| Title |
Estimation of dynamic flux profiles from metabolic time series data
|
|---|---|
| Published in |
BMC Systems Biology, July 2012
|
| DOI | 10.1186/1752-0509-6-84 |
| Pubmed ID | |
| Authors |
I-Chun Chou, Eberhard O Voit |
| Abstract |
Advances in modern high-throughput techniques of molecular biology have enabled top-down approaches for the estimation of parameter values in metabolic systems, based on time series data. Special among them is the recent method of dynamic flux estimation (DFE), which uses such data not only for parameter estimation but also for the identification of functional forms of the processes governing a metabolic system. DFE furthermore provides diagnostic tools for the evaluation of model validity and of the quality of a model fit beyond residual errors. Unfortunately, DFE works only when the data are more or less complete and the system contains as many independent fluxes as metabolites. These drawbacks may be ameliorated with other types of estimation and information. However, such supplementations incur their own limitations. In particular, assumptions must be made regarding the functional forms of some processes and detailed kinetic information must be available, in addition to the time series data. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Canada | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 2% |
| France | 1 | 2% |
| Singapore | 1 | 2% |
| Unknown | 59 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 37% |
| Researcher | 15 | 24% |
| Student > Master | 5 | 8% |
| Student > Postgraduate | 4 | 6% |
| Professor > Associate Professor | 3 | 5% |
| Other | 7 | 11% |
| Unknown | 5 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 47% |
| Engineering | 6 | 10% |
| Computer Science | 5 | 8% |
| Chemical Engineering | 3 | 5% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Other | 9 | 15% |
| Unknown | 7 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 16 July 2012.
All research outputs
#16,720,137
of 25,371,288 outputs
Outputs from BMC Systems Biology
#613
of 1,132 outputs
Outputs of similar age
#114,116
of 177,906 outputs
Outputs of similar age from BMC Systems Biology
#30
of 45 outputs
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