BCB 691 Faculty Seminar Schedule - Fall 2017

BCB Faculty Seminar Schedule for Fall 2017

Location: 1424 Molecular Biology Building

Day/Time: Wednesdays, 4:10 p.m.

August 23 - Lizhi Wang, IMSE and Nicole Valenzuela, EEOB

August 30 - Gregory Phillips, VMPM and Adina Howe, ABE

Sept. 6 - Ludovico Cademartiri, MSE, Iddo Friedberg, VMPM and Zhijun Wu, Math

Sept. 13 - Carolyn Lawrence-Dill, GDCB and Eric Henderson, GDCB

Sept. 20 - Walter Moss, BBMB; Justin Walley, Plant Pathology and Microbiology

Sept. 27 - Robert Jernigan, BBMB

October 4 - Drena Dobbs, GDCB

October 11 - Julie Dickerson, ECPE

October 18 - Dior Kelley, GDCB

October 25 - Cancelled

Nov. 1 - Karin Dorman

Nov. 8 - Allen Miller

Nov. 15 - Crystal Lu

Nov. 29 - Gustavo Macintosh

Dec. 6 - No Class

August 23, 2017

Lizhi Wang

Department: Industrial & Manufacturing Systems Engineering

My research interests include mathematical modeling and computational optimization, with applications in TEAM (transportation, energy, agriculture, and manufacturing) systems optimization. In particular, I am very interested in applying operations research methods to improve the efficiency of plant breeding.

More information can be found here

Area of Expertise: mathematical modeling and computational optimization

Contact: lzwang@iastate.edu


Nicole Valenzuela

Department: Ecology, Evolution and Organismal Biology


We are interested in studying how ecology affects the structure, function, and evolution of the genome and its role in the development and evolution of complex phenotypes. This helps us understand the evolution of biological diversity and how it responds to environmental change.

Sex Determination

Why are there so many ways to establish the sexual fate of the developing embryo?

It is fascinating that sexually-reproducing organisms employ such diversity of mechanisms to produce males and females, ranging from systems under strict genetic control (GSD) [such as highly dimorphic or undifferentiated sex chromosomes (XY, ZW)], to genetic systems susceptible to some environmental influences [such as haplo-dyploidy, polygenic systems, socially-induced sex reversals], to systems under strict environmental control dependent on biotic or abiotic factors. Among vertebrates, an environmental system dependent on temperature (TSD) is commonly found in reptiles and fish.

To explain this diversity we investigate the mechanics, evolutionary dynamics and ecological context of sex determination through a series of complementary projects.

Research on sex determination has important implications for our understanding of multiple traits and phenomena related to sexual reproduction, such as Sex Allocation and Sex Ratio Evolution, Sexual Dimorphism and Sex-linked Traits.

Phylogenomics - What are the genomic causes and consequences of the evolutionary transitions in sex determination? - Sex Chromosome Evolution - Genome Evolution - Evolution of Dosage Compensation

Comparative Ecological and Functional Genomics - How do the developmental networks that control sexual development function during embryogenesis and evolve? What renders TSD thermosensitive?

Bioinformatics - The research areas above include extensive comparative and evolutionary bioinformatic studies across multiple turtles with TSD and GSD that are open for BCB projects (e.g. trancriptomics, methylomics, genome assembly, transcription factor analyses, ChIPseq, among others). Interested students should contact Dr. Valenzuela directly at nvalenzu@iastate.edu to discuss opportunities for rotations and dissertations.

Area of Expertise: Comparative Genomics

Contact: nvalenzu@iastate.edu

Laboratory of Ecological and Evolutionary Genomics

August 30, 2017

Gregory Phillips

Department: Veterinary Microbiology and Preventive Medicine

Microbial Genetics-Our research includes bacterial genetics and genomics, as well as microbiome analysis of selected animal models for human biomedical research.

Area of Expertise: Microbial Genomics

Adina Howe

Department: Agricultural and Biosystems Engineering


Adina Howe's research integrates molecular and computational biology to study, model, and manage complex microbial systems in both natural and engineered environments.

Her research revolves around several themes:

  • identifying microbial drivers of biogeochemical cycling and their response to climate change
  • understanding contributions of microbial genes, individuals, and groups to population function and dynamics
  • detection of biomarkers for environmental health (e.g., antibiotics and pathogens)
  • scalability of increasingly large sequencing datasets through the application of advanced computational approaches
  • leveraging high throughput, next-generation metagenomic and metatranscriptomic sequencing to investigate interactions within environmental microbial communities
  • development of sustainable scientific data/software practices.

