Combinatorial optimization for affinity proteomics

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URI: http://hdl.handle.net/10900/71381
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-713816
http://dx.doi.org/10.15496/publikation-12794
Dokumentart: Dissertation
Date: 2016
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Informatik
Advisor: Zell, Andreas (Prof. Dr.)
Day of Oral Examination: 2016-07-01
DDC Classifikation: 004 - Data processing and computer science
Keywords: Massenspektrometrie , Proteomanalyse , Optimierung , Bioinformatik , Diskrete Optimierung
Other Keywords:
mass spectrometry
proteomics
optimization
bioinformatics
combinatorial optmization
License: Publishing license excluding print on demand
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Abstract:

Biochemical test development can significantly benefit from combinatorial optimization. Multiplex assays do require complex planning decisions during implementation and subsequent validation. Due to the increasing complexity of setups and the limited resources, the need to work efficiently is a key element for the success of biochemical research and test development. The first approached problem was to systemically pool samples in order to create a multi-positive control sample. We could show that pooled samples exhibit a predictable serological profile and by using this prediction a pooled sample with the desired property. For serological assay validation it must be shown that the low, medium, and high levels can be reliably measured. It is shown how to optimally choose a few samples to achieve this requirements. Finally the latter methods were merged to validate multiplexed assays using a set of pooled samples. A novel algorithm combining fast enumeration and a set cover formulation has been introduced. The major part of the thesis deals with optimization and data analysis for Triple X Proteomics - immunoaffinity assays using antibodies binding short linear, terminal epitopes of peptides. It has been shown that the problem of choosing a minimal set of epitopes for TXP setups, which combine mass spectrometry with immunoaffinity enrichment, is equivalent to the well-known set cover problem. TXP Sandwich immunoassays capture and detect peptides by combining the C-terminal and N-terminal binders. A greedy heuristic and a meta-heuristic using local search is presented, which proves to be more efficient than pure ILP formulations. All models were implemented in the novel Java framework SCPSolver, which is applicable to many problems that can be formulated as integer programs. While the main design goal of the software was usability, it also provides a basic modelling language, easy deployment and platform independence. One question arising when analyzing TXP data was: How likely is it to observe multiple peptides sharing the same terminus? The algorithms TXP-TEA and MATERICS were able to identify binding characteristics of TXP antibodies from data obtained in immunoaffinity MS experiments, reducing the cost of such analyses. A multinomial statistical model explains the distributions of short sequences observed in protein databases. This allows deducing the average optimal length of the targeted epitope. Further a closed-from scoring function for epitope enrichment in sequence lists is derived.

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