Molecular Assemblies, Genes and Genomics Integrated Efficiently (MAGGIE) Page: 2 of 6
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I. Executive Summary
We set ambitious goals to model the functions of individual organisms arid their
community from molecular to systems scale. These scientific goals are driving the
development of sophisticated algorithms to analyze large amounts of experimental
measurements made using high throughput technologies to explain and predict
how the environment influences biological function at multiple scales and row the
microbial systems in turn modify the environment. By expeHmentally evaluating
predictions rmade using these models we will test the degree to which our
quantitative rnuitiscale understanding will help to rationally steer individual
microbes and their communities towards specific tasks.
Towards this end we have made substantial progress towards understanding
evolution of gene families, transcriptional structures, detailed structures of
keystone molecular assemblies (proteins and complexes), protein interactions,
bilogical networks, mkcrobial interactions, and community structure,
Using comparative analysis we have tracked the evolutionary history of gene
functions to understand how novel functions evolve. One level up, we have used
proteomics data, high-resolution genome tiling microarrays, and 5' RNA
sequencing to revise genome annotations. discover new genes including ncRNAs,
and map dynamically changing operon structures of five model organisms: For
Desulfovibho vulgaris Hi-denborough. Pyrococcus F/iosis, Sufoiobus solfataricus,
MeThanococcus manpaludis and Halobacterium salinarum NRC-1. We have
developed machine learning algorithms to accurately identify protein interactions at
a near-zero false positive rate from noisy data generated using tagless complex
purification. TAP purification, and analysis of membrane complexes. Combining
other genorne-scale datasets produced by ENIGMA (in particular, microarray data)
and available from literature we have been able to achieve a true positive rate as
high as 65% at almost zero false positives when applied to the manually curated
training set. Applying this method to the data representing around a quarter of the
fraction space for water soluble proteins in . vuigaris, we obtained 854 reliable
pair wise interactions. Further, we have developed algorithms to analyze and
assign significance to protein interaction data from bait puJl-down experiments and
integrate these data with other systems biology data through associative
biclustering in a parallel computing environment, We will TihI-in missing
information in these interaction data using a 'Transitive Closure" algorithm and
subsequently use "Between Commonality Decompositiorf' algorithm to discover
complexes within these large graphs of protein interactions. To characterize the
metabolic activities of proteins and their complexes we are developing algorithms
to deconvolute pure mass spectra, estimate chemical formula for itz values, and
fit isotopic fine structure to metaboiomics data. We have discovered that in
comparison to isotopic pattern fitting methods restricting the chemical formula by
these two dimensions actually facilitates unique solutions for chemical formula
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Baliga, Nitin S. Molecular Assemblies, Genes and Genomics Integrated Efficiently (MAGGIE), text, May 26, 2011; United States. (digital.library.unt.edu/ark:/67531/metadc846214/m1/2/: accessed October 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.