Biology subjects

Bleker, C.

Publications and source records attributed to Bleker, C..

7 recordsLinked to original sources

bioRxiv2026

TabularQual: A spreadsheet-based format for annotating and curating logical models in SBML-qual

Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce TabularQual, a spreadsheet-based, community-driven representation for Boolean and multi-valued logical models, together with a bidirectional converter between spreadsheets and SBML-qual. The converter is accessible programmatically and via a web interface to support diverse user workflows. We further describe two integration strategies that enable existing modeling tools to operate with TabularQual, either through SBML-qual exchange or via direct support. Case studies using the Stress Knowledge Map and CaSQ demonstrate how this integration supports model construction, curation, and reuse. TabularQual provides a practical bridge between human-readable model representations and standardized executable formats, supporting reproducibility, interoperability, and community-driven model development.

systems biology↗

bioRxiv2026

BoolDog: integrated Boolean and semi-quantitative network modelling in Python

SummaryBoolean and logic-based modeling approaches are well suited for the analysis of complex biological systems, particularly when detailed biochemical and kinetic information is unavailable. In such settings, biological pathways are represented as networks capturing system components and their interactions, providing a simplified yet informative abstraction of system behavior. While the structural topology of these networks is often well characterized, the absence of mechanistic detail limits the applicability of parameter-dependent modeling frameworks. To address this, we present BoolDog, a Python package for the construction, simulation, and analysis of Boolean and semi-quantitative Boolean networks. BoolDog supports synchronous simulation with events, attractor and steady-state identification, network visualization, and the systematic transformation of logic-based models into continuous ordinary differential equation (ODE) systems -- enabling the seamless integration of discrete and continuous modeling paradigms. Networks can be imported and exported across standard formats, and BoolDog integrates natively with established Python libraries for network analysis and visualisation, including NetworkX, igraph, and py4Cytoscape. Together, these capabilities provide a flexible, accessible, and interoperable platform for logic-based modeling of complex biological systems. Availability and implementationBoolDog is implemented in Python and available at https://github.com/NIB-SI/BoolDog/.

systems biology↗

bioRxiv2024

Evaluating plant growth-defence trade-offs by modelling the interaction between primary and secondary metabolism

Understanding the molecular mechanisms behind plant response to stress can enhance breeding strategies and help us design crop varieties with improved stress tolerance, yield and quality. To investigate resource redistribution from growth-to defence-related processes in an essential tuber crop, potato, here we generate a large-scale compartmentalised genome-scale metabolic model, Potato-GEM. Apart from a large-scale reconstruction of primary metabolism, the model includes the full known potato secondary metabolism, spanning over 600 reactions that facilitate the biosynthesis of 182 distinct potato secondary metabolites. Constraint-based modelling identifies that the activation of the largest amount of secondary (defence) pathways occurs at a decrease of the relative growth rate of potato leaf, due to the costs incurred by defence. We then obtain transcriptomics data from experiments exposing potato leaves to two biotic stress scenarios, a herbivore and a viral pathogen, and apply it as constraints to produce condition-specific models. We show that these models recapitulate experimentally observed decreases in relative growth rates under treatment, enabling us to pinpoint the metabolic rewiring underlying growth-defence trade-offs. Potato-GEM thus presents a useful resource to study and broaden our understanding of potato and general plant defence responses under stress conditions.

systems biology↗

bioRxiv2024

Integration of multi-omics and deep phenotyping provides novel insights into multiple abiotic stress responses in potato

Potato is highly water and space efficient but susceptible to abiotic stresses such as heat, drought, or flooding, which are severely exacerbated by climate change. Understanding of crop acclimation to abiotic stress, however, remains limited. Here, we present a comprehensive molecular and physiological high-throughput profiling of potato (Solanum tuberosum, cv. Desiree) under heat, drought and waterlogging applied as single stresses or in combinations designed to mimic realistic future scenarios. Stress-responses were monitored via daily phenotyping and multi-omics analyses of leaf samples comprising transcriptomics, proteomics, metabolomics and hormonomics at several timepoints during and after stress treatments. Additionally, critical metabolites of tuber samples were analysed at the end of the stress period. Integrative analysis of multi-omics data was performed using a bioinformatic pipeline, which was established here, based on machine learning and knowledge networks. Overall, waterlogging had the most immediate and dramatic effects on potato plants, interestingly activating ABA-responses similar to drought stress. In addition, we observed distinct stress signatures at multiple molecular levels in response to heat or drought and to a combination of both. In response to all treatments, we found a downregulation of photosynthesis at different molecular levels, an accumulation of minor amino acids and diverse stress induced hormones. Our integrative multi-omics analysis provides global insights into plant stress responses, facilitating improved breeding strategies towards climate-adapted potato varieties. One Sentence SummaryIntegrated multi-omics analysis of high-throughput phenotyping in potato reveals distinct molecular signatures of acclimation to single and combined abiotic stresses related to climate change.

plant biology↗

bioRxiv2023

Evidence based Unification of poTato gene models with UniTato collaborative genome browser

Potato (Solanum tuberosum) is the most popular tuber crop and model organism. Though its gene models are frequently updated, they are not unified, leading to missing or wrongly annotated genes. Here, we thus unify the recent potato double monoploid v4 and v6 gene models by automatic merging. We established an Apollo web server that enables access to the Unified poTato genome annotation database (UniTato) as well as further community-based manual curation at unitato.nib.si. We demonstrate how the web server interface can help to resolve problems with missing or misplaced genes and can be used to further update them or consolidate a wider set of gene models or genome information. Genome annotation files and a comprehensive translation table are provided at github.com/NIB-SI/unitato.

plant biology↗

bioRxiv2023

Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

Stress Knowledge Map (SKM, https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs describing current knowledge of biochemical, signalling, and regulatory molecular interactions in plants: a highly curated model of plant stress signalling (PSS, 543 reactions) and a large comprehensive knowledge network (CKN, 488,390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entrypoint for plant stress response investigations and the related growth tradeoffs. SKM provides interactive exploration of current knowledge. PSS is also formulated as qualitative and quantitative models for systems biology, and thus represents a starting point of a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation, design of validation experiments, or to gain new insights into experimental observations in plant biology.

plant biology↗

bioRxiv2019

What can we learn from over 100,000 Escherichia coli genomes?

The explosion of microbial genome sequences in public databases allows for large-scale population genomic studies of bacterial species, such as Escherichia coli. In this study, we examine and classify more than one hundred thousand E. coli and Shigella genomes. After removing outliers, a semi-automated Mash-based analysis of 10,667 assembled genomes reveals 14 distinct phylogroups. A representative genome or medoid identified for each phylogroup serves as a proxy to classify more than 95,000 unassembled genomes. This analysis shows that most sequenced E. coli genomes belong to 4 phylogroups (A, C, B1 and E2(O157)). Authenticity of the 14 phylogroups described is supported by pangenomic and phylogenetic analyses, which show differences in gene preservation between phylogroups. A phylogenetic tree constructed with 2,613 single copy core genes along with a matrix of phylogenetic profiles is used to confirm that the 14 phylogroups change at different rates of gene gain/loss/duplication. The methodology used in this work is able to identify previously uncharacterized phylogroups in E. coli species. Some of these new phylogroups harbor clonal strains that have undergone a process of genomic adaptation to the acquisition of new genomic elements related to virulence or antibiotic resistance. This is, to our knowledge, the largest E. coli genome dataset analyzed to date and provides valuable insights into the population structure of the species.

genomics↗