| Title |
A Critical Comparison of Rejection-Based Algorithms for Simulation of Large Biochemical Reaction Networks
|
|---|---|
| Published in |
Bulletin of Mathematical Biology, July 2018
|
| DOI | 10.1007/s11538-018-0462-y |
| Pubmed ID | |
| Authors |
Vo Hong Thanh |
| Abstract |
The rejection-based simulation technique has been applying to improve the computational efficiency of the stochastic simulation algorithm (SSA) in simulating large reaction networks, which are required for a thorough understanding of biological systems. We compare two recently proposed simulation methods, namely the composition-rejection algorithm (SSA-CR) and the rejection-based SSA (RSSA), aiming for this purpose. We discuss the right interpretation of the rejection-based technique used in these algorithms in order to make an informed choice when dealing with different aspects of biochemical networks. We provide the theoretical analysis as well as the detailed runtime comparison of these algorithms on concrete biological models. We highlight important factors that are omitted in previous analysis of these algorithms. The numerical comparison shows that for reaction networks where the search cost is expensive then SSA-CR is more efficient, and for reaction networks where the update cost is dominant, often the case in practice, then RSSA should be the choice. |
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| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
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| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
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| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 20% |
| Lecturer | 1 | 20% |
| Other | 1 | 20% |
| Unknown | 2 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Economics, Econometrics and Finance | 1 | 20% |
| Medicine and Dentistry | 1 | 20% |
| Unknown | 3 | 60% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 July 2018.
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#18,641,800
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