{"title":"归一化和误差模型选择对生化反应最大似然估计量分布的影响","authors":"Caterina Thomaseth, Nicole E. Radde","doi":"10.1049/syb2.12055","DOIUrl":null,"url":null,"abstract":"<p>Sparse and noisy measurements make parameter estimation for biochemical reaction networks difficult and might lead to ill-posed optimisation problems. This is potentiated if the data has to be normalised, and only fold changes rather than absolute amounts are available. Here, the authors consider the propagation of measurement noise to the distribution of the maximum likelihood (ML) estimator in an in silico study. Therefore, a model of a reversible reaction is considered, for which reaction rate constants using fold changes is estimated. Noise propagation is analysed for different normalisation strategies and different error models. In particular, accuracy, precision, and asymptotic properties of the ML estimator is investigated. Results show that normalisation by the mean of a time series outperforms normalisation by a single time point in the example provided by the authors. Moreover, the error model with a heavy-tail distribution is slightly more robust to large measurement noise, but, beyond this, the choice of the error model did not have a significant impact on the estimation results provided by the authors.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 1","pages":"1-13"},"PeriodicalIF":1.9000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/92/SYB2-17-1.PMC9931059.pdf","citationCount":"0","resultStr":"{\"title\":\"The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction\",\"authors\":\"Caterina Thomaseth, Nicole E. Radde\",\"doi\":\"10.1049/syb2.12055\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Sparse and noisy measurements make parameter estimation for biochemical reaction networks difficult and might lead to ill-posed optimisation problems. This is potentiated if the data has to be normalised, and only fold changes rather than absolute amounts are available. Here, the authors consider the propagation of measurement noise to the distribution of the maximum likelihood (ML) estimator in an in silico study. Therefore, a model of a reversible reaction is considered, for which reaction rate constants using fold changes is estimated. Noise propagation is analysed for different normalisation strategies and different error models. In particular, accuracy, precision, and asymptotic properties of the ML estimator is investigated. Results show that normalisation by the mean of a time series outperforms normalisation by a single time point in the example provided by the authors. Moreover, the error model with a heavy-tail distribution is slightly more robust to large measurement noise, but, beyond this, the choice of the error model did not have a significant impact on the estimation results provided by the authors.</p>\",\"PeriodicalId\":50379,\"journal\":{\"name\":\"IET Systems Biology\",\"volume\":\"17 1\",\"pages\":\"1-13\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/92/SYB2-17-1.PMC9931059.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Systems Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/syb2.12055\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Systems Biology","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/syb2.12055","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction
Sparse and noisy measurements make parameter estimation for biochemical reaction networks difficult and might lead to ill-posed optimisation problems. This is potentiated if the data has to be normalised, and only fold changes rather than absolute amounts are available. Here, the authors consider the propagation of measurement noise to the distribution of the maximum likelihood (ML) estimator in an in silico study. Therefore, a model of a reversible reaction is considered, for which reaction rate constants using fold changes is estimated. Noise propagation is analysed for different normalisation strategies and different error models. In particular, accuracy, precision, and asymptotic properties of the ML estimator is investigated. Results show that normalisation by the mean of a time series outperforms normalisation by a single time point in the example provided by the authors. Moreover, the error model with a heavy-tail distribution is slightly more robust to large measurement noise, but, beyond this, the choice of the error model did not have a significant impact on the estimation results provided by the authors.
期刊介绍:
IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells.
The scope includes the following topics:
Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.