Stochastic simulation in systems biology
Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2014-11-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037014000403 |
| _version_ | 1818335226946912256 |
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| author | Tamás Székely Jr. Kevin Burrage |
| author_facet | Tamás Székely Jr. Kevin Burrage |
| author_sort | Tamás Székely Jr. |
| collection | DOAJ |
| description | Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity; indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity).
In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology.
There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time; rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest. |
| first_indexed | 2024-12-13T14:20:04Z |
| format | Article |
| id | doaj.art-af55dcad9dab4da4bd10fde0fa148a7c |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-13T14:20:04Z |
| publishDate | 2014-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-af55dcad9dab4da4bd10fde0fa148a7c2022-12-21T23:42:06ZengElsevierComputational and Structural Biotechnology Journal2001-03702014-11-011220142510.1016/j.csbj.2014.10.003Stochastic simulation in systems biologyTamás Székely Jr.0Kevin Burrage1Department of Computer Science, University of Oxford, Oxford, United KingdomDepartment of Computer Science, University of Oxford, Oxford, United KingdomNatural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity; indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity). In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology. There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time; rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest.http://www.sciencedirect.com/science/article/pii/S2001037014000403Stochastic simulationDiscrete-state stochastic methodsHeterogeneity |
| spellingShingle | Tamás Székely Jr. Kevin Burrage Stochastic simulation in systems biology Computational and Structural Biotechnology Journal Stochastic simulation Discrete-state stochastic methods Heterogeneity |
| title | Stochastic simulation in systems biology |
| title_full | Stochastic simulation in systems biology |
| title_fullStr | Stochastic simulation in systems biology |
| title_full_unstemmed | Stochastic simulation in systems biology |
| title_short | Stochastic simulation in systems biology |
| title_sort | stochastic simulation in systems biology |
| topic | Stochastic simulation Discrete-state stochastic methods Heterogeneity |
| url | http://www.sciencedirect.com/science/article/pii/S2001037014000403 |
| work_keys_str_mv | AT tamasszekelyjr stochasticsimulationinsystemsbiology AT kevinburrage stochasticsimulationinsystemsbiology |