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. 2024 Mar 21;25(1):121.
doi: 10.1186/s12859-024-05739-0.

Cell4D: a general purpose spatial stochastic simulator for cellular pathways

Donny Chan #  1   2 , Graham L Cromar #  1 , Billy Taj  1 , John Parkinson  3   4   5
Affiliations

Cell4D: a general purpose spatial stochastic simulator for cellular pathways

Donny Chan et al. BMC Bioinformatics. .

Abstract

Background: With the generation of vast compendia of biological datasets, the challenge is how best to interpret 'omics data alongside biochemical and other small-scale experiments to gain meaningful biological insights. Key to this challenge are computational methods that enable domain-users to generate novel hypotheses that can be used to guide future experiments. Of particular interest are flexible modeling platforms, capable of simulating a diverse range of biological systems with low barriers of adoption to those with limited computational expertise.

Results: We introduce Cell4D, a spatial-temporal modeling platform combining a robust simulation engine with integrated graphics visualization, a model design editor, and an underlying XML data model capable of capturing a variety of cellular functions. Cell4D provides an interactive visualization mode, allowing intuitive feedback on model behavior and exploration of novel hypotheses, together with a non-graphics mode, compatible with high performance cloud compute solutions, to facilitate generation of statistical data. To demonstrate the flexibility and effectiveness of Cell4D, we investigate the dynamics of CEACAM1 localization in T-cell activation. We confirm the importance of Ca2+ microdomains in activating calmodulin and highlight a key role of activated calmodulin on the surface expression of CEACAM1. We further show how lymphocyte-specific protein tyrosine kinase can help regulate this cell surface expression and exploit spatial modeling features of Cell4D to test the hypothesis that lipid rafts regulate clustering of CEACAM1 to promote trans-binding to neighbouring cells.

Conclusions: Through demonstrating its ability to test and generate hypotheses, Cell4D represents an effective tool to help integrate knowledge across diverse, large and small-scale datasets.

