Biology subjects

Olivier, B. G.

Publications and source records attributed to Olivier, B. G..

4 recordsLinked to original sources

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2019

A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. In this work, we performed a systematic assessment of the current genome-scale reconstruction software platforms. To meet our goal, we first defined a list of features for assessing software quality related to genome-scale reconstruction, which we expect to be useful for the potential users of these tools. Subsequently, we used the feature list to evaluate the performance of each tool. In order to assess the similarity of the draft reconstructions to high-quality models, we compared each tools output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We showed that none of the tools outperforms the others in all the defined features and that model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. Author SummaryMetabolic networks that comprise biochemical reactions at genome-scale have become very useful to study and predict the phenotype of important microorganisms. Several software platforms exist to build these metabolic networks. Based on different approaches and utilizing a variety of databases it is, unfortunately, unclear what are the best scenarios to use each of these tools. Hence, to understand the potential uses of these tools, we created a list of relevant features for metabolic reconstruction and we evaluated the tools in all these categories. Here, we show that none of the tools is better than the other in all the evaluated categories; instead, each tool is more suitable for particular purposes. Therefore, users should carefully select the tool(s) that best fit the purpose of their research. This is the first time these tools are systematically evaluated and this overview can be used as a guide for selecting the correct tool(s) for each case.

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↗