An important aspect of the BCB training program is participation in Research Exploration Rotations. Participation in three research exploration rotations is required for all first year BCB students. The rotations serve several purposes:

• They are designed to help students choose their future major professors and to help professors choose graduate students;
• They provide students an opportunity to actively participate in research projects of BCB faculty laboratories; and
• They promote interaction and exchange of information among BCB research groups.

Selecting a Lab for Rotation

The selection of labs for rotations should be guided by the following:

• At least one rotation must be a "wet" laboratory experience (usually in a biological science laboratory using molecular biological, biophysical or biochemical techniques).
• At least one rotation must involve a strong computational component (usually in a research group in computer science, mathematics, physics, statistics or engineering).
• Students are strongly encouraged to participate in rotations in at least two different departments.

Faculty interested in having students rotate through their labs this Fall and in Spring, 2008, are below. Faculty who had rotations in the past are also listed. Links to their home pages are provided so you can become familiar with their on-going research projects. Some also have brief descriptions of potential rotation projects you might be involved with if you rotate in their labs.

Information on the research interests and links to all BCB faculty member's webpages can be found on our website at: http://www.bcb.iastate.edu/faculty/research.html.

Rotation Expectations

Because rotations are necessarily brief, students are not usually able to "complete" a project, in either a biological or computational research group. Instead, during the research exploration rotation period, students should:

• get to know the professor and the students and postdocs working in the research group;
• learn as much as possible about the professor's research projects;
• obtain "hands on" experience in one of the group's research projects;
• attend research group meetings and journal club meetings; and
• read reprints, reviews, and grant proposals related to the group's research.

It is appropriate for a rotating student to ask the rotation advisor whether the advisor would consider accepting him/her as a graduate student, but the final decision should not be made until all rotations have been completed.

Dates for Fall 2007/Spring 2008 Rotation Program

Forms to Establish Rotations/Home Departments

• Rotation Planning Form
• Establishing Home Department Form

Amy Andreotti  Biochemistry, Biophysics & Molecular Biology

• Dr. Andreotti's research - Interested in issues of protein structure and molecular recognition. Nuclear magnetic resonance (NMR) spectroscopy is a primary research tool in the lab and is used to solve protein structures, analyze protein mediated interactions and measure dynamic motions of proteins. All of the information gleaned from structural studies is used to formulate a better understanding of protein function in vivo. (8/14/05)

• Dr. Brendel's research - Algorithms for gene identification in genomic sequences; sequence alignment methods; transcriptional regulation; molecular phylogeny. He is accepting rotation students and invites interested students to visit his lab website for information about his research. (7/31/07)

Anne Bronikowski  Ecology, Evolution and Organismal Biology

• Dr. Bronikowski's research - Our research focuses on the evolution of life history variation with an emphasis on the evolution of senescence (the functional decline in biochemical and physiological processes with age). We address fundamental questions in life history evolution using field studies, laboratory experiments (physiological and molecular), and mathematical modeling. Current research focuses on the evolution and ecology of senescence in 1) natural reptile populations; 2) laboratory populations of mice and 3) semi-natural populations of baboons

  Dr. Bronikowski is accepting rotation students, and invites interested students to visit her lab website for information about her research. (7/19/07)

Hui-Hsien Chou  Genetics, Development and Cell Biology and Computer Science

• Dr. Chou's research - Bioinformatics, Computational Biology and Artificial Life.

Dr. Chou will be on sabbatical leave after November, thus he cannot offer a formal rotation project this academic year. However, he welcomes new students to contact him for rotation opportunities if they are interested in his work. If possible, Dr. can arrange a rotation project that fits his schedule and yours. Another way to work with Dr. Chou is to subscribe to his BCB 596 Genomic Data Processing class, which will be offered in the Fall. (7/19/07)

Di Cook, Statistics

• Dynamic graphics, exploratory data analysis, multivariate methods, statistical computing. She may have a rotation opportunity. Contact her. (8/15/07)

• 1. The Hordeum Toolbox: Develop methods for sorting and displaying SNP
  data using different similarity metrics based on pedigree, traits, and
  genomic sequence information.
  
