<scp>SBML</scp>Level 3: an extensible format for the exchange and reuse of biological models

Keating, Sarah M.; Waltemath, Dagmar; König, Matthias; Zhang, Fengkai; Dräger, Andreas; Chaouiya, Claudine; Bergmann, Frank; Finney, Andrew; Gillespie, Colin S.; Helikar, Tomáš; Hoops, Stefan; Sheriff, Rahuman S. Malik; Moodie, Stuart; Moraru, Ion I.; Myers, Chris J.; Naldi, Aurélien; Olivier, Brett G.; Sahle, Sven; Schaff, James C.; Smith, Lucian P.; Swat, Maciej; Thieffry, Denis; Watanabe, Leandro; Wilkinson, Darren J.; Blinov, Michael L.; Begley, Kimberly; Faeder, James R.; Gómez, Harold F.; Hamm, Thomas M.; Inagaki, Yuichiro; Liebermeister, Wolfram; Lister, Allyson L.; Lucio, Daniel; Mjolsness, Eric; Proctor, Carole J.; Raman, Karthik; Rodríguez, Nicolás; Shaffer, Clifford A.; Shapiro, Bruce E.; Stelling, Joerg; Swainston, Neil; Tanimura, Naoki; Wagner, John; Meier‐Schellersheim, Martin; Sauro, Herbert M.; Palsson, Bernhard Ø.; Bolouri, Hamid; Kitano, Hiroaki; Funahashi, Akira; Hermjakob, Henning; Doyle, John C.; Hucka, Michael; Adams, Richard; Allen, Nicholas; Angermann, B.; Antoniotti, Marco; Bader, Gary D.; Červený, Jan; Courtot, Mélanie; Cox, Chris D.; Pezze, Piero Dalle; Demir, Emek; Denney, William S.; Dharuri, Harish; Dorier, Julien; Drasdo, Dirk; Ebrahim, Ali; Eichner, Johannes; Elf, Johan; Endler, Lukas; Evelo, Chris T.; Flamm, Christoph; Fleming, Ronan M. T.; Fröhlich, Martina; Glonț, Mihai; Gonçalvès, Emanuel; Golebiewski, Martin; Grabski, Hovakim; Gutteridge, Alex; Hachmeister, Damon; Harris, Leonard A.; Heavner, Benjamin D.; Henkel, Ron; Hlavacek, William S.; Hu, Bin; Hyduke, Daniel R.; Jong, Hidde de; Juty, Nick; Karp, Peter D.; Karr, Jonathan R.; Kell, Douglas B.; Keller, Roland; Kiselev, Ilya; Klamt, Steffen; Klipp, Edda; Knüpfer, Christian; Kolpakov, Fedor A.; Krause, Falko; Kutmon, Martina; Laibe, Camille; Lawless, Conor; Li, Lü; Loew, Leslie M.; Machné, Rainer; Matsuoka, Yoshikazu; Mendes, Pedro; Mi, Huaiyu; Mittag, Florian; Monteiro, Pedro T.; Natarajan, Kedar Nath; Nielsen, Poul M. F.; Nguyen, Tramy; Palmisano, Alida; Pettit, Jean Baptiste; Pfau, Thomas; Phair, Robert D.; Radivoyevitch, Tomas; Rohwer, Johann M.; Ruebenacker, Oliver; Sáez-Rodríguez, Julio; Scharm, Martin; Schmidt, Henning; Schreiber, Falk; Schubert, Michaël; Schulte, Roman; Sealfon, Stuart C.; Smallbone, Kieran; Soliman, Sylvain; Stefan, Melanie I.; Sullivan, Devin P.; Takahashi, Koichi; Teusink, Bas; Tolnay, David; Vazirabad, Ibrahim; Kamp, Axel von; Wittig, Ulrike; Wrzodek, Clemens; Wrzodek, Finja; Xénarios, Ioannis; Zhukova, Anna; Zucker, Jeremy

doi:10.15252/msb.20199110

Cited by 238 publications

(210 citation statements)

References 131 publications

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“…We propose that next-generation GSMs should contain (hard-coded) laboratory data about their included BOF(s). Community-driven adjustments to the current version of the SBML format might be required, as has recently happened for a different challenge [37]. Including additional fields in the SBML format regarding the BOF offers the possibility of a diverse range of methods for BOF combinations, whether based on the methods presented here, or something completely different.…”

