A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters
Biolog phenotype microarrays enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summ...
| Main Authors: | , , , , , , |
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| Format: | Article |
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World Scientific
2016
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| Online Access: | https://eprints.nottingham.ac.uk/31379/ |
| _version_ | 1848794188147064832 |
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| author | Gerstgrasser, Matthias Nicholls, Sarah Stout, Michael Smart, Katherine Powell, Chris Kypraios, Theodore Stekel, Dov J. |
| author_facet | Gerstgrasser, Matthias Nicholls, Sarah Stout, Michael Smart, Katherine Powell, Chris Kypraios, Theodore Stekel, Dov J. |
| author_sort | Gerstgrasser, Matthias |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Biolog phenotype microarrays enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo methods to enable high throughput estimation of important information, including length of lag phase, maximal ``growth'' rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models. |
| first_indexed | 2025-11-14T19:12:13Z |
| format | Article |
| id | nottingham-31379 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:13Z |
| publishDate | 2016 |
| publisher | World Scientific |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-313792020-05-04T17:32:15Z https://eprints.nottingham.ac.uk/31379/ A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters Gerstgrasser, Matthias Nicholls, Sarah Stout, Michael Smart, Katherine Powell, Chris Kypraios, Theodore Stekel, Dov J. Biolog phenotype microarrays enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo methods to enable high throughput estimation of important information, including length of lag phase, maximal ``growth'' rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models. World Scientific 2016-01-13 Article PeerReviewed Gerstgrasser, Matthias, Nicholls, Sarah, Stout, Michael, Smart, Katherine, Powell, Chris, Kypraios, Theodore and Stekel, Dov J. (2016) A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. Journal of Bioinformatics and Computational Biology . ISSN 1757-6334 Biolog Growth Model Diauxic Lag Phase Bayesian Statistics Phenotype Microarrays http://www.worldscientific.com/doi/abs/10.1142/S0219720016500074 doi:10.1142/S0219720016500074 doi:10.1142/S0219720016500074 |
| spellingShingle | Biolog Growth Model Diauxic Lag Phase Bayesian Statistics Phenotype Microarrays Gerstgrasser, Matthias Nicholls, Sarah Stout, Michael Smart, Katherine Powell, Chris Kypraios, Theodore Stekel, Dov J. A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| title | A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| title_full | A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| title_fullStr | A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| title_full_unstemmed | A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| title_short | A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| title_sort | bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters |
| topic | Biolog Growth Model Diauxic Lag Phase Bayesian Statistics Phenotype Microarrays |
| url | https://eprints.nottingham.ac.uk/31379/ https://eprints.nottingham.ac.uk/31379/ https://eprints.nottingham.ac.uk/31379/ |