• Statistical inference methods...

Treffer: Statistical inference methods for sparse biological time series data.

Title:
Statistical inference methods for sparse biological time series data.
Authors:
Ndukum J; Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA., Fonseca LL, Santos H, Voit EO, Datta S
Source:
BMC systems biology [BMC Syst Biol] 2011 Apr 25; Vol. 5, pp. 57. Date of Electronic Publication: 2011 Apr 25.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 101301827 Publication Model: Electronic Cited Medium: Internet ISSN: 1752-0509 (Electronic) Linking ISSN: 17520509 NLM ISO Abbreviation: BMC Syst Biol Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : BioMed Central
MeSH Terms:
Magnetic Resonance Spectroscopy/*methods , Saccharomyces cerevisiae/*metabolism, Algorithms ; Carbon Isotopes/chemistry ; Computational Biology/methods ; Gene Expression Regulation, Fungal ; Glucose/metabolism ; Models, Biological ; Models, Statistical ; Models, Theoretical ; Regression Analysis ; Saccharomyces cerevisiae/genetics ; Systems Biology ; Time Factors
References:
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Grant Information:
1P30ES014443 United States ES NIEHS NIH HHS; CA133844 United States CA NCI NIH HHS
Substance Nomenclature:
0 (Carbon Isotopes)
IY9XDZ35W2 (Glucose)
Entry Date(s):
Date Created: 20110427 Date Completed: 20111003 Latest Revision: 20211020
Update Code:
20250114
PubMed Central ID:
PMC3114728
DOI:
10.1186/1752-0509-5-57
PMID:
21518445
Database:
MEDLINE

Weitere Informationen

Background: Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles.
Results: The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001).
Conclusion: We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.