Abstract
cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10–100 nm) between molecular and cellular biology scales. cellPACK's modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive three-dimensional models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is available as open-source code, with tools for validation of models and with 'recipes' and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org/.
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Acknowledgements
We thank S. Takamori and colleagues for the model of an average synaptic vesicle, C.P. Arthur for his synaptic cleft tomogram model, K. Schulten and A. Shih for their HDL models from molecular dynamics, K. Schulten and J.R. Perilla for their extra HIV capsid models, M. Yeager for his HIV capsid model, T.D. Goddard for developing Chimera's .apr reader and for simplified HIV capsid models, J.A. Briggs and his lab for discussions on details on their HIV Ca tomography models, and J. Klusák (jiriklusak.cz) for his model of the HIV life cycle. We thank our colleagues for detailed suggestions on cellPACK experiments and extended analysis tools that can more easily bridge gaps to experimental biology. This is manuscript number 25098 from The Scripps Research Institute. This work was supported in part by a predoctoral fellowship from the US National Science Foundation (NSF 07576 to G.T.J.), gift donations from Autodesk, grants from the US National Institutes of Health (P41 GM103426 to L.A., M.F.S. and A.J.O.; P50GM103368 to D.S.G. and A.J.O.), a QB3 Fellowship grant from the California Institute for Quantitative Biosciences, qb3@UCSF, to G.T.J. and a UCSF School of Pharmacy, 2013 Mary Anne Koda-Kimble Seed Award for Innovation to G.T.J.
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Contributions
G.T.J. conceived of autoPACK and cellPACK and designed the original code, wrote the core code, designed user interfaces and edited interface code, conducted experiments and analyzed results. L.A. wrote core code, designed and wrote the interface code, wrote analysis code, and designed and conducted HIV experiments and analyses. M.A.-A. co-designed and wrote prototype autoPACK code. D.S.G. co-conceived of and guided cellPACK design and designed HIV experiments. M.F.S. transposed the prototype autoPACK code to Python with G.T.J., added cellPACK packing and grid preparation features, and structured and cleaned the first drafts of the current core code. A.J.O. guided code and experiment design and implementation.
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Integrated supplementary information
Supplementary Figure 1 autoPACK packs arbitrary shapes into arbitrarily shaped volumes with no overlaps by default.
(a) A recipe consisting of three sizes of the Stanford Bunny geometry (a canonical 3D computer graphics test model from Stanford University Computer Graphics Laboratory) is packed into a larger Stanford Bunny. (b) 10 large, 500 medium and 21,911 bunnies were packed in this model. (c) Molecule sized arbitrary shapes were used during the development transition from autoPACK to cellPACK. Here two organelle shapes are packed with different geometric recipes.
Supplementary Figure 2 User interfaces of cellPACK.
(a) User interfaces have been developed for modeling, animation, and analysis. uPy generates consistent GUIs and models across all supported molecular viewer and professional animation software packages to provide sensible modeling, animation, and analysis interfaces rather than constructing mesoscale viewers from scratch. Users can construct models de novo as described in the online methods and in greater detail in cellPACK’s online documentation, or they can adjust the parameters of existing models to modify cellPACK results. In either case, users can modify scripts directly or interact through provided GUIs and export the modified parameters as a new recipe. (b) Several online tools are available for model viewing and interaction. cellPACK results may be displayed online as libraries of static images, movies, zoomable images, and interactive 3D models. Online multiscale viewers such as Gigapan enable users to register, critique, and annotate models and provide interactive guided walkthroughs that highlight notable details. The gallery pages for each model at cellPACK.org currently encourage visitors to suggest changes to the models by submitting emails or commenting publically on the pages.
Supplementary Figure 3 Detailed histograms with labeled values plotting the frequency of distribution and orientation of recipes described in Figure 2 and shown at larger size here.
