SBtab: a flexible table format for data exchange in systems biology

Lubitz, Timo; Hahn, Jens; Bergmann, Frank; Noor, Elad; Klipp, Edda; Liebermeister, Wolfram

doi:10.1093/bioinformatics/btw179

Cited by 36 publications

(37 citation statements)

References 9 publications

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“…A Matlab implementation of Model Balancing, together with example models and data, is available at https: //github.com/liebermeister/cmb. The file format for models and data (kinetic constants, fluxes, metabolite levels, protein levels) is SBtab [38] and metabolic networks can be defined in SBML [39] i . Uncorrelated priors for these variables yield a meaningful correlated prior for the metabolite levels, and a similar splitting can be used for enzyme levels.…”

Section: Methodsmentioning

confidence: 99%

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

Liebermeister

Noor

2019

Preprint

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Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. In theory, kinetic constants can be fitted to measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, but these estimation problems are typically non-convex. This makes them hard to solve, especially if models are large. Here I assume that the metabolic fluxes are given and show that consistent kinetic constants, metabolite levels, and enzyme levels can then be found by solving a convex optimality problem. If logarithmic kinetic constants and metabolite concentrations are used as free variables and if Gaussian priors are employed, the posterior density is strictly convex. The resulting estimation method, called model balancing, can employ a wide range of rate laws, accounts for thermodynamic constraints on parameters, and considers the dependences between flux directions and metabolite concentrations through thermodynamic forces. It can be used to complete and adjust available data, to estimate in-vivo kinetic constants from omics data, or to construct plausible metabolic states with a predefined flux distribution. To demonstrate model balancing and to assess its practical use, I balance a model of E. coli central metabolism with artificial or experimental data. The tests show what information about kinetic constants can be extracted from omics data and reveal practical limits of estimating kinetic constants in vivo.Large models have been parameterised [21,22], and pipelines for model parameterization have been developed [23,24]. Finally, even if parameters are unknown, methods for parameter sampling and ensemble modelling allow to find plausible parameter sets [25] and to draw conclusions about possible dynamic behaviour [26].The key problem here is to obtain realistic, consistent values of kinetic constants. In-vivo values are hard to measure, and in-vitro values as proxies may be unreliable -or at least, this is hard to assess unless in-vivo values are known. So how can we infer model parameters from omics data? To obtain realistic model parameters and metabolic states, various types of measurement data must be combined. In an ideal case, in which kinetic constants and enzyme concentrations are precisely known, metabolite levels and fluxes could be computed by simulating the model. In another ideal case, in which enzyme levels, metabolite levels and fluxes are precisely known for a number of states, we can solve for the kinetic constants. In reality, we are in between these two cases: data of different types are available, but these data are incomplete and too noisy to be used directly in models. In practice, in model construction we may have different aims, for example (i) finding consistent kinetic constants in plausible ranges; (ii) adjusting and completing a data set of measured kinetic constants to obtain consistent model parameters; (iii) estimating in-vivo kinetic constants from omics data (measured enzyme levels, metabolite levels, and fluxes); (iv) completing and adju...

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Section: Methodsmentioning

confidence: 99%

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

Liebermeister

Noor

2019

Preprint

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“…Parameter-free models were created automatically using the rxncon compiler ( The cell division cycle model is compiled in an SBtab compatible spreadsheet (Lubitz, Hahn et al 2016), and is available from https://github.com/rxncon/models (CDC_S_cerevisiae.xls). The model is fully referenced through the "reference" columns in the reaction and contingency sheet, and explained in detail in the supplementary material.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive, mechanistically detailed, and executable model of the Cell Division Cycle in Saccharomyces cerevisiae

Muenzner

Klipp

Krantz

2018

Preprint

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10Understanding how cellular functions emerge from the underlying molecular mechanisms is a 11 key challenge in biology. This will require computational models, whose predictive power is 12 expected to increase with coverage and precision of formulation. Genome-scale models 13 revolutionised the metabolic field and made the first whole-cell model possible. However, the 14 lack of genome-scale models of signalling networks blocks the development of eukaryotic 15 whole-cell models. Here, we present a comprehensive mechanistic model of the molecular 16 network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the 17 reaction-contingency language, to neutralise the scalability issues preventing formulation, 18 visualisation and simulation of signalling networks at the genome-scale. We use parameter-19 free modelling to validate the network and to predict genotype-to-phenotype relationships 20 down to residue resolution. This mechanistic genome-scale model offers a new perspective on 21 eukaryotic cell cycle control, and opens up for similar models -and eventually whole-cell 22 models -of human cells. 23. CC-BY-NC 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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“…The SBtab version of the original models of iCel1273 and ElegCyc were used [16]. For the merged WormJam model, an updated, intermediate version of the previously published version was used [10].…”

Section: Caenorhabditis Elegans Modelsmentioning

confidence: 99%

Suggestions for Standardized Identifiers for Fatty Acyl Compounds in Genome Scale Metabolic Models and Their Application to the WormJam Caenorhabditis elegans Model

Witting

2020

Metabolites

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Genome scale metabolic models (GSMs) are a representation of the current knowledge on the metabolism of a given organism or superorganism. They group metabolites, genes, enzymes and reactions together to form a mathematical model and representation that can be used to analyze metabolic networks in silico or used for analysis of omics data. Beside correct mass and charge balance, correct structural annotation of metabolites represents an important factor for analysis of these metabolic networks. However, several metabolites in different GSMs have no or only partial structural information associated with them. Here, a new systematic nomenclature for acyl-based metabolites such as fatty acids, acyl-carnitines, acyl-coenzymes A or acyl-carrier proteins is presented. This nomenclature enables one to encode structural details in the metabolite identifiers and improves human readability of reactions. As proof of principle, it was applied to the fatty acid biosynthesis and degradation in the Caenorhabditis elegans consensus model WormJam.

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SBtab: a flexible table format for data exchange in systems biology

Cited by 36 publications

References 9 publications

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

A comprehensive, mechanistically detailed, and executable model of the Cell Division Cycle in Saccharomyces cerevisiae

Suggestions for Standardized Identifiers for Fatty Acyl Compounds in Genome Scale Metabolic Models and Their Application to the WormJam Caenorhabditis elegans Model

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