COPASI—a COmplex PAthway SImulator

Hoops, Stefan; Sahle, Sven; Gauges, Ralph; Lee, Christine; Pahle, Jürgen; Simus, Natalia; Singhal, Mudita; Xu, Liang; Mendes, Pedro; Kummer, Ursula

doi:10.1093/bioinformatics/btl485

Cited by 2,264 publications

(2,048 citation statements)

References 40 publications

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“…This imposes a demand for systems biology community to develop tools that allow automatic sensitivity analysis of systems biology models. Several software tools have been developed to perform sensitivity analysis for biological models, for example, BioSens [82], COPASI [83], SBML-SAT [12], Systems Biology Toolbox 2 [84], SensSB [85], TinkerCell [86] and Virtual Cell [87]. These software tools can perform local sensitivity analysis and some of them are able to execute global sensitivity analysis (e.g.…”

Section: Software Tools For Sensitivity Analysis Of Systems Biology Mmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Systems Biology

330

263

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With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

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Section: Software Tools For Sensitivity Analysis Of Systems Biology Mmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Systems Biology

330

263

View full text Add to dashboard Cite

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“…Parameters of binding kinetics from FRET measurements were obtained by fitting a tandem two-site model (Jakubík et al, 2000) to the pooled data with subtracted background values using the program COPASI (www.copasi.org) (Hoops et al, 2006). Initial parameter estimates and background values were obtained by fitting two exponential growth and two exponential decay functions, respectively, to the individual data sets using the program Grace.…”

Section: Data Treatmentmentioning

confidence: 99%

Molecular Mechanisms of Methoctramine Binding and Selectivity at Muscarinic Acetylcholine Receptors

et al. 2014

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Methoctramine (N,-methyl]hexyl]-1,8-octane] diamine) is an M 2 -selective competitive antagonist of muscarinic acetylcholine receptors and exhibits allosteric properties at high concentrations. To reveal the molecular mechanisms of methoctramine binding and selectivity we took advantage of reciprocal mutations of the M 2 and M 3 receptors in the second and third extracellular loops that are involved in the binding of allosteric ligands. To this end we performed measurements of kinetics of the radiolabeled antagonists N-methylscopolamine (NMS) in the presence of methoctramine and its precursors, fluorescence energy transfer between green fluorescent protein-fused receptors and an Alexa-555-conjugated precursor of methoctramine, and simulation of molecular dynamics of methoctramine association with the receptor. We confirm the hypothesis that methoctramine high-affinity binding to the M 2 receptors involves simultaneous interaction with both the orthosteric binding site and the allosteric binding site located between the second and third extracellular loops. Methoctramine can bind solely with low affinity to the allosteric binding site on the extracellular domain of NMSoccupied M 2 receptors by interacting primarily with glutamate 175 in the second extracellular loop. In this mode, methoctramine physically prevents dissociation of NMS from the orthosteric binding site. Our results also demonstrate that lysine 523 in the third extracellular loop of the M 3 receptors forms a hydrogen bond with glutamate 219 of the second extracellular loop that hinders methoctramine binding to the allosteric site at this receptor subtype. Impaired interaction with the allosteric binding site manifests as low-affinity binding of methoctramine at the M 3 receptor.

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“…Numerical calculation employed the metabolic simulation package COPASI (Hoops et al 2006), which is freely available from http://www.copasi.org. Here the input reaction kinetics and the reaction network structure are translated automatically into ordinary differential equations.…”

Section: Softwarementioning

confidence: 99%

Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves

et al. 2008

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Aliphatic glucosinolates are a major class of defensive secondary metabolites in plants that are mostly derived from methionine. Occurring in different chain lengths, they show a structural diversity arising from the variable number of chain elongation cycles taking place during their biosynthesis. The key enzymes in determining glucosinolate chain length are the methylthioalkylmalate (MAM) synthases, MAM1 and MAM3, with MAM3 showing a broader substrate specificity than MAM1. A comparison of the measurements of wild type and MAM1 knockout mutant plants shows the following distinct changes in glucosinolate chain length profiles:(1) a reversal of the relative proportions of the two shortest glucosinolates, (2) a significant increase in the concentration of the longest glucosinolate, (3) an increase in total glucosinolate content in the mutant.MAM3 knockout mutants on the contrary differ from wild type plants by a pronounced abundance of the second shortest glucosinolate and the depletion of the two longest glucosinolates. To clarify the contribution of the multifunctional enzymes MAM1 and MAM3 to the glucosinolate profile of Arabidopsis thaliana leaves, we simulated glucosinolate biosynthesis in a kinetic model, taking into account the structure of the pathway and measured enzymatic properties. The predicted glucosinolate profiles show all characteristics of the actual differences between wild-type and MAM1 mutants or MAM3 mutants, respectively. The model strongly supports experimental indications that the two MAM activities are not independent of each other. In particular, it showed that an elevated expression of MAM3 in the MAM1 mutant is critical in determining the glucosinolate profile of this plant line. The simulation was critical for this finding since it allowed us to assess the individual effects of two processes-the knocking out of MAM1 and the overexpression of MAM3-that are difficult to separate experimentally.

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COPASI—a COmplex PAthway SImulator

Cited by 2,264 publications

References 40 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

Molecular Mechanisms of Methoctramine Binding and Selectivity at Muscarinic Acetylcholine Receptors

Mathematical modelling of aliphatic glucosinolate chain length distribution in Arabidopsis thaliana leaves

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