Open Science, Open Data, Open Source Projects for Undergraduate Research Experiences

1. Open Science, Open Data, Open Source Projects for Undergraduate Research Experiences Kam D. Dahlquist, Ph.D. Department of Biology Loyola Marymount University BioQUEST/HHMI/CaseNet Summer Workshop June 13, 2015

2. Outline • An open science ecosystem enhances student learning • Quick example: XMLPipeDB project in a Biological Databases course • Longer example: GRNmap project in Biomathematical Modeling course • Potential research projects for BioQUEST participants • Challenges are also opportunities • Computer literacy • Data literacy • Information literacy

3. Open Science Ecosystem Open Pedagogy Open Science (open process) Open Data Open Source Code Open Access (creative commons) Research Integrity Reproducible Research Citizen Science With thanks to John Jungck

4. Open Science Pedagogy Adds Open Source Values and Tools to Problem Spaces • Students solve an authentic research problem. • They investigate large, publicly available datasets. • They return the products of their research to the scholarly community. Image: http://www.bioquest.org/bedrock/problem_spaces/

5. Official Open Source Definition (http://opensource.org) Free redistribution Source code Derived works Integrity of the author’s source code No discrimination against persons or groups No discrimination against fields of endeavor Distribution of license License must not be specific to a product License must not restrict other software License must be technology-neutral

6. Open Source Values Mirror STEM Curricular Reform

7. Pedagogy Implemented on Course Wikis • Team-taught and cross-listed • BIOL/CMSI 367: Biological Databases • https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Main_Page • BIOL/MATH 388: Biomathematical Modeling • http://www.openwetware.org/wiki/BIOL398-04/S15 • Single instructor • BIOL 368: Bioinformatics Laboratory • http://www.openwetware.org/wiki/BIOL368/F14 • BIOL 478: Molecular Biology of the Genome • (wet lab, mostly offline) • data analysis: http://www.openwetware.org/wiki/BIOL478/S15:Microarray_Data_Analysis • Weekly assignments leading up to final research project • All projects involve exploration of DNA microarray data

8. Pedagogy Implemented on Course Wikis • Team-taught and cross-listed • BIOL/CMSI 367: Biological Databases • https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Main_Page • BIOL/MATH 388: Biomathematical Modeling • http://www.openwetware.org/wiki/BIOL398-04/S15 • Single instructor • BIOL 368: Bioinformatics Laboratory • http://www.openwetware.org/wiki/BIOL368/F14 • BIOL 478: Molecular Biology of the Genome • (wet lab, mostly offline) • data analysis: http://www.openwetware.org/wiki/BIOL478/S15:Microarray_Data_Analysis • Weekly assignments leading up to final research project • All projects involve exploration of DNA microarray data

9. Biological Databases Team Final Project: create a gene database for a bacterial species http://xmlpipedb.cs.lmu.edu/ PostgreSQL Intermediate Database GenMAPP-compatible Gene Database Visualize data Microarray data

10. Each Student on the Team is Assigned a Specific Role Coder Project Manager Quality Control Data Analysis

11. Student Products Are Shared with the Scientific Community http://sourceforge.net/projects/xmlpipedb/

12. Pedagogy Implemented on Course Wikis • Team-taught and cross-listed • BIOL/CMSI 367: Biological Databases • https://xmlpipedb.cs.lmu.edu/biodb/fall2013/index.php/Main_Page • BIOL/MATH 388: Biomathematical Modeling • http://www.openwetware.org/wiki/BIOL398-04/S15 • Single instructor • BIOL 368: Bioinformatics Laboratory • http://www.openwetware.org/wiki/BIOL368/F14 • BIOL 478: Molecular Biology of the Genome • (wet lab, mostly offline) • data analysis: http://www.openwetware.org/wiki/BIOL478/S15:Microarray_Data_Analysis • Weekly assignments leading up to final research project • All projects involve exploration of DNA microarray data

13. Systems Biology Workflow DNA microarray data: wet lab-generated or published Statistical analysis, clustering, Gene Ontology, term enrichment New experimental questions Visualizing the results Generate gene regulatory network Modeling dynamics of the network

14. Systems Biology Workflow DNA microarray data: wet lab-generated or published Statistical analysis, clustering, Gene Ontology, term enrichment New experimental questions Visualizing the results Generate gene regulatory network Modeling dynamics of the network

15. Central Dogma of Molecular Biology (simplified) DNA Transcription mRNA Translation Protein Freeman (2003)

16. And Now in the “omics” Era… Genome Transcription Transcriptome Translation Proteome Freeman (2002)

24. Budding Yeast, Saccharomyces cerevisiae, is an Ideal Model Organism for Systems Biology • Small genome of ~6000 genes • Extensive genome- wide datasets readily accessible • Molecular genetic tools available Alberts et al. (2004)

25. Environmental Changes and Stresses • All organisms must respond to changes in the environment • pH • oxygen availability • pressure • osmotic stress • temperature (heat and cold) • Some changes in the environment cause cellular damage and trigger a “stress response” • damage from reactive oxygen species • damage from UV radiation • sudden and/or large change in temperature (increase or decrease)

26. Cold Shock Is an Environmental Stressthat Is Not Well-Studied • Increases in temperature (heat shock) • response very well-characterized • proteins denature due to heat • induction of heat shock proteins (chaperonins), that assist in protein folding • conserved in all organisms (prokaryotes, eukaryotes) • Decreases in temperature (cold shock) • response less well-characterized • decrease fluidity of membranes • stabilize DNA and RNA secondary structures • impair ribosome function and protein synthesis • decrease enzymatic activities • no equivalent set of cold shock proteins that are conserved in all organisms