All graphs plot data averaged over 1000 independent runs chosen to have an error bound to be less than 5%. Histograms bar heights indicate the average frequency per bins with errors bars equal the margin of error at 95% confidence interval (1.96*SEM). (a) Histograms reveal biased packing in a recipe using random ingredient selection, (b) and uniform random distribution in a method that uses a weighted ingredient packing, which is the default method of autoPACK. (c–f) Two difficult ingredients (a concave C shape and a procedurally grown flexible fiber) pack randomly amongst a red convex polarized object that is given two intentional packing biases: a distribution40–42 and orientation43,44 preference for the lower left corner of the 2D fill area. All graphs show data averaged over 1000 independent runs chosen to have an error bound <5%. Histogram bar heights indicate the average frequency per bin with error bars equal to the margin of error at 95% confidence interval (1.96*SEM).
Supplementary Figure 4 Env quantification does not affect STED projection foci count.
Within the observed ranges of Env quantification, the number of trimeric envelope glycoprotein (Env) packed into a virion surface does not affect the foci count in any of the hypothesis models, which agrees with the original observations. Extended details available at http://mgldev.scripps.edu/projects/autoPACK/web/fluo_hiv.html#Table1 and at http://www.cellpack.org/documentation.
Supplementary Figure 5 Env quantification is highly sensitive to counting methods, and objective automated methods should be developed to reduce human biases.
(a) The first two images show the input Env positions, and the corresponding convolution (STED fluorescence simulation). The fluorescence images are filtered to remove the background (deleted background shown in blue). Depending on the values of a background threshold and a maximum number of pixels parameter, the counting algorithm will categorize the same image differently. This figure shows four different background removal threshold values (left) and how the counting is affected for different values for a maximum number of pixel parameter (plot on the right) for two different representative hypothesis models. (b) The distribution statistics are thus highly dependent on the technique used to distinguish one versus two versus multiple foci in each STED image. These foci were manually counted in the original paper and would need to be counted with our automated system for a more objective comparison to the cellPACK results. Extended details available at http://mgldev.scripps.edu/projects/autoPACK/web/fluo_hiv.html and at http://www.cellpack.org/documentation.
Supplementary Figure 6 cellPACK’s referential data structure enables any recipe to use any other recipe to construct nested hierarchies.
Semantic versioning tracks a history of all changes. Older ingredients can be overwritten by iterating the ingredient and pointing to the new version or by overriding at the recipe level.
Supplementary Figure 7 Overview of the cellPACK packing algorithm.
(a–b) make a selection box including parts of containers within an environment container. (c–d) build a grid based on the size of the smallest ingredient to pack. (e–g) compartmentalize the grid into containers and calculate distances to container surfaces. Right: pack and track as described in the figure labels.
Supplementary Figure 8 cellPACK builds a grid (spatial data) and frames other data onto this grid by connecting recipes to the grid points.
(a) An earlier recipe for HIV-1 version 0.1.2 shows two polyhedral surfaces that define the containers for the envelope and genome region as well as an orthogonal selection boundary to limit the packing of the surrounding blood plasma. (b) cellPACK builds a master grid bounded by the selection or environment volume and compartmentalizes the points.
Supplementary Figure 9 Procedural packing loops enable fiber and crystal growth.
A spring force binding partner “agent” behavior algorithm simulates non-specific binding with rigid body shape complementarity. An example of agent behavior attributed to an ingredient (blue isosceles triangle) that results (dotted triangle) in the agent being pulled with collision-avoidance of all nearby structures towards the filamentous structure by chance.
Supplementary Figure 10 An example of the procedural loop used to grow helical fibrous elements modeled (e.g., cytoskeletal actin demonstrated as symbolic As in Supplementary Fig. 1c).
The loop can be made recursive to handle branches or to grow at both ends with different probabilities for extension, etc. A similar loop could be used to extend crystals into any space-group.
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autoPACK/cellPACK code (ZIP 1872 kb)
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Johnson, G., Autin, L., Al-Alusi, M. et al. cellPACK: a virtual mesoscope to model and visualize structural systems biology. Nat Methods 12, 85–91 (2015). https://doi.org/10.1038/nmeth.3204
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DOI: https://doi.org/10.1038/nmeth.3204
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