Our research aims were motivated by a desire to understand the complexity of living self-organized systems. We focused on microtubule-based systems in their entirety, and thus were led to mass spectrometry-based proteomics as our measurement approach of choice. We made multiple improvements to various aspects of this measurement pipeline. We developed a new quantitative proteomics measurement approach that has significantly better signal to noise (median of >100) than the previous state-of-the-art (~30). We also developed a novel computational approach for imputing missing values in large quantitative proteomics datasets. Our approach relies on coupling underlying biological covariation between samples with regularized regression. We also investigated the properties of matching acquired mass spectra to peptide sequences with database searches that varied widely in the size of the precursor mass error used. We found that using search spaces that are ~250 times larger than what is typically used confer multiple advantages and can increase the potential number of sequence matches by 20 to 35% in various datasets. We used advances in proteomics methodology from ourselves and others to do some biology. We used quantitative multiplexed proteomics to obtain a systems biochemistry view of microtubule based structures in cell free extracts from the model system X. laevis. By combining classical biochemical binding assays with modern mass spectrometry we were able to measure partition coefficients for microtubules and chromatin for thousands of proteins. In some cases, exchange rates and salt sensitivities or other proxies for affinity were also measured. We developed and used a rapid filtration approach to isolate metaphase spindles directly from X. laevis extracts faithfully and measure their protein composition.
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