Area of Expertise: Metagenomics

Contact: adina@iastate.edu

Personal Website for Adina Howe

Lab Website for Adina Howe


3346 Elings Hall

September 6, 2017

Three BCB Faculty will present at the BCB Faculty Seminar on Wednesday, Sept. 6 at 4:10 p.m. in 1424 MBB:

Ludivico Cademartiri

Departments: Chemical and Biological Engineering and Materials Science and Engineering

Research Interests: We have recently been awarded a Beckman Young Investigator Award by the Arnold and Mabel Beckman Foundation to explore collective behaviors in plants that might be originating from root-root connections that would enable plants to form communities capable of exchanging effectively small amounts of signaling molecules over large distances. Interestingly, the behavior of this community should then depend on the topology of the network formed by the root systems. Our proposal detailed the construction of experimental setups for the creation of such communities of plants and their investigation.

This study is highly interdisciplinary and would require expertise that extend far beyond pure engineering. We are therefore interested in candidates that would understand biology but also have the mathematical capabilities to handle network theory, without being afraid of the highly disciplined type of experimental work that is required.

Area of Expertise: Materials Science

Contact: lcademar@iastate.edu

Cademartiri Lab


3109 Gilman Hall

Iddo Friedberg

DepartmentVeterinary Microbiology and Preventive Medicine

I am interested in large scale analyses of proteins, genomes and metagenomes.

Metagenomics is the study of genomic material extracted directly from the environment. New sequencing technologies have enabled the study of whole populations of genomes taken from microbial communities in the field, as opposed to single species clonal cultures in the lab. Metagenomics offers a way to study how genomes evolve to cope with the microbial biotic and abiotic environments. Our lab helped developed a method to study the correlation between the human gut microbiota and gut gene expression. We are applying this method towards studying infant gut development the effect of gut microbes on human health and wellness.

Bacterial Genome Evolution: Gene blocks are a common occurrence in bacteria: these are genes which lie close together on the chromosome, and may participate in a common cellular or biochemical function. Operons are gene blocks whose member genes are co-transcribed. We have developed a new method to describe the evolution of operons and gene blocks in bacteria. We describe a small set of evolutionary events that can take place in gene block evolution, and count these events to create a new type of molecular clock that tells us how fast or how slow certain gene blocks may have evolved. We hope to learn how new funcitons are acquired by ensembles of genes such as these.

Function Prediction

Another interest of mine is the prediction of protein function. Genomics, proteomics and various other ``-omics'' inundate us with sequence and structure information, but the biological functions of those proteins in many cases still eludes us. Computational prediction of protein and gene function is a rapidly growing research field in bioinformatics [4]. I am the co-organizer of the automated computational protein function prediction meetings: AFP. The AFP meetings bring together researchers to discuss various methods for protein function prediction. My personal interest in function prediction lies in predicting function from protein structure [5]. We have recently started work on predicting gene function based on its genomic context in bacteria, using both genomic and metagenomic data towards that end.

Structural Signatures

We are interested in locating ``structural signatures'' that span different protein folds. My working hypothesis is that there are short local structural commonalities between proteins that otherwise share no obvious structure or function. Detecting these commonalities can help us understand protein evolution, folding, and design. [1] ,  [2]

Different Representations of Protein Structures

The computational representation of a protein's 3D structure is a challenging problem because of varying and often conflicting considerations: at first sight it seems that as far as information is concerned, more is better, hence the drive to atomic level description. However, elaboration on the atomic level can be very ``noisy'' and be time and memory intensive. Therefore we often ask what is the minimal information we need to achieve a specific task, without going into the unnecessary detail of representing each and every atom. I am interested in different computational representations of protein structures suitable for different tasks. In one study we have shown that a 1D representation of protein structures can be used for fast database searching and alignments, and still preserve relevant structural information. [3].