  2.  PLEXdb: Experiment with different methods for pathway and annotation
  analysis in gene lists derived from microarray experiments. (www.plexdb.org)
  
• See her website (www.eng.iastate.edu/~julied) for information about her research. (8/14/07)

Drena Dobbs  Genetics, Development and Cell Biology

Four BCB Rotation Projects - Fall 2007

• #1- Tools for cracking the protein-RNA recognition code: RNABindR & PRIDB

  Protein-RNA interactions play critical roles in many essential biological processes. We are developing tools to investigate the molecular recognition code that mediates protein-RNA interactions.

  Dobbs lab projects involve collaborations with Honavar & Jernigan groups, and include:

  1) design, implementation and evaluation of improved machine learning algorithms to predict RNA binding sites in proteins (& protein binding sites in RNAs); implement in our web-based server, RNABindR

  2) design and implementation of a new database, Protein-RNA Interface Database(PRIDB), a comprehensive resource for analysis, characterization and visualization of structurally-characterized RNA-protein complexes (database will be modeled after PPIDB, see URL below):

  Web Resources: RNABindR: http://bindr.gdcb.iastate.edu/RNABindR/

  PPIDB: http://ppidb.cs.iastate.edu/

  References: Terribilini M, Sander JD, Lee JH, Zaback P, Jernigan RL, Honavar V, Dobbs D. RNABindR: a server for analyzing and predicting RNA-binding sites in proteins. Nucleic Acids Res. 2007 May 5; [Epub ahead of print]

  http://nar.oxfordjournals.org/cgi/content/full/gkm294v1

  Preferred skills: Some computer programming ability & basic biology

• #2- Using structural information to re-engineer Zinc Finger DNA binding domains

  We are using both computational and wet-lab experiments to design DNA binding proteins that specifically recognize unique sequences in genomic DNA. Our server, Zinc Finger Targeter (ZiFiT), is designed to facilitate the modular design of ZFPs as well as the discovery of "rules" that govern protein-DNA interactions.

  Dobbs lab projects involve collaborations with Voytas, Miller and Honavar groups, and include:

  1) develop improved algorithms for site-specific ZFP design, e.g., by evaluating the use of structural information, in addition to sequence information

  2) analyze and develop algorithms for distinguishing ZFPs that bind DNA vs RNA vs protein

  3) develop high throughput DNA binding assays (e.g., SPR or microarray-based) to evaluate affinity & specificity of designed ZFPs

  Web Resources: http://bindr.gdcb.iastate.edu/ZiFiT

  http://www.zincfingers.org/

  Reference: Sander JD, Zaback P, Keith Joung J, Voytas DF, Dobbs D. Zinc Finger Targeter (ZiFiT): an engineered zinc finger/target site design tool. Nucleic Acids Res. 2007 May 25;

  http://nar.oxfordjournals.org/cgi/content/full/gkm349v1

  Preferred skills: Some computer programming ability & basic biology

• #3- Predicting structure and functional sites in the human telomerase RNP complex

  Telomerase is a ribonucleoprotein (RNP) enzyme that adds telomeric DNA repeat sequences to the ends of linear chromosomes. The enzyme plays pivotal roles in cellular senescence and aging, and because it provides a telomere maintenance mechanism for ~90% of human cancers, it is a promising target for cancer therapy. Despite its importance, a high-resolution structure of the telomerase enzyme has been elusive.

  Dobbs lab projects involve collaborations with Ho, Honavar and Jernigan groups, and include:

  1) using threading and homology modeling to predict the structure of the telomerase reverse transcriptase enzyme, including its protein components (hTERT & dyskerin) and its RNA component (hTERC).

  2) using machine learning algorithms to predict which residues in the hTERT protein interact with DNA, RNA and other proteins.

   Web Resources: http://www.genlink.wustl.edu/teldb/tel.html

  http://www4.utsouthwestern.edu/cellbio/shay-wright/intro/sw_intro.html

  Reference: Blackburn, EH, Greider, CW, and Szostak, JW Telomeres and telomerase: the path from maize, Tetrahymena and yeast to human cancer & aging Nature Medicine 12, 1133 - 1138 (2006).

  http://www.nature.com/doifinder/10.1038/nm1006-1133

  Preferred skills: Some computer programming ability & basic biology

• #4- Deciphering SNARE complex interactions in Arabidopsis (Bassham/Dobbs Rotation)

  Membrane fusion reactions within cells are catalyzed by members of the SNARE protein family and regulated by SM proteins. Expansion of the SNARE family in plants makes Arabidopsis a particularly attractive system for studying the specificity and functional specialization of SNARE family members. We are beginning to use computational modeling approaches, in conjunction with genetic and biochemical analyses, to investigate the mechanism and regulation of SNARE function and complex formation. Our overall goal is to understand how structural features of the SNARE proteins lead to specificity in membrane fusion pathways in vivo.