Keywords: Computational modeling; Meso-scale; Simulations; Temporal–spatial models; Visualization.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Conceptual overview of Cell4D. A Screenshot of the Cell4D graphical interface. For further details on the interface see the project GitHub: https://github.com/ParkinsonLab/cell4d. B Model design interface. A web interface for creating and editing Cell4D model files. Custom XML model files can be loaded in by a user, or a preset example model can be selected. Once a model is loaded in, parameters of the model will automatically fill the text boxes in the interface. Users can edit the model by modifying the text in each textbox; real-time error messages will appear to prevent invalid inputs from being added. When the “Save” button on the bottom is pressed, if no textboxes have invalid inputs, the information in the text boxes of the current active tab will be saved to the loaded model. Users can switch between tabs that contain different model information such as modifying compartment spaces, molecular species, and reactions. Once all changes are saved on each tab, the user can click “Save model” on the top banner to download the loaded model onto their local device. C Simulation set up and flow time cycle logic. Parameters that describe system behavior such as how molecules behave within the simulation space as well as the way they interact with other molecules are described in an XML input file, which is then used to initialize the simulation space. The simulation then cycles through a series of steps until the end condition is met. Output occurs in two forms: tab-delimited files (.tsv) of molecule counts at each time step, and particle logs recording the position and state information of molecules in the simulation. D Diffusion of bulk molecules shown for a single voxel. At each time step, a portion of the bulk molecules for each c-voxel will diffuse into a neighboring c-voxel, based on the current concentration and the molecule’s diffusion rate constant. This is calculated for all c-voxels at every timestep. (i) an initial setup where a c-voxel contains 100 molecules (orange) with three adjacent voxels that contain 5 molecules each (blue). Diffusion of molecules into grey voxels are disabled in this example. (ii) bulk molecule diffusion calculation is done for each voxel which depends on the system timestep length setting and the concentration of molecules in each voxel. (iii) image shows the concentration of molecules in each voxel after one timestep. E Implementation of off-lattice movement of point particles. (i) and (ii) show the diffusion path of the reactant (blue) after 1 µs for 0.2 µs and 1 µs timestep systems respectively. In (i), using 0.2 µs timesteps, the particle was able to enter the reaction radius of the first reactant (red) which would allow a reaction to occur. In (ii), using 1 µs timesteps, although the final diffusion path of the particle remains the same, there is no step where the second reactant has an opportunity to react with the first reactant.To avoid such cases Cell4D implements the Andrews-Bray-adjustment to artificially increase reaction radii of molecules according to the size of the time step (iii and iv) [26]
Fig. 2
Fig. 2
Activation of calmodulin in Ca2+ microdomains. A Schematic representation of simulations of calmodulin activation involving five compartments (Q1–Q5). End view shows the spatial orientation of the center, original and split types (indicated by red, green, and blue squares respectively) of microdomain-layout used to release Ca2+ into the system. Calcium is removed at the Q5 region to maintain the expected overall Ca2+ concentration of approximately 1–2 µM. The top and bottom of the environment represent hard boundaries that constrain Ca2+ diffusion. B Effective average concentration (mol/L) of Ca2+ in each of the compartments over the course of the simulations. C Average saturation of two states of calmodulin (CaM_2 and CaM_4) in each compartment over the course of the simulations
Fig. 3
Fig. 3
Modeling CEACAM1 signaling. A Schematic of reactions used in the model. CEACAM1 dimers are transported between the membrane and cytosol compartments. Within the membrane, CEACAM1 dimers disassociate to monomers based on interactions with activated calmodulin. Src-family kinases (Lck) phosphorylate the ITIM regions of the CEACAM1 cytoplasmic tail, preventing its transport back to the cytosol and shifting the equilibrium of CEACAM1 localization to the membrane. The membrane region can be defined into lipid-ordered (lipid rafts) and lipid-disordered regions. CEACAM1 preferentially associates with these regions based on its oligomeric state, as shown by the solid and dashed orange arrows between the two membrane regions that indicate transport. The end state of the activated T cell consists of the clustering of CEACAM1 monomers within lipid rafts. B Representation of a 2D membrane compartment with lipid raft sub-compartments. CEACAM1 dimers (green) are generally localized outside of lipid raft regions (indicated in dark yellow), while CEACAM1 monomers (blue) preferentially localize within lipid raft compartments. C The lipid raft CEACAM1 model was tested using 0, 10, 20 molecules of Lck and 0, 2, 5, 10, 20 molecules of active calmodulin to examine the effects of both proteins on CEACAM1 surface expression. Error bars represent standard deviation for 6 replicates. Results show that CEACAM1 surface concentration is dependent on the concentration of activated calmodulin, but not Lck. D Impact of trans-binding rate constants (i.e. unbound monomer to trans-bound (immobilized) monomer) on CEACAM clustering. Simulations were performed for both the lipid raft model (left) and the no-raft model (right). The binding rate constant shows a positive correlation with the total count of trans-bound (clustered) CEACAM1 monomers. For low binding constant conditions in the presence of Lck and CaM in the no-raft model, there appears to be a threshold effect where the rate of cluster formation is slow in the beginning of the simulation, but accelerates after a certain point. For the no-raft Lck-absent models, low calmodulin levels did not lead to a significant clustered CEACAM1 population, while high calmodulin only produced an increased surface CEACAM1 concentration at the highest binding rate constant
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References

    1. Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C. Genome-scale metabolic modeling enables in-depth understanding of big data. Metabolites. 2021;12(1):14. doi: 10.3390/metabo12010014. - DOI - PMC - PubMed
    1. Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber ME, et al. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res. 2021;49(D1):D575–d588. doi: 10.1093/nar/gkaa746. - DOI - PMC - PubMed
    1. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(Database issue):D991–995. - PMC - PubMed
    1. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. doi: 10.1126/science.1260419. - DOI - PubMed
    1. Karlsson M, Zhang C, Méar L, Zhong W, Digre A, Katona B, Sjöstedt E, Butler L, Odeberg J, Dusart P, et al. A single-cell type transcriptomics map of human tissues. Sci Adv. 2021;7(31):eabh2169. doi: 10.1126/sciadv.abh2169. - DOI - PMC - PubMed

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