BioModels—15 years of sharing computational models in life science

Sheriff, Rahuman S. Malik; Glonț, Mihai; Nguyen, Tung V. N.; Tiwari, Krishna Kumar; Roberts, Matthew G; Xavier, Ashley; Vu, Minh Tuan; Men, Jinghao; Maire, Matthieu; Kananathan, Sarubini; Fairbanks, Emma L.; Meyer, Johannes P; Arankalle, Chinmay; Varusai, Thawfeek; Knight-Schrijver, Vincent; Li, Lu; Dueñas-Roca, Corina; Dass, Gaurhari; Keating, Sarah M.; Park, Young Mi; Buso, Nicola; Rodríguez, Nicolás; Hucka, Michael; Hermjakob, Henning

doi:10.1093/nar/gkz1055

Cited by 233 publications

(206 citation statements)

References 39 publications

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“…This exercise was done in conjunction with the curation of models in BioModels. The manual curation of models in BioModels involves a two-step process (1) encoding models in standard formats and reproducing the simulation figures in the reference manuscript and (2) semantic enrichment of the model and its components 25 . The reproducibility assessment was done along with the first step of the model curation.…”

Section: Methodsologymentioning

confidence: 99%

“…Hence, to investigate the reproducibility crisis, we systematically analyzed mathematical models in conjunction with the curation process in BioModels repository 25 . BioModels (https://www.ebi.ac.uk/biomodels/) is one of the largest public open source databases of quantitative mathematical models, where the models are manually curated and semantically enriched.…”

Section: Introductionmentioning

confidence: 99%

“…7. SBML representation of all the analysed models were submitted to BioModels 25 . The reproducible ones were labelled as curated models.…”

mentioning

confidence: 99%

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Reproducibility in systems biology modelling

Tiwari

Kananathan

Roberts

et al. 2020

Preprint

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The reproducibility crisis has emerged as an important concern across many fields of science including life science, since many published results failed to reproduce. Systems biology modelling, which involves mathematical representation of biological processes to study complex system behaviour, was expected to be least affected by this crisis. While lack of reproducibility of experimental results and computational analysis could be a repercussion of several compounded factors, it was not fully understood why systems biology models with well defined mathematical expressions fail to reproduce and how prevalent it is. Hence, we systematically attempted to reproduce 455 kinetic models of biological processes published in peer-reviewed research articles from 152 journals; which is collectively a work of about 1400 scientists from 49 countries. Our investigation revealed that about half (49%) of the models could not be reproduced using the information provided in the published manuscripts. With further effort, an additional 12% of the models could be reproduced either by empirical correction or support from authors. The other 37% remained non-reproducible models due to missing parameter values, missing initial concentration, inconsistent model structure, or a combination of these factors. Among the corresponding authors of the non-reproducible model we contacted, less than 30% responded. Our analysis revealed that models published in journals across several fields of life science failed to reproduce, revealing a common problem in the peer-review process. Hence, we propose an 8-point reproducibility scorecard that can be used by authors, reviewers and journal editors to assess each model and address the reproducibility crisis.

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Section: Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reproducibility in systems biology modelling

Tiwari

Kananathan

Roberts

et al. 2020

Preprint

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“…I downloaded the kinetic metabolic model of E. coli glycolysis by Chassagnole et al [74] from the BioModels database [85] Table 4 and removing other metabolites and enzymes. Each sub-module has one input and one output metabolite.…”

Section: Kinetic Model Of Glycolysismentioning

confidence: 99%

Emergence and Propagation of Epistasis in Metabolic Networks

Kryazhimskiy

2020

Preprint

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AbstractGenome-wide measurements of epistasis are often used to probe functional relationships between genes. However, we lack a theory for understanding how functional relationships at the molecular level translate into epistasis measured at the level of whole-organism phenotypes, such as fitness. Here, I develop a mathematical model of a hierarchical metabolic network to address this gap. I derive how the topological relationship between reactions affected by mutations translates into epistasis and how this epistasis propagates from lower- to higher-level phenotypes in the network. An analysis of a computational model of glycolysis indicates that epistasis in more realistic metabolic networks follows similar albeit more complex patterns. These results imply that epistasis coefficients measured for high-level phenotypes carry some but not all information about the underlying functional relationships between gene products. They also suggest that pairwise inter-gene epistasis should be common and should depend on the genetic background and environment.

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“…In this work, we benchmark numerical integration methods and their hyperparameters. We established a benchmark collection of 167 models from the two freely accessible databases BioModels [Li et al, 2010, Malik-Sheriff et al, 2019 and JWS Online [Olivier and Snoep, 2004], which covers a broad range of different properties. These models were simulated using various ODE solver algorithms implemented in the SUNDIALS package CVODES [Cohen et al, 1996, Hindmarsh et al, 2005, a widely used state-of-the-art toolbox for solving differential equations, which offers a set of different methods.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of numerical integration methods for ODE models of biological systems

Städter

Schälte

Schmiester

et al. 2020

Preprint

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Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 167 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models.

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BioModels—15 years of sharing computational models in life science

Cited by 233 publications

References 39 publications

Reproducibility in systems biology modelling

Reproducibility in systems biology modelling

Emergence and Propagation of Epistasis in Metabolic Networks

Benchmarking of numerical integration methods for ODE models of biological systems

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