Area of Expertise: Bioinformatics, genomics, Critical Assessments

Contact: idoerg@iastate.edu

Lab Website

Google Scholar



2118 Vet Med

Zhijun Wu

Department: Mathematics


Nonlinear optimization, game theory, numerical linear algebra, linear programming, integer & combinatorial optimization, mathematical biology, protein modeling, modeling of evolution and natural selection

Selected Papers:

  • Wang M, Huang Y, Wu, Z, Simulation of yeast cooperation in 2D, Bull Math Biol 78, 531-555, 2016
  • Huang Y, Wang M, Hao Y, Zhou W, Wu Z, Optimality and stability conditions of symmetric evolutionary games with applications to genetic selection, J Math Biosci & Engineering 12, 503-523, 2015
  • Park J, Jernigan R, Wu Z, Coarse-grained NMR vs refined GNM for protein residue-level fluctuations, Bull Math Biol 75, 124-160, 2013
  • Huang Y, Wu Z, Game dynamic model for yeast development, Bull Math Biol 74, 1469-1484, 2012
  • Sit A, Wu Z, Solving a generalized distance geometry problem for protein structure determination, Bull Math Biol 73, 2809-2836, 2011

Area of Expertise: optimization and game theory; protein modeling; evolutionary dynamics; 

Contact: zhijun@iastate.edu

Zhijun Wu - Mathematic Department's website


462 Carver Hall

September 13, 2017

Eric Henderson

Eric Henderson’s path in science is:

  • B.A. in Biology UCLA
  • Ph.D. in Molecular Biology UCLA
  • Post-Doc studying telomere biology, UC Berkeley
  • Professor, ISU

Eric teaches, promotes and practices rule breaking (when appropriate), fearlessness, and entrepreneurship in all facets of life. He has been involved in several companies including:

  • Bio Force Nanosciences, Inc., (biotech)
  • Aspera Corp (biotech)
  • Creodyne, llc (tech development)
  • Bumblefunk Music (media)
  • Hello Holdings (Griffle, GriffleGames, Griffle TV; media)
  • eMoJoCo, llc (art and media)

Eric is always looking for challenges and opportunities to work with young entrepreneurs. On the side Eric is a performing musician, fiction writer, tinkerer, and gadget freak.


We are interested in the interface between biology and nanotechnology. This has recently been labeled either bionanotechnology or synthetic biology. The label is of no importance. What is important is that 3.6 billion years of biological evolution has resulted in fantastic developments that are the stuff of which the dreams of nanotechnology is made. The challenge is to understand and, in a practical fashion, transfer these insights to the emerging field of nanotechnology so that the wheel (and eyeball, nose, etc. ) need not be invented twice. This is no small challenge. We have created a few tools that will facilitate this effort and a portion of my time is spent determining how best to provide these tools to researchers. A conclusion I have drawn is that a commercial enterprise accomplishes this and also provides an avenue (in theory) by which future developments may be funded and manifest. This theory is far from proven, however.

More on the Henderson Lab Research

Using the magic of DNA hybridization we design and build self-assembling DNA Nanodevices like the Pathogen Sentinel.

DNA Nanostructures and Devices: We make functional nanodevices out of DNA. Our most current device is a pathogen sentinel that can detect, measure and report the presence of a variety of pathogen-related biomarkers. Billions of these sentinels can be created for pennies in a few microliters of saltwater. Even better, since they are made out of DNA they are extremely robust.

We also developed a new method for creating 2D and 3D DNA nanostructures. This method uses DNA origami as a design tool but does not require a single-stranded scaffold of biological origin. In this way, our method allows the creation of any number of DNA nanostructures with much fewer restrictions on size and, importantly, simultaneous assembly in a single reaction ("single pot" self-assembly). Creating useful machines and expanding the general method of DNA-based nanodevice construction are currently the main objectives.

Recent publications

  • R. Lutz, J. Lutz, J. Lathrop, T. Klinge, E. Henderson, D. Mathur, and D. Abo Sheasha, (2012) Engineering and verifying requirements for programmable self-assembling nanomachines, Proceedings of the Thirty-Fourth International Conference on Software Engineering (ICSE 2012, Zurich, Switzerland, June 2-9, 2012), pp. 1361-1364.
  • Lutz, Robyn R., Lutz, Jack H., Lathrop, James I., Klinge, Titus H., Mathur, Divita, Stull, Don M., Bergquist, Taylor G. and Henderson, Eric R. (2012) Requirements analysis for a product family of DNA nanodevices, Proceedings of the Twentieth IEEE International Requirements Engineering Conference (RE 2012, Chicago, IL, September 24-28, 2012), pp. 211-220.
  • Mathur, D. and Henderson, E. (2013) Complex DNA Nanostructures from Oligonucleotide Ensembles, ACS Synthetic Biology, 2, 180-185.
  • Ellis, Samuel J., Henderson, Eric R., Klinge, Titus H., Lathrop, James I., Lutz, Jack H., Lutz, Robyn R., Mathur, Divita, and Miner, Andrew S. (2014) Automated Requirements Analysis for a Molecular Watchdog Timer In Proceedings of the 29th ACM/IEEE international conference on Automated software engineering (ASE '14). ACM, New York, NY, USA, 767-778. DOI=10.1145/2642937.2643007 (Awarded the "Manfred Paul Award for Excellence in Software: Theory and Practice").

