PyBioNetFit and the Biological Property Specification Language

Mitra, Eshan D.; Suderman, Ryan; Colvin, Joshua; Ionkov, Alexander; Hu, Andrew; Sauro, Herbert M.; Posner, Richard G.; Hlavacek, William S.

doi:10.2139/ssrn.3382545

Cited by 14 publications

(18 citation statements)

References 93 publications

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“…7). Parameters were adjusted to the data using the differential evolution algorithm of PyBioNetFit 53 . The rate of T69 and T71 phosphorylation by JNK was set to be equal (k1).…”

Section: Methodsmentioning

confidence: 99%

Co-regulation of the transcription controlling ATF2 phosphoswitch by JNK and p38

et al. 2020

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Transcription factor phosphorylation at specific sites often activates gene expression, but how environmental cues quantitatively control transcription is not well-understood. Activating protein 1 transcription factors are phosphorylated by mitogen-activated protein kinases (MAPK) in their transactivation domains (TAD) at so-called phosphoswitches, which are a hallmark in response to growth factors, cytokines or stress. We show that the ATF2 TAD is controlled by functionally distinct signaling pathways (JNK and p38) through structurally different MAPK binding sites. Moreover, JNK mediated phosphorylation at an evolutionarily more recent site diminishes p38 binding and made the phosphoswitch differently sensitive to JNK and p38 in vertebrates. Structures of MAPK-TAD complexes and mechanistic modeling of ATF2 TAD phosphorylation in cells suggest that kinase binding motifs and phosphorylation sites line up to maximize MAPK based co-regulation. This study shows how the activity of an ancient transcription controlling phosphoswitch became dependent on the relative flux of upstream signals.

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“…7). Parameters were adjusted to the data using the differential evolution algorithm of PyBioNetFit 53 . The rate of T69 and T71 phosphorylation by JNK was set to be equal (k1).…”

Section: Methodsmentioning

confidence: 99%

Co-regulation of the transcription controlling ATF2 phosphoswitch by JNK and p38

et al. 2020

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“…The parameters were estimated by minimizing this penalized objective function. This approach was implemented in the toolbox pyBioNetFit (Mitra et al 2019), making it generally applicable to other problems and recently extended using a probabilistic distance measure (Mitra and Hlavacek 2020).…”

Section: Introductionmentioning

confidence: 99%

“…This approach was implemented in the toolbox pyBioNetFit (Mitra et al. 2019 ), making it generally applicable to other problems and recently extended using a probabilistic distance measure (Mitra and Hlavacek 2020 ). (2) Pargett and Umulis ( 2013 ) and Pargett et al.…”

Section: Introductionmentioning

confidence: 99%

Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach

2020

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Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

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“…Experimental kinetic information can be retrieved in SBML format from the SABIO-Reaction Kinetics database (Wittig et al,2017). Tools such as COPASI (Hoops et al, 2006) and PyBioNetFit (Mitra et al, 2019) provide facilities to estimate parameters and to simulate the model with various algorithms. Other SBML-enabled tools such as Tellurium (Medley et al, 2018) and PySCeS (Olivier et al, 2005) provide capabilities such as identifiability and bifurcation analysis.…”

Section: Sbml Throughout the Model Life Cyclementioning

confidence: 99%

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

Keating¹,

Waltemath²,

König³

et al. 2020

Molecular Systems Biology

238

202

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Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

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PyBioNetFit and the Biological Property Specification Language

Cited by 14 publications

References 93 publications

Co-regulation of the transcription controlling ATF2 phosphoswitch by JNK and p38

Co-regulation of the transcription controlling ATF2 phosphoswitch by JNK and p38

Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach

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

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