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MC-Fit: using Monte-Carlo methods to get accurate confidence limits on enzyme parameters
- Source :
- Computer applications in the biosciences : CABIOS. 10(3)
- Publication Year :
- 1994
-
Abstract
- A program is described for estimating enzymatic parameters from experimental data using Apple Macintosh computers. MC-Fit uses iterative least-square fitting and Monte-Carlo sampling to get accurate estimates of the confidence limits. This approach is more robust than the conventional covariance matrix estimation, especially in cases where experimental data is partially lacking or when the standard error on individual measurements is large. This happens quite often when analysing the properties of variant enzymes obtained by mutagenesis, as these can have severely impaired activities and reduced affinities for their substrates.
- Subjects :
- Statistics and Probability
Computer science
Monte Carlo method
Methionine-tRNA Ligase
Biochemistry
User-Computer Interface
Microcomputers
Statistics
Confidence Intervals
Escherichia coli
Least-Squares Analysis
Molecular Biology
Mathematical Computing
Covariance matrix
Experimental data
Sampling (statistics)
Confidence interval
Computer Science Applications
Enzymes
Computational Mathematics
Kinetics
Standard error
Computational Theory and Mathematics
Mutagenesis
Algorithm
Monte Carlo Method
Software
Subjects
Details
- ISSN :
- 02667061
- Volume :
- 10
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Computer applications in the biosciences : CABIOS
- Accession number :
- edsair.doi.dedup.....03b285fb6afd70a69133f6bd5df004d0