Area of Expertise: Nanotechnology

Other Affiliations: Henderson Laboratory


Carolyn Lawrence-Dill


By creating tools to enable the automated analysis of data and by creating unique data storage solutions, we hope to enable other researchers to accomplish their research goals more efficiently and effectively.

We develop computational systems and tools that enable researchers to leverage plant genetics and genomics information to better understand basic biology and effect crop improvement. Group members are specifically interested in functional prediction for genes, predictive phenomics, and how gene and chromosome architecture regulate cellular processes. Although work by group members is not specifically limited to maize, it is by far our favorite model system.

Maize Genomics

How are plant chromosomes arranged? Is it possible to relate the genetic and cytological maps to an assembled genome sequence? Are there sequences present at centromeres that signal the cell to construct kinetochores, the machines that ensure proper chromosome segregation to occur, at the correct site?

As the genomes of more plants get sequenced, complex questions like these can can be translated into testable hypotheses. Eventually the content of plant genomes can be related to broad function, both within the cell and at the level of the organism as a whole.

Convergence of traditional biological investigation with genome content and organization is the focus of much of the work carried out in this group. We explore this area of research using maize, Arabidopsis, and other plants.

Phenotype Prediction for Basic Research

The ability to compare phenotypes, both within and across species, enables predictive biology. Though descriptions of myriad aspects of phenotype are readily available, representation of morphology, development, and other traits using computable formats is in its infancy. It has been shown in vertebrate and other (primarily animal) systems that biological equivalencies can be predicted across broad diversity based on reasoning across phenotype ontology markup of experimentally well-characterized genes and pathways. Within plants, maize, rice, soybean, Medicago, Arabidopsis, and tomato have sufficient gene function information (as inferred from mutational screens) to develop such systems. Given current data and existing algorithms that reason across annotations, it is now possible to assert biologically relevant phenolog relationships associated with genes, genomic regions, molecular pathways, and gene function data for plants.

This sort of work enables:

(1) Prediction of the biology that underlies phenotype in non-model systems, including crops that do not have well-characterized genomes (e.g., blueberry, strawberry, apple, peach, etc.). Using this method, the phenotype of a non-model plant can be used to query model species' genes, molecular markers, pathways, etc. directly to bootstrap testable hypotheses.

(2) Identification of non-obvious model systems to study conserved processes across broad taxa.

(3) Creation of phenotypic data systems that interoperate.

Crop Improvement: Phenotype = Genotype x Environment

Codifying and integrating genotypes with phenotypes and precise environmental conditions enables the discovery of basic biological mechanisms and revolutionize plant breeding. Currently deployed high-throughput phenotype data collection and analysis systems cannot be leveraged across multiple groups' datasets due to the complete absence of guidelines. The development and use of standards and best practices will allow researchers to tease out biologically-relevant environmental conditions and molecular mechanisms from large-scale datasets to enable targeted crop improvement. Satisfying this basic need to enable data sharing is necessary to effect a scale-change for basic biology that leads to agricultural advancement and is critically needed given that doubling production by 2050 in the face of climate change is required to meet worldwide projected needs.

Area of Expertise: 

Comparative Genomics

Crop Genetics


  • B.A., Biology, Hendrix College, 1996
  • M.S., BIology, Texas Tech University, 1997
  • Ph.D., Botany, University of Georgia, 2003


