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:10.1016/j.isci.2019.08.045

Cited by 43 publications

(55 citation statements)

References 114 publications

(337 reference statements)

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“…Metaheuristic optimization algorithms are useful for a range of problems for which gradient-based methods are not feasible. Such algorithms are implemented in PyBioNetFit and have been demonstrated on a library of problems [8] including large rule-based models and stochastic models. A notable example problem features a rule-based model of TCR signal initiation simulated by network-free simulation [42].…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

“…A static penalty function takes a value of zero when an inequality constraint is satisfied and a value proportional to the extent of constraint violation when a constraint is violated (and thus is shaped like the relu function used in machine learning). This method is available for general use in PyBioNetFit [8].…”

Section: Parameter Estimation Using Qualitative Datamentioning

confidence: 99%

“…Various software tools such as COPASI [5], Data2Dynamics [6], AMICI [7], and PyBioNetFit [8] make parameterization of detailed models possible without the need for problem-specific code. PyBioNetFit and AMICI are the newest of these tools, and both provide features that are complementary to those available in older tools.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation and uncertainty quantification for systems biology models

Mitra

Hlavacek

2019

Current Opinion in Systems Biology

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Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena. Highlights• Models of immunoreceptor signaling often contain parameters that must be fit to data.• New tools including PyBioNetFit and AMICI support automated parameter estimation.• Optimization algorithms can be used to obtain point estimates of parameter values.• Parameter estimation can incorporate both quantitative and qualitative data.• Uncertainty quantification assesses confidence in parameter values and model predictions.

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Section: Metaheuristic Optimizationmentioning

confidence: 99%

Section: Parameter Estimation Using Qualitative Datamentioning

confidence: 99%

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Parameter estimation and uncertainty quantification for systems biology models

Mitra

Hlavacek

2019

Current Opinion in Systems Biology

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“…Fitting was performed using the differential evolution (DE) algorithm implemented in PyBioNetFit version 0.3.2 (https://github.com/lanl/PyBNF) (Mitra et al 2019). An important parameter of the DE algorithm is population size: this parameter was set to 200.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Fitting runs were allowed to continue until apparent convergence, i.e., until the value of the objective function in the optimization problem stopped decreasing. PyBioNetFit (Mitra et al 2019) is a software package that replaces BioNetFit (Thomas et al 2016). PyBioNetFit takes as input three file types: a BioNetGen input file (i.e., a BNGL-formatted model definition), a configuration file, and one or more EXP files with experimental data.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Integrating Multiplex SiMPull and Computational Modeling to Evaluate Combinatorial Aspects of EGFR Signaling

Salazar-Cavazos

Nitta

Mitra

et al. 2017

Preprint

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The single molecule pull-down (SiMPull) assay allows for capture of individual proteins or macromolecular complexes that can then be interrogated via single molecule imaging. We describe several technical improvements over previous protocols that make it possible to directly detect and quantify the phosphorylation state of thousands of individual membrane receptors, and thereby estimate both the fraction of receptors phosphorylated at specific tyrosine residues and the frequency of multisite phosphorylation. These improvements include 1) the reduction of autofluorescence in the green spectral channel; 2) a simplified imaging chamber that accommodates higher sample number with lower sample volume; 3) corrections for membrane receptor surface expression; 4) three-color multiplex imaging; and 5) corrections for steric hindrance of dual antibody binding. These improvements enabled the first direct detection of multisite phosphorylation on full-length Epidermal Growth Factor Receptor (EGFR) and revealed that phosphorylation fraction varied by tyrosine residue. These SiMPull measurements provide a new level of detail in the status of receptor phosphorylation that was previously inaccessible with traditional biochemical techniques. INTRODUCTIONThe ability of a cell to respond rapidly and specifically to changes in the surrounding environment is controlled by protein-protein interactions at the plasma membrane and along the signaling cascade. While much is known about the biochemical events that govern signaling pathways, this information has mostly been derived from population-based measurements that typically average over millions of cells and/or proteins. However, there is growing evidence that the heterogeneity of the system contributes to how cellular information is processed [1][2][3][4] . To better understand the role of protein phosphorylation heterogeneity in directing signaling outcomes, we have adapted the single molecule pull down (SiMPull) assay to identifying the phosphorylation state of individual receptors.SiMPull is a powerful technique that allows for interrogation of macromolecular complexes at the individual protein level. Jain et al. first demonstrated the ability of this technique to capture macromolecular complexes 5 . SiMPull samples are prepared in a manner similar to IP/Western Blot protocols, but the sample is interrogated using single molecule microscopy. Briefly, cells are lysed and the protein of interest is captured by antibodies bound to the coverglass. If the proteins are fluorescently tagged, either by fluorescent proteins or subsequent antibody labeling, their presence will be quantified by single molecule imaging (Fig. 1A).Here, we describe a modification of SiMPull for the study of phosphorylation patterns of transmembrane receptors. Traditionally, protein phosphorylation has been measured using ensemble techniques, such as Western Blot analysis or flow cytometry, which provide information on the relative changes of a protein phosphorylation amount. However, these techniques cannot p...

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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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PyBioNetFit and the Biological Property Specification Language

Cited by 43 publications

References 114 publications

Parameter estimation and uncertainty quantification for systems biology models

Parameter estimation and uncertainty quantification for systems biology models

Integrating Multiplex SiMPull and Computational Modeling to Evaluate Combinatorial Aspects of EGFR Signaling

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

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