Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.
Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-12-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5154471?pdf=render |
| _version_ | 1811197578939727872 |
|---|---|
| author | James C Schaff Fei Gao Ye Li Igor L Novak Boris M Slepchenko |
| author_facet | James C Schaff Fei Gao Ye Li Igor L Novak Boris M Slepchenko |
| author_sort | James C Schaff |
| collection | DOAJ |
| description | Hybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell. |
| first_indexed | 2024-04-12T01:17:16Z |
| format | Article |
| id | doaj.art-17fc54b60bec4fea80d043a395347d1d |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-12T01:17:16Z |
| publishDate | 2016-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-17fc54b60bec4fea80d043a395347d1d2022-12-22T03:53:55ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-12-011212e100523610.1371/journal.pcbi.1005236Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.James C SchaffFei GaoYe LiIgor L NovakBoris M SlepchenkoHybrid deterministic-stochastic methods provide an efficient alternative to a fully stochastic treatment of models which include components with disparate levels of stochasticity. However, general-purpose hybrid solvers for spatially resolved simulations of reaction-diffusion systems are not widely available. Here we describe fundamentals of a general-purpose spatial hybrid method. The method generates realizations of a spatially inhomogeneous hybrid system by appropriately integrating capabilities of a deterministic partial differential equation solver with a popular particle-based stochastic simulator, Smoldyn. Rigorous validation of the algorithm is detailed, using a simple model of calcium 'sparks' as a testbed. The solver is then applied to a deterministic-stochastic model of spontaneous emergence of cell polarity. The approach is general enough to be implemented within biologist-friendly software frameworks such as Virtual Cell.http://europepmc.org/articles/PMC5154471?pdf=render |
| spellingShingle | James C Schaff Fei Gao Ye Li Igor L Novak Boris M Slepchenko Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology. PLoS Computational Biology |
| title | Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology. |
| title_full | Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology. |
| title_fullStr | Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology. |
| title_full_unstemmed | Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology. |
| title_short | Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology. |
| title_sort | numerical approach to spatial deterministic stochastic models arising in cell biology |
| url | http://europepmc.org/articles/PMC5154471?pdf=render |
| work_keys_str_mv | AT jamescschaff numericalapproachtospatialdeterministicstochasticmodelsarisingincellbiology AT feigao numericalapproachtospatialdeterministicstochasticmodelsarisingincellbiology AT yeli numericalapproachtospatialdeterministicstochasticmodelsarisingincellbiology AT igorlnovak numericalapproachtospatialdeterministicstochasticmodelsarisingincellbiology AT borismslepchenko numericalapproachtospatialdeterministicstochasticmodelsarisingincellbiology |