TransMiner: Biotransformation PredictionMinesh Upadhyaya1, Imran Shah2, Daniel McShan, Weiming Zhang
firstname.lastname@example.org, UCHSC; email@example.com, UCHSC
TransMiner: Biotransformation Prediction
One of the main challenges to understanding the pathways in a living
system is identifying new metabolic capabilities. We are developing
TransMiner, a symbolic computational approach for inferring novel
Data on thousands of enzyme-catalyzed transformations and associated
metabolites is available in BioCyc and KEGG. This information is
invaluable for learning about the fundamental nature of metabolism.
We abstract biotransformations expressively in terms of mappings
between the chemical graphs of metabolites. We have developed a novel
sub-graph isomorphism-based algorithm to search the detailed
representations of known biotransformations to induce biocatalytic
"rules". These biocatalytic rules represent the symbolic
functions of enzymes. We can also apply these rules to infer
novel possible biotransformations that can be catalyzed by an enzyme.
Our biochemical inference algorithm and chemical knowledge are
developed in Lisp, which is a flexible symbolic computation
This poster presents and overview of our approach and summarizes our
results for a large number of biocatalytic functions.