Name
Abstract | ||
|---|---|---|
The budding yeast cell cycle provides an excellent example of the need for modeling stochastic effects in mathematical modeling of biochemical reactions. The continuous deterministic approach using ordinary differential equations is adequate for understanding the average behavior of cells, while the discrete stochastic approach accurately captures noisy events in the growth-division cycle. This paper presents a stochastic approximation of the cell cycle for budding yeast using Gillespie's stochastic simulation algorithm. To compare the stochastic results with the average behavior, the simulation must be run thousands of times. A load balancing algorithm reduces the cost for making those runs by 14% when run on a parallel supercomputer. |
Year | Venue | Keywords |
|---|---|---|
2009 | SpringSim | stochastic result,budding yeast cell cycle,stochastic approximation,stochastic cell cycle modeling,continuous deterministic approach,stochastic effect,stochastic simulation algorithm,growth-division cycle,cell cycle,discrete stochastic approach,average behavior,parallel computing,mathematical model,load balance,parallel computer,load balancing |
Field | DocType | Citations |
Stochastic simulation,Mathematical optimization,Stochastic optimization,Ordinary differential equation,Supercomputer,Computer science,Load balancing (computing),Tau-leaping,Deterministic system (philosophy),Stochastic approximation | Conference | 2 |
PageRank | References | Authors |
0.51 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae-Hyuk Ahn | 1 | 19 | 4.47 |
| Pengyuan Wang | 2 | 24 | 1.61 |
| Layne T. Watson | 3 | 1253 | 290.45 |
| Yang Cao | 4 | 8 | 3.88 |
| Clifford A. Shaffer | 5 | 999 | 131.98 |
| William T Baumann | 6 | 26 | 4.87 |