress-related and depressive disorder-related pathways.
Our work introduces a network model of PTSD and a set of prioritized core genes, based on their expression and predicted impact on the network. This integrative approach lays the groundwork for an improved mechanistic understanding of PTSD.
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in Liverpool, UK. The meeting will feature two keynote talks and contributed presentations.