September 20, 2017

Walter Moss

Department: BBMB

RNA is considered by many to be the primordial molecule of life. The major component of the ribosome—the most ancient organelle, which is shared between all living things—is ribosomal (r)RNA; which also forms the catalytic center necessary for protein production. The instructions needed for producing proteins is encoded within messenger (m)RNA, which is decoded by the ribosome by way of transfer (t)RNA. Beyond the classical RNAs involved in protein synthesis, a wide array of noncoding (nc)RNAs exist that mediate important biological processes: e.g. regulation of gene expression, mRNA splicing, post-transcriptional modification, chromatin structure, and more. In the vast majority of cases, we know almost nothing about the function of ncRNA (the transcriptional “dark matter”); however, function is inferred from the differential expression/processing of ncRNAs (e.g. in diseases such as cancer) or from their evolutionary conservation. An important feature of all functional RNAs, is the central role played by molecular structure. RNAs can fold back on themselves to form complex 2D (base paired) and 3D (atomic arrangement) shapes. These shapes govern how RNAs interact with other biomolecules (e.g. proteins, nucleic acids and small-molecules), form catalytic centers, determine molecular stability (e.g. lifetime) of RNA, and more. The major goal of the Moss Lab is to identify RNA sequences with a high propensity to form structure, deduce their structures, functions, and the roles played by structure.

Area of Expertise: Noncoding RNA discovery; RNA structure and function


  • B.S., Chemistry, SUNY Stony Brook
  • Ph.D., Chemistry, University of Rochester

Contact: wmoss@iastate.edu


3256 Molecular Biology Building

Website: Walter Moss Lab Website


Justin Walley

Research in my lab investigates molecular mechanisms that underpin plant-microbe interactions.  Our research focuses on immune signaling in corn and Arabidopsis. We specialize in mass spectrometry based proteomics to globally quantify protein abundance and post-translational modifications (including phosphorylation and acetylation). Using systems biology we integrate these data with other types of omics datasets, such as transcriptome profiling, to generate hypotheses to test using various biochemical and genetic approaches. 

Area of Expertise: proteomics


  • PhD, University of California - Davis, 2009
  • MS, Miami University, 2005
  • BS, University of Mount Union, 2001


BCBIO 402: Fundamentals of Bioinformatics and Computational Biology II

Contact: jwalley@iastate.edu


423 Bessey

Website: Google Scholar

September 27, 2017

Robert Jernigan

DepartmentBiochemistry, Biophysics and Molecular Biology

Computational studies on the structures of cells, proteins, nucleic acids, and small molecules, and their interactions. Overall the direction of his research has been to push toward the comprehension of the functions of large structures, and the incorporation of diverse data.

Data mining is used to assess protein structures and their interactions, to obtain data for improving sequence matching and to identify the important parts for their mechanisms. We developed a standard ways to view interaction energies between residues, based on sets of protein structures, and standard ways for extracting entropies from changes in structures. These approaches have led to useful ways to incorporate structural information into simulations and new ways to incorporate structural information into protein sequence matching.

Protein Dynamics can be extracted from sets of experimental structures, or from simple models. Large-domain motions of proteins are computed with simple inter-connected elastic models. These highly cohesive, cooperative models are most appropriate for considering the largest functional motions of proteins, which are necessarily independent of the structural details. Functional mechanisms for processing proteins or for protein machines can be developed. The methods lend themselves in straightforward ways to the investigation of the motions of extremely large biomolecular assemblages.

Molecular Mechanisms are developed from protein dynamics, by combining the slowest motions into a sequence of events. Important changes to the dynamics are observed when ligands bind or proteins interact with other proteins.

Deleterious Protein Mutants can affect protein stabilities in significant ways, making them either more stable or less stable. The effects of point mutations are complex and require consideration of their structural environment. These interactions affect the allosteric behaviors, with many deleterious mutants interfering with the biological mechanism.

Dr. Jernigan's new book, Protein Actions - Principles and Modeling with Garland Science is now available.

Research interests: Molecular models; Nucleic acids; Small molecules; Structures of proteins


  • B.S., Chemistry, California Institute of Technology, 1963
  • Ph.D., Physical Chemistry, Stanford University, 1968
  • Postdoctoral Fellow, University of California – San Diego





303 Kildee




The Jernigan Laboratory


Complete Bibliography of Published Work – (Robert Jernigan)

October 4, 2017

Drena Dobbs

Department: Genetics, Development & Cell Biology

Title: Machine Learning in Genomics: CRISPR-Cas9 Design & RNA-Protein Interaction Prediction 

Research description:

Long-term research goals of the Dobbs group are to understand how proteins and nucleic acids achieve their functional three-dimensional structures and to elucidate mechanisms that determine recognition and regulate interactions among proteins, nucleic acids and other molecules in cells.  We have used both computational and wet-lab experimental approaches to explore the structure and function of important macromolecular complexes, in close collaboration with several groups at ISU, Pennsyvania State University, University of Minnesota, the Mayo Clinic, and Harvard University.

