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Adina's Faculty Introduction - ISU ABE

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The presentation resulting from my chair asking me to introduce myself to faculty in ISU ABE.

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Adina's Faculty Introduction - ISU ABE

  1. 1. Adina Howe, ABE Faculty Retreat, January 8, 2015
  2. 2. Measurements of health and productivity Biological sequencing, chemical characterization, yield / growth / weight, climate data, structured & unstructured Unifying heterogeneous datasets Moving beyond the Big Data craze:
  3. 3. Background Purdue University, BSME, Mechanical Engineering Purdue University, MS, Environmental Engineering (Sustainability) University of Iowa, PhD, Environmental Engineering (Microbiology/Bioremediatio n) Michigan State University NSF Postdoc Math and Biology Fellow (cross- training) Computational Biologist Microbiology / Microbial Ecology
  4. 4. GERMS Lab (Genomics & Environmental Research in Microbial Systems) Jin Choi, PhD, University of Tennessee, ChemE Ryan Williams, PhD, Iowa State, Ecology Evolution Dan Shea, MS, Northeastern, Bioinforma Website: germslab.org
  5. 5. GERMS Mission  We are changing the environment that we live in.  To preserve our environmental integrity, we must understand and manage the impacts of global change.  Scientific research must inform our decisions and policy.  Therefore, we use innovative scientific methods to evaluate and understand our complex
  6. 6. Towards this Mission: Microbes as lens into understanding global change in the natural world MICROBES IN ECOSYSTEMS NATURE AIR WATER SOIL MICROBIOMES HUMANS/ANIMAL ENGINEERED BIOREACTORS WASTEWATER
  7. 7. GERMS Vision (5 year goals)  To provide scalable, quantitative tools to monitor microbial responses in complex environments  To identify the microbial drivers responding to global change in complex environments (e.g., soils, waters, gut)  To predict and model the impacts of microbial responses on ecosystem health and servicesTo monitor, evaluate, and manage our microbial partners and their services.
  8. 8. WATER project: Improved methods to evaluating water quality
  9. 9. Scalable, quantitative tools to monitor microbial responses in complex environments Data Type Example Cost per sample / Frequency of sampling Precision / Water quality information Challenges Water properties chemical analysis of water quality narrow range of information about services in ecosystem Traditional integrity indicators presence of coliform bacteria detection methods lack specificitity and are often imprecise Phytoplankton community characterization cyanotoxin detection through fractionation of ammonia detection of toxicity may not reveal source Microbial community characterization (16S rRNA) abundance of genes present and assoiated with all cyanobacteria characterization of microbial community structure may not reveal gene function; data volume large for public understanding Proposed MAVeRiC genes (DNA) abundance of genes present associated with specific source of pollution identifying relevant genes of interest to water quality; DNA reveals genes present but not necessarily actively expressed Proposed MAVeRiC genes (RNA) abundance of genes expressed and present associated with specific source pollution identifying relevant genes of interest to water quality
  10. 10. Scalable, quantitative tools to monitor microbial responses in complex environments Estimating risks from pathogens Biotic integrity of a healthy water system Sources of non point pollution Role of waters in stabilizing climate change Microbial genetic biomarkers can capture…
  11. 11. MicroArray Value and Risk Chip (MAVeRIC) $24 for 216 bioindicators/sample, estimates gene abundance of biological signals, quantitative PCR Pollution Pathogens Nutrients Toxicity Biodiversity Pollution biomarkers: Non point pollution source markers Pathogen biomarkers: Specific bacteria or virus genes Nutrient cycling biomarkers: Carbon, nitrogen, phosphorus metabolic genes Toxicity biomarkers Biodiversity biomarkers A B C D Monitoring, Evaluating, Predicting
  12. 12. Scaling: Iowa Lake Waters (John Downing and Chris Filstrup) Integrate measurements of bioindicators with water quality measurements in 132 lakes sampled for a routine EPA-reported, lake water quality assessment program. Interdisciplinary collaboration allowing for evaluation and prediction
  13. 13. SOIL Project: Microbial drivers of carbon cycling
  14. 14. Carbon cycling in agricultural soils (in response to global change) Collaboration with Kirsten Hofmockel, ANL, PNNL
  15. 15. THE DIRT ON SOIL Biodiversity in the dark, Wall et al., Nature Geoscience, 2010 Jeremy Burgress MAGNIFICENT BIODIVERSITY
  16. 16. THE DIRT ON SOIL SPATIAL HETEROGENEITY http://www.fao.org/ www.cnr.uidaho.edu
  17. 17. THE DIRT ON SOIL DYNAMIC
  18. 18. THE DIRT ON SOIL INTERACTIONS: BIOTIC, ABIOTIC, ABOVE, BELOW, S Philippot, 2013, Nature Reviews Microbiology
  19. 19. Strategy of breaking down complexity: Identifying drivers of carbon degradation Labeled Carbon (Cellulose) Monitoring & Evaluating Soil microbial communities Communities assimilating carbon Cutting edge fluorescent cell sorting
  20. 20. GUT Project: Identifying the microbes that make us chubby and sick
  21. 21. How do our microbial partners in our bodies affect our stability and resilience to change? Collaboration with ANL and University of Chicago (Eugene Chang and Daina Rin We have the same genes, but why are you a rounder?
  22. 22. A fascination with viruses Despite its ferocity in humans, Ebola is a life-form of mysterious simplicity. ..If it were the size of a piece of spaghetti, then a human hair would be about twelve feet in diameter and would resemble the trunk of a giant redwood tree. (Michael Specter, New Yorker) 80% unknown
  23. 23. Concluding thoughts  All my projects depend heavily on collaborations  Unifying heterogeneous datasets – improved resolutions, investigating diverse questions  Biological data: Rapid, high resolution, cheap  Effective integrations are POISED FOR IMPACT. Looking forward to the adventure together!

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