Section: Discussionmentioning

confidence: 99%

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Schulz

Kumelj

Karlsen

et al. 2020

Preprint

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Genome-scale metabolic modeling is an important tool in understanding metabolism, by enhancing collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A central assumption in the construction and use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.However, a cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a first conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.Author summaryChanges in the environment promote changes in an organism’s metabolism. To achieve balanced growth states for near-optimal function, cells respond through metabolic rearrangements, which may influence the biosynthesis of metabolic precursors for building a cell’s molecular constituents. Therefore, it is necessary to take the dependence of biomass composition on environmental conditions into consideration. While measuring the biomass composition for some environments is possible, and should be done, it cannot be completed for all possible environments.In this work, we propose two main approaches, BTW and HIP, for addressing the challenge of estimating biomass composition in response to environmental changes. We evaluate the phenotypic consequences of BTW and HIP by characterizing their effect on growth, secretion potential, respiratory efficiency, and gene essentiality of a cell.Our work constitutes a first conceptual step in accounting for the influence of growth conditions on biomass composition, and in turn the biomass composition’s effect on metabolic phenotypic traits, within constraint-based modelling. As such, we believe it will improve the relevance of constraint-based methods in metabolic engineering and drug discovery, since the biosynthetic potential of microbes for generating industrially relevant products or drugs often is closely linked to their biomass composition.

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Section: Discussionmentioning

confidence: 99%

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Schulz

Kumelj

Karlsen

et al. 2020

Preprint

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“…An upper-case I refers to the immune system. SBML [26,27] defines the units item and dimensionless to indicate that a quantity occurs in a piece number or that its unit originates from the cancellation of other units.…”

Section: Mucosa Compartment Containing Yementioning

confidence: 99%

“…The Maple workbook 3. The described model is available in SBML format [26] (Level 3 Version 2 [27]) from BioModels database [36] under model identifier MODEL2002070001.…”

Section: The Eigenvalues Of Trivial Equilibrium Pointmentioning

confidence: 99%

Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection

Mostolizadeh

Dräger

2020

Biology

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The complex interplay between pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism Yersinia entercolitica (Ye) is one of the five top frequent causes of bacterial gastroenteritis based on the Epidemiological Bulletin of the Robert Koch Institute (RKI), 10 September 2020. A fundamental challenge in predicting the course of an infection is to understand whether co-infection with two Yersinia strains, differing only in their capacity to resist killing by the host immune system, may decrease the overall virulence by competitive exclusion or increase it by acting cooperatively. Herein, we study the primary interactions among Ye, the host immune system and the microbiota, and their influence on Yersinia population dynamics. The employed model considers commensal bacterial in two host compartments (the intestinal mucosa the and lumen), the co-existence of wt and mut Yersinia strains, and the host immune responses. We determine four possible equilibria: disease-free, wt-free, mut-free, and co-existence of wt and mut in equilibrium. We also calculate the reproduction number for each strain as a threshold parameter to determine if the population may be eradicated or persist within the host. We conclude that the infection should disappear if the reproduction numbers for each strain fall below one, and the commensal bacteria growth rate exceeds the pathogen’s growth rate. These findings will help inform medical control strategies. The supplement includes the MATLAB source script, Maple workbook, and figures.

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“…The Systems Biology Simulation Core Library (SBSCL) is an open-source, cross-platform pure Java™ programming library that numerically solves systems biology models in multiple mathematical frameworks. A popular file format for representing computational models in a standard way and facilitating the exchange of models between different tools is the Systems Biology Markup Language (SBML) 7 . SBML encodes biological models in a declarative form.…”

Section: Introductionmentioning

confidence: 99%

“…Constrained optimization solver: SBML Level 37 combined with the fbc package 12 added support for constrainedbased models and their analysis. Typically, Flux Balance Analysis (FBA) is used for such time-invariant steady-state CORE The capabilities of the Systems Biology Simulation Core Library (SBSCL) as an overview.…”

mentioning

confidence: 99%

The Systems Biology Simulation Core Library

Panchiwala¹,

Shah²,

Planatscher³

et al. 2020

Preprint

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Summary: Studying biological systems generally relies on computational modeling and simulation, e.g., for model-driven discovery and hypothesis testing. Progress in standardization efforts led to the development of interrelated file formats to exchange and reuse models in systems biology, such as SBML, the Simulation Experiment Description Markup Language (SED-ML), or the Open Modeling EXchange format (OMEX). Conducting simulation experiments based on these formats requires efficient and reusable implementations to make them accessible to the broader scientific community and to ensure the reproducibility of the results. The Systems Biology Simulation Core Library (SBSCL) provides interpreters and solvers for these standards as a versatile open-source API in Java™. The library simulates even complex bio-models and supports deterministic Ordinary Differential Equations (ODEs); Stochastic Differential Equations (SDEs); constraint-based analyses; recent SBML and SED-ML versions; exchange of results, and visualization of in silico experiments; open modeling exchange formats (COMBINE archives); hierarchically structured models; and compatibility with standard testing systems, including the Systems Biology Test Suite and published models from the BioModels and BiGG databases. Availability and implementation: SBSCL is freely available at https://draeger-lab.github.io/SBSCL/.

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SBMLLevel 3: an extensible format for the exchange and reuse of biological models

Cited by 238 publications

References 131 publications

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Computational Model Informs Effective Control Interventions against Y. enterocolitica Co-Infection

The Systems Biology Simulation Core Library

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