Name
Abstract | ||
|---|---|---|
Metabolic flux analysis with measurement data from 13C tracer experiments has been an important approach for exploring metabolic networks. Though various methods were developed for 13C positional enrichment or isotopomer modelling, few researchers have investigated flux estimation problem in detail. In this paper, flux estimation is formulated as a global optimization problem by carbon enrichment balances. Differential evolution, which is a simple and robust evolutionary algorithm, is applied to flux estimation. The algorithm performances are illustrated and compared with ordinary least squares estimation through simulation of the cyclic pentose phosphate metabolic network in a noisy environment. It is shown that differential evolution is an efficient approach for flux quantification. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/978-3-540-32003-6_12 | EvoWorkshops |
Keywords | Field | DocType |
metabolic network,cyclic pentose phosphate metabolic,metabolic flux analysis,carbon enrichment balance,squares estimation,flux estimation,flux quantification,algorithm performance,flux estimation problem,differential evolution,pentose phosphate,ordinary least square,evolutionary algorithm,global optimization | Least squares,Mathematical optimization,Evolutionary algorithm,Biological system,Computer science,Metabolic flux analysis,Mean squared error,Metabolic network,Differential evolution,Flux,Genetic algorithm,Distributed computing | Conference |
Volume | ISSN | ISBN |
3449 | 0302-9743 | 3-540-25396-3 |
Citations | PageRank | References |
3 | 0.71 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Yang | 1 | 3 | 0.71 |
| Sarawan Wongsa | 2 | 11 | 2.09 |
| Visakan Kadirkamanathan | 3 | 431 | 62.00 |
| Steve A Billings | 4 | 432 | 31.41 |
| Phillip C Wright | 5 | 36 | 4.61 |