CAREER: Developing Biochemoinformatics Tools for Large-Scale Metabolomics Applications

Technical Description Research: Recent advances in stable isotope-resolved metabolomics (SIRM) are enabling orders-of-magnitude increase in the number of observable metabolic traits (a metabolic phenotype) for a given organism or community of organisms. Analytical experiments that take only a few minutes to perform can detect stable isotope-labeled variants of thousands of metabolites. Thus, unique metabolic phenotypes may be observable for almost all significant biological states, biological processes, and perturbations. Currently, the major bottleneck is the lack of data analysis that can properly organize and interpret this mountain of phenotypic data as highly insightful biochemical and biological information for a wide range of biological research applications. So, our research goals are to develop bioinformatic, biostatistical, and systems biochemical tools implemented in an integrated data analysis pipeline that will alleviate this limitation, enabling a broad application of SIRM from the discovery of specific metabolic phenotypes representing biological states of interest to a mechanism-based understanding of a wide range of biological processes with particular metabolic phenotypes. The major specific intellectual merits are: •Developing novel methods for detection and identification of metabolites that utilize the unprecedented combined advantages from stable isotope labeling, chemoselective probes, ultra-high resolution/accurate MS, and NMR. Since unidentified metabolites make up the majority of detected features in current metabolomics datasets, identification was singled out as the principal bottleneck at a panel session at the recent Metabolomics 2012 conference. •Developing the first error analyses that allow: i) rigorous quantitative evaluation of detected isotopologue intensities and their errors; ii) evaluation of error propagation through subsequent analyses; and iii) development of quality control measures derived from the detected errors. •Developing new algorithms to deal with the isotopic non-steady state conditions of SIRM experiments, especially deconvolution methods that will aid relative flux interpretation and metabolic flux analysis. •Developing new methods that integrate and cross-validate metabolomics with genomics, transcriptomics, and proteomics via mutually-identified metabolic, gene expression, and signaling pathways. •Automating and parallelizing these methods to keep pace with the rapidly increasing data collection. Education: Simultaneous trends of declining student effort and declining graduation rates in STEM disciplines do not bode well for the successful education of the next generation of scientists. A more expedient approach to improving student outcomes may be to increase the effectiveness of students’ effort. Using a design-based research approach, I have integrated multiple advanced teaching-learning methods into my content-rich college science courses. Statistical analysis of these methods shows large effect sizes for the use of scaffolded explicit revision to improve the effectiveness of student effort and indicates a path for significant refinement of these methods, which will be pursued and implemented. Nontechnical Description The proposed research will create computational tools that analyze and derive unique mechanistic information from extremely large and highly underutilized datasets available from cutting-edge metabolomics technologies which track atomic level changes in the production and utilization of thousands of molecules (metabolites) inside the cells of organisms. These novel computational tools will be tested and refined in the NSF-initiated Center for Regulatory and Environmental Analytical Metabolomics (CREAM), which provides state-of-the-art analytical services and expertise for national and international stable isotope-resolved metabolomics (SIRM) research efforts. Once these computational tools reach production-quality, they will be disseminated to the broader scientific community for a wide variety of scientific applications involving biological processes with changes in cellular metabolism. We will also develop methods that integrate metabolomics datasets with genomics and other omics-level datasets, to allow new systems-level metabolic insights into a wide range of biological processes. During the execution of this proposed research, significant numbers of high school, undergraduate, and graduate students from a wide variety of STEM (Science, Technology, Engineering, & Math) disciplines will be exposed to and trained with multidisciplinary bioinformatics research projects using interdisciplinary approaches to research. This new lab has already included 4 graduate, 25 undergraduate, and 8 high school students, including 8 women, in laboratory research projects. Furthermore, the principle investigator (PI) has developed an integrated set of advanced teaching-learning methods amenable to content-rich college science courses that have statistically-proven significant impact on student effort and outcomes. These advanced teaching-learning methods focus students’ efforts at correcting and learning from prior mistakes on assignments, quizzes, and exam questions via a series of explicit revision steps that span different levels of learning. The PI will refine, present, and publish these methods.