Award details

Development of methods to assess technical and biological variation and system artifacts in 2D PAGE and non-gel based quantitative proteomics

Reference	BB/C506964/1
Principal Investigator / Supervisor	Professor Kathryn Lilley
Co-Investigators / Co-Supervisors	Professor Julian Griffin, Dr Natasha Karp, Dr Martin Welch
Institution	University of Cambridge
Department	Biochemistry
Funding type	Research
Value (£)	365,433
Status	Completed
Type	Research Grant
Start date	01/03/2005
End date	31/01/2009
Duration	47 months

Abstract

Quantitative proteomics investigates cellular mechanisms at a molecular level by measuring the relative differences in protein expression under different experimental conditions. Measurement of such changes in protein expression is fundamental to many branches of biology, including mechanisms of disease, drug discovery, and agriculture. In order to achieve useful data sets in such studies, it is necessary to carry out comparisons across large numbers of biological samples. All comparative experiments are subject to many degrees of variation such as technical experiment stochasticity and biological variation (genetic, growth conditions). Although this has been elegantly detailed at the mRNA level, the proteomics field lags behind in the assessment of variation with quantative methodology. The challenge for researchers is to be aware of the degree of variation within and across experiments, in order that experiments can be adequately designed allowing conclusions to be accurately drawn. In this proposal we plan to establish a universal set of recommendations facilitating the design of quantitative proteomics experiments. We plan to base our recommendations around five different technological approaches to acquiring quantitative data, 2D gel based and three using mass-spectrometric analysis. We envisage that this resource will be used by the proteomics community at large as a blueprint to work from, such that they are aware of the following: the number of replicate experiments they should plan and thus the amount of material they require; where to focus efforts to reduce experimental variation; have knowledge of the false positive rates and significance thresholds for a given technique; robust methods of multivariat statistical analysis for such data sets.

Summary

unavailable

Committee	Closed Committee - Engineering & Biological Systems (EBS)
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	X - not in an Initiative
Funding Scheme	X – not Funded via a specific Funding Scheme

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