Guidelines for Reproducibly Building and Simulating Systems Biology Models

Medley, Kyle; Goldberg, Arthur P.; Karr, Jonathan R.

doi:10.1109/tbme.2016.2591960

“…Such tools are valuable because they enable parameterization and uncertainty quantification without the need for problem-specific coding. Such a workflow helps promote reproducible modeling [69,70], and allows modelers to focus on model analysis rather than debugging code. Table 1 summarizes four major software tools that fit this use case: Py-BioNetFit [8], COPASI [5,71], Data2Dynamics (D2D) [6], and AMICI [72].…”

Section: Software Toolsmentioning

confidence: 99%

Parameter estimation and uncertainty quantification for systems biology models

Mitra

¹

,

Hlavacek

²

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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“…64 In a similar spirit, the Whole-Cell project has published guidelines and key concepts for facilitating whole-cell modeling and the sharing of systems biology models. 65,66 The development of standards, tools, and infrastructure for facilitating interoperability between existing ones will prove to be a major enabler of integrative modeling in the biological sciences.…”

Section: Enabling Model Discovery and Interoperabilitymentioning

confidence: 99%

Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices

Sarma

¹

,

Faúndez

²

2017

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Integrative biological simulations have a varied and controversial history in the biological sciences. From computational models of organelles, cells, and simple organisms, to physiological models of tissues, organ systems, and ecosystems, a diverse array of biological systems have been the target of large-scale computational modeling efforts. Nonetheless, these research agendas have yet to prove decisively their value among the broader community of theoretical and experimental biologists. In this commentary, we examine a range of philosophical and practical issues relevant to understanding the potential of integrative simulations. We discuss the role of theory and modeling in different areas of physics and suggest that certain sub-disciplines of physics provide useful cultural analogies for imagining the future role of simulations in biological research. We examine philosophical issues related to modeling which consistently arise in discussions about integrative simulations and suggest a pragmatic viewpoint that balances a belief in philosophy with the recognition of the relative infancy of our state of philosophical understanding. Finally, we discuss community workflow and publication practices to allow research to be readily discoverable and amenable to incorporation into simulations. We argue that there are aligned incentives in widespread adoption of practices which will both advance the needs of integrative simulation efforts as well as other contemporary trends in the biological sciences, ranging from open science and data sharing to improving reproducibility.

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“…64 In a similar spirit, the WholeCell project has published guidelines and key concepts for facilitating whole-cell modeling and the sharing of systems biology models. 65,66 The development of standards, tools, and infrastructure for facilitating interoperability between existing ones will prove to be a major enabler of integrative modeling in the biological sciences.…”

Section: Enabling Model Discovery and Interoperabilitymentioning

confidence: 99%

Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices

Sarma¹,

Faúndez²

2017

Preprint

0

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Integrative biological simulations have a varied and controversial history in the biological sciences. From computational models of organelles, cells, and simple organisms, to physiological models of tissues, organ systems, and ecosystems, a diverse array of biological systems have been the target of large-scale computational modeling efforts. Nonetheless, these research agendas have yet to prove decisively their value among the broader community of theoretical and experimental biologists. In this commentary, we examine a range of philosophical and practical issues relevant to understanding the potential of integrative simulations. We discuss the role of theory and modeling in different areas of physics and suggest that certain sub-disciplines of physics provide useful cultural analogies for imagining the future role of simulations in biological research. We examine philosophical issues related to modeling which consistently arise in discussions about integrative simulations and suggest a pragmatic viewpoint that balances a belief in philosophy with the recognition of the relative infancy of our state of philosophical understanding. Finally, we discuss community workflow and publication practices to allow research to be readily discoverable and amenable to incorporation into simulations. We argue that there are aligned incentives in widespread adoption of practices which will both advance the needs of integrative simulation efforts as well as other contemporary trends in the biological sciences, ranging from open science and data sharing to improving reproducibility.

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Guidelines for Reproducibly Building and Simulating Systems Biology Models

Cited by 30 publications

References 48 publications

Parameter estimation and uncertainty quantification for systems biology models

Parameter estimation and uncertainty quantification for systems biology models

Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices

Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices

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