Browsing by Author "Kim, Sarah Michelle"
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Item Improving the interpretation of metabolic pathfinding results with clustering and compound hubs(2016-04-27) Kim, Sarah Michelle; Kavraki, Lydia EKnowledge on metabolic networks across species can be utilized to help address many challenges in biotechnology, including metabolic engineering. Large-scale annotated metabolic databases, such as KEGG and MetaCyc, provide a wealth of information to researchers designing novel biosynthetic pathways. However, many metabolic pathfinding tools that assist in identifying possible solution pathways fail to facilitate the interpretation of these pathway results. This work begins to address this problem by examining the performance of standard clustering algorithms on results produced by a popular metabolic pathfinding algorithm and suggesting the use of compound ”hubs” for examining the produced results. To address the first point, we assessed the ability of standard clustering method to expertly group pathways. Three standard clustering methods (hierarchical, k-means, and k-medoids) along with three pair-wise distance measures (Levenshtein, Jaccard, and n-gram) were used to group lysine, isoleucine, and 3-hydroxypropanoic acid (3-HP) biosynthesis pathways produced by a recent metabolic finding algorithm. The quality of the resulting clusters were quantitatively evaluated against expected pathway groupings taken from theliterature. Hierarchical clustering and Levenshtein distance appeared to best match external pathway labels across the three biosynthesis pathways but results suggest that grouping pathways with more complex underlying topologies may require more tailored clustering methods. In summary, the clustering of pathways proved much more nuanced than excepted due to the various intricacies of computed paths and several ways of getting between two compounds conserving the same number of atoms. To address the second point, we investigate the use of “hub” compounds. Hub compounds were selected by metabolic experts among compounds with a large number of in-degree reactions. An analysis of our results shows that hub compounds are common in the pathfinding results but that themselves alone cannot be used to cluster pathways. Our observations give rise to a new proposed method that will compute pathways between input and output compounds by using a precomputed a lookup table for pathways between the most well connected compound hubs in the metabolic network. The ultimate goal of precomputing the lookup table is to reduce search space while still obtaining most, if not all, pathway results found by the original search algorithm. We provide evidence that this is a promising direction for future research and can yield results that are more easily interpreted and refined by users.Item Precomputation and Visualization of Metabolic Pathways(2019-03-06) Kim, Sarah Michelle; Kavraki, Lydia E.; Bennett, George N.Advances in metabolic engineering have led to the development of alternative, renewable methods for producing chemicals that are traditionally challenging to obtain. The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of chemicals that can be produced with metabolic engineering. However, manually searching through the tens of thousands of possible enzymatic reactions for promising metabolic pathways has become increasingly difficult. Over the past two decades, several computational search algorithms have been developed for automating the identification of novel metabolic pathways. Even so, these searches may return thousands of pathway results presented in a way that is tedious to sift through. Although there are a large number of possible compounds and reactions to include in metabolic pathways, a smaller subset of core reaction “modules” may be repeatedly incorporated into pathways across multiple searches. To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway Search with Atom Tracking (HPAT), was developed as a first step to take advantage of a precomputed network of subpath modules. The result pathways are visualized as a single interactive graph, allowing the users to filter pathways based on a collection of pathway features. The modularity of pathways is also exploited in visualization to organize pathways in a more concise way. A test set of nineteen known pathways taken from literature and metabolic databases was used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating the utility of these filters in reducing the amount of displayed information. This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and providing a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.