Hybrid Models and Biological Model Reduction with PyDSTool

Clewley, Robert

doi:10.1371/journal.pcbi.1002628

Cited by 82 publications

(82 citation statements)

References 48 publications

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“…XPPAUT [77], PyDSTool [78], and Brian [30] are prominent members of this class. Equations for XPPAUT are specified by a text file, but it is otherwise controlled through a GUI interface.…”

Section: Neuroscience Simulatorsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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Objective Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. Methods Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. Results Building on these standard practices, model sharing sites and tools have been developed that fit into several categories: 1. standardized neural simulators, 2. shared computational resources, 3. declarative model descriptors, ontologies and standardized annotations; 4. model sharing repositories and sharing standards. Conclusion A number of complementary innovations have been proposed to enhance sharing, transparency and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. Significance Model management will become increasingly important as multiscale models become larger, more detailed and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.

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“…XPPAUT [77], PyDSTool [78], and Brian [30] are prominent members of this class. Equations for XPPAUT are specified by a text file, but it is otherwise controlled through a GUI interface.…”

Section: Neuroscience Simulatorsmentioning

confidence: 99%

Reproducibility in Computational Neuroscience Models and Simulations

McDougal

Bulanova

Lytton

2016

IEEE Trans. Biomed. Eng.

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“…One time unit in the simulations corresponds to approximately 1 day. Basal parameter values of each model are listed in supplementary tables (see ‘Cell-to-Cell Variability’ subsection for details). All simulations and bifurcation analyses were performed with PyDSTool, a software environment for dynamical systems (Clewley 2012). …”

Section: A1 Dynamical Modelsmentioning

confidence: 99%

“…All simulations and bifurcation analyses were performed with PyDSTool, a software environment for dynamical systems (Clewley 2012). …”

Section: A1 Dynamical Modelsmentioning

confidence: 99%

A Mathematical Framework for Understanding Four-Dimensional Heterogeneous Differentiation of $$\hbox {CD4}^{+}$$ CD4 + T Cells

2015

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At least four distinct lineages of CD4+ T cells play diverse roles in the immune system. Both in vivo and in vitro, naïve CD4+ T cells often differentiate into a variety of cellular phenotypes. Previously, we developed a mathematical framework to study heterogeneous differentiation of two lineages governed by a mutual-inhibition motif. To understand heterogeneous differentiation of CD4+ T cells involving more than two lineages, we present here a mathematical framework for the analysis of multiple stable steady states in dynamical systems with multiple state variables interacting through multiple mutual-inhibition loops. A mathematical model for CD4+ T cells based on this framework can reproduce known properties of heterogeneous differentiation involving multiple lineages of this cell differentiation system, such as heterogeneous differentiation of TH1–TH2, TH1–TH17 and iTReg–TH17 under single or mixed types of differentiation stimuli. The model shows that high concentrations of differentiation stimuli favor the formation of phenotypes with co-expression of lineage-specific master regulators.

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“…It is of great interest to understand the conditions under which these three behaviours occur. We carried out a bifurcation analysis of the DP switch using the PyDSTool [60] in order to get an indication of the stabilities this model is capable of, and at which parameter ranges these are found (Fig. 4 b).…”

Section: Resultsmentioning

confidence: 99%

A computational method for the investigation of multistable systems and its application to genetic switches

et al. 2016

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BackgroundGenetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology.ResultsHere we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour.ConclusionsOur results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0375-z) contains supplementary material, which is available to authorized users.

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Hybrid Models and Biological Model Reduction with PyDSTool

Cited by 82 publications

References 48 publications

Reproducibility in Computational Neuroscience Models and Simulations

Reproducibility in Computational Neuroscience Models and Simulations

A Mathematical Framework for Understanding Four-Dimensional Heterogeneous Differentiation of $$\hbox {CD4}^{+}$$ CD4 + T Cells

A computational method for the investigation of multistable systems and its application to genetic switches

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