  This Bassham/Dobbs rotation project also involves collaborations with Honavar, Jernigan, and Ho groups. Specific projects include:

  1) computational structure prediction: homology modeling of helical bundle interactions required for SNARE-catalyzed membrane fusion and analysis/prediction of both structural and phenotypic effects of mutations on helix association and SNARE functional specificity

  2) machine learning: analysis and prediction of interactions between SM proteins and SNAREs in Arabidopsis; tools originally developed for prediction of MHC epitopes will be modified to investigate specific sequence and structural motifs that mediate specific SM-SNARE interactions

  Web Resources: PepMIL: http://ailab.cs.iastate.edu/PepMIL

  References: Chen Y, Y-K Shin and DC Bassham. 2005. YKT6 is a core constituent of membrane fusion machineries at the Arabidopsis trans-Golgi network. J Mol Biol 350:92-101.

  Preferred skills: Some computer programming ability & basic biology (8/3/07)

Karin Dorman  Statistics

• You can find rotation information at her website at this address: http://rumi.gdcb.iastate.edu/ (8/16/07)

Oliver Eulenstein  Computer Science

• Dr. Eulenstein's Rotation projects - algorithms that support biologists in their efforts to construct the tree of all species -- the "Tree of Life". He writes, "I am accepting rotation students who are interested in developing, implementing and analyzing algorithms in collaboration with evolutionary biologists. Rotation students should have fundamental algorithmic knowledge (e.g. introductory algorithms course ComS 311). For more information visit our group website. (7/31/07)

Vasant Honavar  Computer Science

• Rotation projects in Honavar's Lab
   
  Analysis of macromolecular interaction networks
   
  Collaboration involving: Dr. Vasant Honavar, Dr. Drena Dobbs, Dr. Heather Greenlee and graduate students Fadi Towfic, Tim Alcon
   
  We are interested in analyzing mRNA and proteomics data in the context of other available information to elucidate macromolecular networks involved in retinal differentiation. Our group has developed Retina Workbench, a database and associated tools for exploring gene expression data sets. This project will involve application of the Retina Workbench, and in the case of students with sufficient computer programming expertise, extending the Retina Workbench further.
   
  Web resources: www.cs.iastate.edu/~honavar/aigroup.html
   
  References:
   
  Hecker, L., Alcon, T., Honavar, V., and Greenlee, H. (2007). Querying multiple large-scale gene expression datasets from the developing retina using a seed network to prioritize experimental targets. Under review.
   
  Kohutyuk, O. (2007). Retina Workbench. MS Thesis. Computer Science.
   
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  Structural, physicochemical, and geometric characterization of macromolecular (protein-protein, protein-DNA, and protein-RNA) interfaces
  Collaborations involving:  Dr. Vasant Honavar, Dr. Drena Dobbs, Dr. Robert Jernigan, and graduate students Feihong Wu, Cornelia Caragea, Michael Terribilini
   
  We have developed machine learning approaches to analysis and prediction of protein-protein, protein-DNA, protein-RNA interfaces from amino acid sequence (and whenever available), protein structure. We have also constructed a comprehensive databases of protein-protein and protein-RNA interfaces. We are especially interested in characterization of structural and physicochemical characteristics of interfaces. This project will involve analysis and characterization of macromolecular interface datasets. Of particular interest is analysis of the RNA side of the protein-RNA interfaces.
   