Current areas of focus include: providing computational resources to improve efficacy of CRISPR-Cas9 and other designer nucleases for precision genome modification; predicting RNA-protein interactions and interaction networks; predicting and validating of structural and functional effects of mutations and SNPs in both proteins in ncRNAs; comparative genomics.

Recent Publications:


  • ddobbs@iastate.edu
  • University Professor
  • Genetics, Development & Cell Biology
  • 437A Bessey Hall 
  • 515-294-4991 (lab)

October 11, 2017

Julie Dickerson

Department: Electrical and Computer Engineering

Julie Dickerson is a Professor at Iowa State University in the Department of Electrical and Computer Engineering (ECpE). She served as a program officer at the National Science Foundation in the Advances in Biological Informatics Program and the Postdoctoral Research Fellowships in Biology Program in the Biology Directorate as the lone engineer. She has also served as the Chair of the Bioinformatics and Computational Biology program at Iowa State University.

She holds a B.S. degree in electrical engineering from the University of California, San Diego. She received her master's degree and Ph.D. in electrical engineering from the University of Southern California. She designed radar systems for Hughes Aircraft Company and Martin Marrietta while getting her Ph.D. Her current research activities are in systems biology, bioinformatics, bioinformatics education, and data visualization. She was a Carver Fellow in the Virtual Reality Applications Center and a member of the Baker Center for Bioinformatics in the Plant Sciences Institute and the Human-Computer Interaction Program. Dr. Dickerson has over 100 peer-reviewed publications in journals, book chapters, and conference proceedings and supervises research projects funded by the National Science Foundation, ARDA, and the United States Department of Agriculture.

Research Interests

  • Systems biology, bioinformatics, pattern recognition, data visualization, real-time sensor networks, metabolomics, image processing, machine learning
  • Core Research Area: Communications and signal processing; secure and reliable computing
  • Strategic Research Area: Bioengineering

Selected Publications

  • Mao, L., J. Van Hemert, S. Dash, and J. Dickerson. Arabidopsis Gene Co-expression Network and Its Functional Modules. BMC Bioinformatics 10, (2009): 346.
  • Grimplet, J., G. R. Cramer, J. A. Dickerson, K. Mathiason, J. Van Hemert, and A. Y. Fennell. VitisNet: Omics Integration through Grapevine Molecular Networks. PLoS ONE 4, no. 12, (2009): e8365.
  • Call, A., S. Herrnstadt, E. S. Wurtele, J. Dickerson, and D. Bassham. Meta!Blast Virtual Cell: A Pedagogical Convergence Between Game Design and Science Education. Journal of Systematics, Cybernetics, and Informatics 5, no. 5, (December 2007): 27-31.?
  • Zhou W., T. Xia, J. Tong, J. Dickerson, B. Su, and X. Gu. Modeling Protein Interaction Network and Mechanisms in Exocytosis, In Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, Cambridge, MA, November 2007.?
  • S. Y. Rhee, J. Dickerson, and D. Xu. Bioinformatics and its Applications in Plant Biology, Annual Review of Plant Biology 57, (2006): 335-359.


  • Ph.D., Electrical Engineering, University of Southern California, 1993
  • M.S., Electrical Engineering, University of Southern California, 1986
  • B.S., Electrical Engineering, University of California at San Diego, 1983


October 11, 2017

Dr. Dior Kelley

Department: GDCB

Dr. Kelley, Adjunct Assistant Professor, received her B.S. in Chemistry from the University of California Santa Cruz in 2000, and her Ph.D. in Plant Biology from the University of California Davis in 2009. Dr. Kelley joined GDCB in January 2015.

Research Description

Auxin is a major plant hormone that plays instructive roles in almost every aspect of plant biology, including cell growth and fate. Depending on the context, auxin action can lead to different developmental outcomes. How this is achieved is not well understood. My research program explores how auxin signaling modules control diverse developmental processes in Arabidopsis. I am applying a variety of molecular approaches to these studies, including RNA-seq, Ribo-seq, proteomics and phenotyping. Such multi-scale integration will elucidate new aspects of auxin biology, including links between different aspects of gene regulation and identify novel regulators of plant development.


  • Gilkerson J., Kelley D.R., Tam R., Estelle M., and Callis J. Lysine residues are not required for proteasome-mediated proteolysis of the Aux/IAA protein IAA1. Plant Physiology, published online before print April 2015.
  • Kelley, D.R. and Estelle, M. (2012) