BioModels: expanding horizons to include more modelling approaches and formats

Glonț, Mihai; Nguyen, Tung V. N.; Graesslin, Martin; Hälke, Robert; Raza, Ali; Schramm, J.; Wimalaratne, Sarala M.; Kothamachu, Varun B.; Rodríguez, Nicolás; Swat, Maciej; Eils, Jürgen; Eils, Roland; Laibe, Camille; Sheriff, Rahuman S. Malik; Chelliah, Vijayalakshmi; Novère, Nicolas Le; Hermjakob, Henning

doi:10.1093/nar/gkx1023

Cited by 87 publications

(62 citation statements)

References 12 publications

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“…These models abound in the literature. Another option we suggest is to select a published dynamic (differential-equation-based) model, e.g., from the BioModels repository [40], automatically extract the underlying GRN, stochastically simulate this model to mimic a single-cell gene expression data set, invoke a GRN inference algorithm, and measure the accuracy of the output against the true GRN.…”

Section: Discussionmentioning

confidence: 99%

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa

Jalihal

Law

et al. 2019

Preprint

134

285

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We present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. Our contributions include a comprehensive evaluation pipeline based on simulated data from "toy", artificial networks with predictable cellular trajectories and on simulated data from carefully-curated Boolean models. We develop a strategy to simulate these two types of data that avoids the pitfalls of existing strategies that have been used to mimic bulk transcriptional data. We found that the accuracy of the algorithms measured in terms of AUROC and AUPRC was moderate, by and large, although the methods were better in recovering interactions in the artificial networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. There were an excess of feedforward loops in predicted networks than in the Boolean models. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of these algorithms. The outputs of the methods were quite inconsistent with each other, indicating that combining these approaches using ensembles is likely to be challenging. We present recommendations on how to create simulated gene expression datasets for testing GRN inference algorithms. We suggest that new ideas for avoiding the prediction of indirect interactions appear to be necessary to improve the accuracy of GRN inference algorithms for single cell gene expression data. Simulated data from synthetic networks Simulated data from curated models LEAP PIDC GRN inference methods Predicted networks SCODE LEAP PIDC SCODE Run algorithms Parameter search Software run time Evaluate network motifs ROC Early Precision Stability of inferred networks

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Section: Discussionmentioning

confidence: 99%

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa

Jalihal

Law

et al. 2019

Preprint

134

285

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“…The resultant final Xoo GEM ( i XOO673) contains 673 genes, 570 proteins, 839 reactions, and 808 unique metabolites (for more details, refer to Tables and ). The model is also available as Systems Biology Markup Language (SBML) file (level 3, version 1, http://sbml.org/) (Data ) and can be downloaded from BioModels database (Glont et al , ) with identifier MODEL1912100001.…”

Section: Resultsmentioning

confidence: 99%

Genome‐scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae

Koduru

Kim

Mohanty

et al. 2020

Molecular Plant Pathology

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Xanthomonas oryzae pv. oryzae (Xoo) is a vascular pathogen that causes leaf blight in rice, leading to severe yield losses. Since the usage of chemical control methods has not been very promising for the future disease management, it is of high importance to systematically gain new insights about Xoo virulence and pathogenesis, and devise effective strategies to combat the rice disease. To do this, we reconstructed a genome‐scale metabolic model of Xoo (iXOO673) and validated the model predictions using culture experiments. Comparison of the metabolic architecture of Xoo and other plant pathogens indicated that the Entner–Doudoroff pathway is a more common feature in these bacteria than previously thought, while suggesting some of the unique virulence mechanisms related to Xoo metabolism. Subsequent constraint‐based flux analysis allowed us to show that Xoo modulates fluxes through gluconeogenesis, glycogen biosynthesis, and degradation pathways, thereby exacerbating the leaf blight in rice exposed to nitrogenous fertilizers, which is remarkably consistent with published experimental literature. Moreover, model‐based interrogation of transcriptomic data revealed the metabolic components under the diffusible signal factor regulon that are crucial for virulence and survival in Xoo. Finally, we identified promising antibacterial targets for the control of leaf blight in rice by using gene essentiality analysis.

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“…As is the case for genomics data sets, journals could request authors to provide models in an established repository. Two such model repositories currently exist: BioModels geared toward cell‐ and molecular‐based systems models and DDMoRE for pharmacometrics. Consortiums have also been established to develop more comprehensive tissue‐ and disease‐focused models, such as DILIsym (drug‐induced liver injury), Certara's QSP Immunogenicity Consortium, or the Critical Path for Alzheimer's Disease; hence, these may serve in specific cases.…”

Section: Best Practice Recommendations For Publishing Qsp Modelsmentioning

confidence: 99%

Reproducibility of Quantitative Systems Pharmacology Models: Current Challenges and Future Opportunities

Kirouac

Cicali

Schmidt

2019

CPT Pharmacom & Syst Pharma

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The provision of model code is required for publication in CPT: Pharmacometrics & Systems Pharmacology , enabling quantitative systems pharmacology (QSP) model availability. A searchable repository of published QSP models would enhance model accessibility. We assess the feasibility of establishing such a resource based on 18 QSP models published in this journal. However, because of the diversity of software platforms (nine), file formats, and functionality, such a resource is premature. We evaluated 12 of the models (those coded in R, PK ‐Sim/ MoBi, and MATLAB) for functionality. Of the 12, only 4 were executable in that figures from the associated manuscript could be generated via a “run” script. Many researchers are aware of the challenges involved in repurposing published models. We offer some ideas to enable model sharing going forward, including annotation guidelines, standardized formats, and the inclusion of “run” scripts. If practitioners can agree to some minimum standards for the provision of model code, model reuse and extension would be accelerated.

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BioModels: expanding horizons to include more modelling approaches and formats

Cited by 87 publications

References 12 publications

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Genome‐scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae

Reproducibility of Quantitative Systems Pharmacology Models: Current Challenges and Future Opportunities

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