Tellurium: An extensible python-based modeling environment for systems and synthetic biology

Choi, Kiri; Medley, Kyle; König, Matthias; Stocking, Kaylene; Smith, Lucian P.; Gu, Stanley; Sauro, Herbert M.

doi:10.1016/j.biosystems.2018.07.006

Cited by 148 publications

(141 citation statements)

References 29 publications

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“…In the presence of these various markup languages, the research group of Sauro [191,192] proposed the Tellurium platform as an integrated environment (i.e. models, Python code and documentation; similar to a Jupyter notebook) that is designed for model building, analysis, simulation and reproducibility in systems biology while facilitating the use of multiple, heterogeneous libraries, plugins as well as specialized modules/methods.…”

Section: Systems-based Approachesmentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

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Section: Systems-based Approachesmentioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

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“…To represent the pathway reactions, we used a generalized Michaelis-Menten rate law formulation (Chou and Talaly 1977). We specied irreversible rate laws for each enzymatic reaction in the pathway model using the Tellurium library (Choi et al 2018), as previously described (see Supplemental text and (Wheeler and Smith 2019)). Each enzyme rate law in the pathway has three dierent types of parameters: K cat ; the catalytic turnover rate, K m ; the Michaelis constant (related to a dissociation constant), and E t ; the enzyme concentration.…”

Section: Methodsmentioning

confidence: 99%

Structure and contingency determine mutational hotspots for flower color evolution

Wheeler¹,

Wing²,

Smith³

2020

Preprint

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Evolutionary genetic studies have uncovered abundant evidence for genomic hotspots of phenotypic evolution, as well as biased patterns of mutations at those loci. However, the theoretical basis for this concentration of particular types of mutations at particular loci remains largely unexplored. In addition, historical contingency is known to play a major role in evolutionary trajectories, but has not been reconciled with the existence of such hotspots. For example, do the appearance of hotspots and the fixation of different types of mutations at those loci depend on the starting state and/or on the nature and direction of selection? Here we use a computational approach to examine these questions, focusing the anthocyanin pigmentation pathway, which has been extensively studied in the context of flower color transitions. We investigate two transitions that are common in nature, the transition from blue to purple pigmentation and from purple to red pigmentation. Both sets of simulated transitions occur with a small number of mutations at just four loci and show strikingly similar peaked shapes of evolutionary trajectories, with the mutations of largest effect occurring early but not first. Nevertheless, the types of mutations (biochemical vs. regulatory) as well as their direction and magnitude are contingent on the particular transition. These simulated color transitions largely mirror findings from natural flower color transitions, which are known to occur via repeated changes at a few hotspot loci. Still, some types of mutations observed in our simulated color evolution are rarely observed in nature, suggesting that pleiotropic effects further limit the trajectories between color phenotypes. Overall, our results indicate that the branching structure of the pathway leads to a predictable concentration of evolutionary change at hotspot loci, but the types of mutations at these loci and their order is contingent on the evolutionary context.

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“…Chassagnole02: We used the Python Tellurium package (25) to simulate kinetic models that were in SBML format. The model was modified by 1) fixing stoichiometry in PTS system 2) extracellular volume, and 2) removing training-data specific equations for cofactors.…”

Section: Simulation Setupmentioning

confidence: 99%

Benchmarking kinetic models ofEscherichia colimetabolism

Shepelin

Machado

Nielsen

et al. 2020

Preprint

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Predicting phenotype from genotype is the holy grail of quantitative systems biology.Kinetic models of metabolism are among the most mechanistically detailed tools for phenotype prediction. Kinetic models describe changes in metabolite concentrations as a function of enzyme concentration, reaction rates, and concentrations of metabolic effectors uniquely enabling integration of multiple omics data types in a unifying mechanistic framework. While development of such models for Escherichia coli has been going on for almost twenty years, multiple separate models have been established and systematic independent benchmarking studies have not been performed on the full set of models available. In this study we compared systematically all recently published kinetic models of the central carbon metabolism of Escherichia coli . We assess the ease of use of the models, their ability to include omics data as input, and the accuracy of prediction of central carbon metabolic flux phenotypes. We conclude that there is no clear winner among the models when considering the resulting tradeoffs in performance

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Tellurium: An extensible python-based modeling environment for systems and synthetic biology

Abstract: tellurium.analogmachine.org.

Cited by 148 publications

References 29 publications

Towards reproducible computational drug discovery

Towards reproducible computational drug discovery

Structure and contingency determine mutational hotspots for flower color evolution

Benchmarking kinetic models ofEscherichia colimetabolism

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