  Web resources:
  http://www.cs.iastate.edu/~honavar/aigroup.html
  http://ppidb.cs.iastate.edu/
  http://bindr.gdcb.iastate.edu/RNABindR/
   
  References:
   
  Terribilini, M., Sander, J.D., Lee, J-H., Zaback, P., Jernigan, R.L., Honavar, V. and Dobbs, D. (2007). RNABindR: A Server for Analyzing and Predicting RNA Binding Sites in Proteins. Nucleic Acids Research. doi:10.1093/nar/gkm294
   
  Yan, C., Dobbs, D., Jernigan, R., and Honavar, V. (2007). Characterization of Protein-Protein Interfaces. Protein. In press.
  
  Terribilini, M., Lee, J.-H., Yan, C., Jernigan, R. L., Honavar, V. and Dobbs, D. (2006). Predicting RNA-binding Sites from Amino Acid Sequence. In: RNA Journal.. Vol. 12. No. 1450. pp. 1462.
  
  Yan, C., Terribilini, M., , Wu, F., Jernigan, R.L., Dobbs, D. and Honavar, V. (2006) Identifying amino acid residues involved in protein-DNA interactions from sequence. BMC Bioinformatics, 2006.
   
  Wu, F., Towfic, F., Dobbs, D. and Honavar, V. (2007) Analysis of Protein Protein Dimeric Interfaces. IEEE International Conference on Bioinformatics and Biomedicine, San Jose, In press, 2007.
   
  ----
   
  Machine learning approaches to functional annotation of proteins
   
  Collaboration involving: Dr. Vasant Honavar, Dr. Drena Dobbs, and graduate students Carson Andorf, Cornelia Caragea
   
  We have been developing machine learning approaches to automated functional annotation of proteins. Carson Andorf has used these methods to  discover potential errors in  annotations of Mouse protein kinases in MGD. This project will involve analysis of several additional datasets for potential annotation errors.
   
  Web resources:
  http://www.geneontology.org/meeting/go_users_2006_09_seattle/Honavar.ppt
   
   
  References:
   
  Andorf, C., Dobbs, D., and Honavar, V. (2007). Detecting Inconsistencies in Genome-wide Protein Function Annotations: A Machine Learning Approach.  BMC Bioinformatics.
   
  ---
   
  Machine learning approaches to prediction of functional sites (e.g., phosphorylation sites, glycosylation sites, MHC peptide binding sites) in proteins.
   
  Collaboration involving: Dr. Vasant Honavar, Dr. Drena Dobbs, Yasser El-Manzalawi, Cornelia Caragea, Jivko Sinapov
   
  We have been developing machine learning methods for automated identification of functional sites in proteins. Yasser, Cornelia, and Jivko have  developed  novel machine learning algorithms for this problem. This project will involve application of machine learning algorithms to improve the accuracy of functional site prediction.
   
  Web resources:
  http://ailab.cs.iastate.edu/PepMIL/
  http://turing.cs.iastate.edu/EnsembleGly/
   
  References:
   
  El-Manzalawi, Y., Dobbs, D., and Honavar, V. (2007). PepMIL: A Novel Method for Predicting Flexible Length MHC-II binding Peptides. Under review.
   
  Caragea, C., Sinapov, J., Silvescu, A., Dobbs, D., and Honavar, V.  (2007). Glycosylation Site Prediction: A Machine Learning Approach. Under review.
   
  ---
  Development of Web Services Associated with Protein-Protein Interface Database (PPIDB)
   
  Collaboration involving Dr. Vasant Honavar, Dr. Drena Dobbs, Dr. Robert Jernigan, and graduate students Feihong Wu and Fadi Towfic
   
  We have developed a comprehensive database of protein-protein dimeric interfaces. This project involves development of web services to perform additional analysis and visualization  of PPIDB data.
   
  Web resources:
  http://ppidb.cs.iastate.edu
   
  References:
   
  Wu, F., Towfic, F., Dobbs, D. and Honavar, V. (2007) Analysis of Protein Protein Dimeric Interfaces. IEEE International Conference on Bioinformatics and Biomedicine, San Jose, In press, 2007.
   
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  AnexDB: Animal Gene Expression Database and Associated Analysis Tools
   
  Collaboration involving Dr. Vasant Honavar and Dr. Chris Tuggle and graduate students Oliver Couture and Neeraj Koul
   
  We are developing an animal gene expression database and associated tools for querying and analysis of data from gene expression analysis experiments. This project involves extending the current implementation of AnexDB. (8/6/07)