27. Yeast Respond to Cold Shock by Changing Gene Expression • Cold shock temperature range for yeast is 10-18°C • Previous studies indicate that the cold shock response can be divided into: • Late response genes – 12 to 60 hours • General environmental stress response genes (ESR) are induced • Regulated by the Msn2/Msn4 transcription factors • Early response genes – 15 minutes to 2 hours • Genes unique to cold shock are induced, such as genes involved in ribosome biogenesis and membrane fluidity • Which transcription factors regulate this response is unknown

28. Transcription Factors Control Gene Expression by Binding to Regulatory DNA Sequences • Activators increase gene expression • Repressors decrease gene expression • Transcription factors are themselves proteins that are encoded by genes

29. Experimental Design and Methods

30. Yeast Cells Were Harvested for Microarrays Before, During, and After a Cold Shock and During Recovery

31. Mixture of labeled cDNA from two samples • 4 replicates of each experiment with dye swaps • wt and transcription factor deletion strains

32. DNA Microarray One spot = one gene Green = decreased relative to control Red = increased Yellow = no change in gene expression Freeman (2002)

33. Gene Expression Changes Due to Cold Shock Return to Pre-shock Levels During Recovery t30/t0 cold shock t60/t0 cold shock • Four sets of biological replicates were performed • Dye orientation was swapped for two sets of replicates t90/t0 recovery t120/t0 recovery

34. Steps Used to Analyze DNA Microarray Data 1. Quantitate the fluorescence signal in each spot 2. Calculate the ratio of red/green fluorescence 3. Log2 transform the ratios 4. Normalize the ratios on each microarray slide 5. Normalize the ratios for a set of slides in an experiment 6. Perform statistical analysis on the ratios 7. Compare individual genes with known data 8. Pattern finding algorithms/clustering • Modeling the dynamics of the gene regulatory network • Visualizing the results

35. Systems Biology Workflow DNA microarray data: wet lab-generated or published Statistical analysis, clustering, Gene Ontology, term enrichment Excel, stem New experimental questions Visualizing the results Generate gene regulatory network Modeling dynamics of the network

36. And so on…

37. Within-strain ANOVA Reveals How Many Genes Had Significant Changes in Expression at AnyTimepoint

38. A Modified T Test Was Used to Determine Significant Changes in Gene Expression at Each Timepoint wild type

39. Short Time Series Expression Miner (stem) Software Clusters Genes with Similar Profiles Expression (log2 fold change) Time (minutes)

41. Short Time Series Expression Miner (stem) Software Clusters Genes with Similar Profiles Expression (log2 fold change) Time (minutes) • Gene Ontology categories assigned to clusters: • Ribosome biogenesis • Zinc ion homeostasis • Hexose transport • Endomembrane system • Protein and vesicle transport • Negative regulation of nitrogen • compound process

42. The Transcription Factor Gln3 Regulates Genes Involved in Nitrogen Metabolism • Yeast differentiate between preferred and non-preferred nitrogen sources. • When the nitrogen source is poor, Gln3 localizes to the nucleus and activates genes required to utilize the poor nitrogen source. • The Dgln3 strain is impaired for growth at cold temperatures: • Doubling time at 13°C of 15 hours vs. 8.3 hours for wild type. • A microarray experiment was performed on the Dgln3 strain.

43. Gln3 Target Genes Were Extracted from the YEASTRACT Database 37 out of 164 (23%) have significantly different expression profiles in the wild type versus the Dgln3 strain

44. Systems Biology Workflow DNA microarray data: wet lab-generated or published Statistical analysis, clustering, Gene Ontology, term enrichment New experimental questions Visualizing the results YEASTRACT, Excel Generate gene regulatory network Modeling dynamics of the network

45. Genome-wide Location Analysis has Determined the Relationships between Transcription Factors and their Target Genes in Yeast • Does not show whether activation or repression occurs • Shows topology, but not the behavior of the network over time • Data found in YEASTRACT database Lee et al. (2002)

46. A Transcriptional Network Controlling the Cold Shock Response Assumptions made in our model: • Each node represents one gene encoding a transcription factor. • When a gene is transcribed it is immediately translated into protein; a node represents both the gene and the protein it encodes. • An edge drawn between two nodes represents a regulation relationship, either activation or repression, depending on the sign of the weight.

47. Systems Biology Workflow DNA microarray data: wet lab-generated or published Statistical analysis, clustering, Gene Ontology, term enrichment New experimental questions Visualizing the results Generate gene regulatory network Modeling dynamics of the network GRNmap (Windows-only)

48. GRNmap: Gene Regulatory Network Modeling and Parameter Estimation • Parameters are estimated from DNA microarray data from wild type and transcription factor deletion strains subjected to cold shock conditions. • Weight parameter, w, gives the direction (activation or repression) and magnitude of regulatory relationship.

49. The “Worst” Rate Equation is:

50. Optimization of the 92 Parameters Requires the Use of a Regularization (Penalty) Term • Plotting the least squares error function showed that not all the graphs had clear minima. • We added a penalty term so that MATLAB’s optimization algorithm would be able to minimize the function. • θ is the combined production rate, weight, and threshold parameters. • a is determined empirically from the “elbow” of the L-curve. Least Squares Residual Parameter Penalty Magnitude