Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

Drawert, Brian; Hellander, Andreas; Bales, Benjamin B.; Banerjee, Debjani; Bellesia, Giovanni; Daigle, Bernie J.; Douglas, Geoffrey; Gu, M.; Gupta, Anand; Hellander, Stefan; Horuk, Christopher B.; Nath, Dibyendu; Takkar, Aviral; Wu, Sheng; Lötstedt, Per; Krintz, Chandra; Petzold, Linda R.

doi:10.1371/journal.pcbi.1005220

Cited by 71 publications

(64 citation statements)

References 46 publications

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“…Within lattice approaches, variation exists depending on how each molecule is represented and reaction is modeled. In the well-established reaction-diffusion master equation (RDME) models [27][28][29][30][31][32][33], space is discretized into lattice voxels called subvolumes. In each subvolume, point-like molecules are assumed to be dilute and well-mixed.…”

Section: Introductionmentioning

confidence: 99%

Reaction-diffusion kinetics on lattice at the microscopic scale

et al. 2018

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Lattice-based stochastic simulators are commonly used to study biological reaction-diffusion processes. Some of these schemes that are based on the reaction-diffusion master equation (RDME), can simulate for extended spatial and temporal scales but cannot directly account for the microscopic effects in the cell such as volume exclusion and diffusion-influenced reactions. Nonetheless, schemes based on the high-resolution microscopic lattice method (MLM) can directly simulate these effects by representing each finite-sized molecule explicitly as a random walker on fine lattice voxels. The theory and consistency of MLM in simulating diffusion-influenced reactions have not been clarified in detail. Here, we examine MLM in solving diffusion-influenced reactions in 3D space by employing the Spatiocyte simulation scheme. Applying the random walk theory, we construct the general theoretical framework underlying the method and obtain analytical expressions for the total rebinding probability and the effective reaction rate. By matching Collins-Kimball and lattice-based rate constants, we obtained the exact expressions to determine the reaction acceptance probability and voxel size. We found that the size of voxel should be about 2% larger than the molecule. The theoretical framework of MLM is validated by numerical simulations, showing good agreement with the off-lattice particle-based method, eGFRD. MLM run time is more than an order of magnitude faster than eGFRD when diffusing macromolecules with typical concentrations observed in the cell. MLM also showed good agreements with eGFRD and mean-field models in case studies of two basic motifs of intracellular signaling, the protein production-degradation process and the dual phosphorylation-dephosphorylation cycle. In addition, when a reaction compartment is populated with volume-excluding obstacles, MLM captures the non-classical reaction kinetics caused by anomalous diffusion of reacting molecules.

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Section: Introductionmentioning

confidence: 99%

Reaction-diffusion kinetics on lattice at the microscopic scale

et al. 2018

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“…For precise predictions, we suggest to use our method as a starting point for refinement by means of numerically demanding, but asymptotically exact methods. Although there is a lot of development making stochastic analysis tools with state of the art algorithms and fast implementations ( Drawert et al , 2016 ; Fan et al , 2016 ; Somogyi et al , 2015 ) available for a broad community the optimization of stochastic biochemical systems is hardly performed due to its challenging computational effort ( Poovathingal and Gunawan, 2010 ). With this study, we demonstrated that the proposed method is capable of describing biochemical systems containing intrinsic and extrinsic noise and it represents a promising tool suited for optimization and analysis of distributed, stochastic biochemical reaction systems.…”

Section: Discussionmentioning

confidence: 99%

Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems

2017

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MotivationBiological cells operate in a noisy regime influenced by intrinsic, extrinsic and external noise, which leads to large differences of individual cell states. Stochastic effects must be taken into account to characterize biochemical kinetics accurately. Since the exact solution of the chemical master equation, which governs the underlying stochastic process, cannot be derived for most biochemical systems, approximate methods are used to obtain a solution.ResultsIn this study, a method to efficiently simulate the various sources of noise simultaneously is proposed and benchmarked on several examples. The method relies on the combination of the sigma point approach to describe extrinsic and external variability and the τ-leaping algorithm to account for the stochasticity due to probabilistic reactions. The comparison of our method to extensive Monte Carlo calculations demonstrates an immense computational advantage while losing an acceptable amount of accuracy. Additionally, the application to parameter optimization problems in stochastic biochemical reaction networks is shown, which is rarely applied due to its huge computational burden. To give further insight, a MATLAB script is provided including the proposed method applied to a simple toy example of gene expression.Availability and implementationMATLAB code is available at Bioinformatics online.Supplementary information Supplementary data are available at Bioinformatics online.

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“…Many other research software systems make use of notebooks, and some incorporate special extensions. StochSS [ 87 ], the GenePattern Notebook [ 88 ], the SAGE math system [ 89 ], and the commercial Mathematica software [ 30 ] all utilize notebooks which are specially tailored or feature special extensions for each respective application. However, none of these approaches attempt to solve the problem we address: workflow integration with exchangeable standards.…”

Section: Discussionmentioning

confidence: 99%

Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology

et al. 2018

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The considerable difficulty encountered in reproducing the results of published dynamical models limits validation, exploration and reuse of this increasingly large biomedical research resource. To address this problem, we have developed Tellurium Notebook, a software system for model authoring, simulation, and teaching that facilitates building reproducible dynamical models and reusing models by 1) providing a notebook environment which allows models, Python code, and narrative to be intermixed, 2) supporting the COMBINE archive format during model development for capturing model information in an exchangeable format and 3) enabling users to easily simulate and edit public COMBINE-compliant models from public repositories to facilitate studying model dynamics, variants and test cases. Tellurium Notebook, a Python–based Jupyter–like environment, is designed to seamlessly inter-operate with these community standards by automating conversion between COMBINE standards formulations and corresponding in–line, human–readable representations. Thus, Tellurium brings to systems biology the strategy used by other literate notebook systems such as Mathematica. These capabilities allow users to edit every aspect of the standards–compliant models and simulations, run the simulations in–line, and re–export to standard formats. We provide several use cases illustrating the advantages of our approach and how it allows development and reuse of models without requiring technical knowledge of standards. Adoption of Tellurium should accelerate model development, reproducibility and reuse.

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Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

Cited by 71 publications

References 46 publications

Reaction-diffusion kinetics on lattice at the microscopic scale

Reaction-diffusion kinetics on lattice at the microscopic scale

Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems

Tellurium notebooks—An environment for reproducible dynamical modeling in systems biology

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