Biology subjects

Dräger, A.

Publications and source records attributed to Dräger, A..

6 recordsLinked to original sources

bioRxiv2026

MCBO: Mammalian Cell Bioprocessing Ontology, A Hub-and-Spoke, IOF-Anchored Application Ontology

Mammalian cell-based biopharmaceutical manufacturing generates vast, heterogeneous datasets that remain fragmented due to the lack of a standardized metadata framework. A key challenge in biologics manufacturing is linking bioreactor conditions, cell line characteristics and recombinant product production. A datahub for mammalian cell bioprocessing, integrated by semantic technologies, will serve as a tool to understand and query the connections between these complex datasets. While existing ontologies cover general biological and experimental concepts, they often lack the operational specificity required to harmonize bioreactor conditions, cell line engineering, and product quality metrics. To address this specific gap, we present the Mammalian Cell Bioprocessing Ontology (MCBO), a hub-and-spoke application ontology built on Basic Formal Ontology (BFO) foundations and anchored to the Industrial Ontology Foundry (IOF) Core. MCBO formalizes the process-participant-quality modeling pattern, enabling precise tracking of culture environmental conditions as qualities of the physical culture system. We demonstrate the utility of MCBO through a central datahub populated with 723 curated cell culture process instances and 325 unique bioprocess samples from published studies. The framework is validated against eight competency questions implemented via SPARQL, demonstrating efficient cross-study querying of culture optimization, cell line engineering, and multi-omics integration. By providing a stable, schema-independent substrate for data harmonization, MCBO enables AI agent-powered, human-in-the-loop workflows and facilitates LLM-assisted extraction of structured metadata from legacy records. MCBO is open-source and designed for deployment behind institutional firewalls to support interoperable biomanufacturing intelligence while maintaining intellectual property sensitivity. MCBO is supported by the International Biomanufacturing Network (IBioNe), which aims to accelerate discoveries and developments by providing a network of biomanufacturing training and workforce development to educate the next generation of biomanufacturing experts. MCBO is evaluated using over 700 curated cell culture processes, validated against eight competency questions, and quality-controlled using automated ontology checks. Evaluation results and formal reasoning validation are provided in the Supplementary Materials. Availability: https://github.com/lewiscelllabs/mcbo

bioinformatics↗

bioRxiv2024

MCC: Automated Mass and Charge Curation at Genome-Scale Applied to C. tuberculostearicum

BackgroundFor many years, antibiotics reliably protected mankind against bacterial infections, including the respiratory tract colonizer Corynebacterium tuberculostearicum. However, the spread of antimicrobial resistance necessitates the search for new treatment options, where the microbiota may play a crucial role. One way to investigate the complex nature of bacteria and their interactions with human hosts or microbiota is through genome-scale metabolic models (GEMs). Constructing GEMs is labor-intensive and time-consuming. ResultsWe introduce the Python package Mass and Charge Curation (MCC), which implements a new automated algorithm to facilitate mass and charge balancing--one of the most time-consuming reconstruction steps. This package manipulates reconstructions by consolidating data from multiple resources and updating the notes-field with relevant changes. It also generates a visual comparison between draft and curated models, ensuring high-quality metabolic reconstructions. Using MCC, we developed a metabolic reconstruction of C. tuberculostearicum strain DSM 44922. The model was improved based on standardization policies, resulting in a functional, well-annotated, high-quality product. We also simulated the organisms growth in synthetic nasal medium 3 (SNM3). ConclusionsThe high-quality model iCTUB2024RM consistently resembles growth behavior under realistic conditions in an artificial human nasal environment, enhancing understanding of C. tuberculostearicum and its potential impact on health and disease. The curation process of this model led to the development of the MCC package, which facilitates the mass and charge balancing of arbitrary flux balance constraints (fbc) models in SBML format. AvailabilityMCC is freely available via PIP, from https://github.com/draeger-lab/MassChargeCuration/. The model iCTUB2024RM can be obtained as an SBML file wrapped in an OMEX archive from BioModels Database, accession MODEL2407310001. ImportanceThe rise of antibiotic resistance has made it essential to explore alternative treatments for bacterial infections, particularly those caused by respiratory tract colonizers like Corynebacterium tuberculostearicum. Understanding the metabolic behavior of these bacteria and their interactions with the human host or microbiota is crucial. genome-scale metabolic models (GEMs) are powerful tools for investigating these interactions, but they are time-consuming to build. Our new Python package, Mass and Charge Curation (MCC), automates a crucial step in the GEM reconstruction process-mass and charge balancing-making it more efficient and reliable. By applying this tool, we developed a high-quality, functional metabolic model for C. tuberculostearicum (iCTUB2024RM), which provides deeper insights into the organisms growth in a simulated human nasal environment. This work offers a foundation for future research into microbial communities and their role in human health.

systems biology↗

bioRxiv2023

Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum

Genome-scale metabolic models (GEMs) are organism specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Approaches towards modeling rarely studied organisms are scarce. We introduce a cost-effective and easy experimental protocol which can be adapted to other organisms as well. Furthermore, the comparability of growth kinetics and in silico growth rates is discussed. This work introduces five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and experimentally validated by laboratory experiments conducted specifically for this purpose. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.

systems biology↗

bioRxiv2023

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

bioRxiv2018

Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.\n\nHere, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the models performance parameters, which supports informed model development and facilitates error detection.\n\nMemote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

systems biology↗

bioRxiv2018

Harmonizing semantic annotations for computational models in biology

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition, and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current semantic annotation practices among the COmputational Modeling in BIology NEtwork (COMBINE) community and provide a set of recommendations for building a consensus approach to semantic annotation.

scientific communication and education↗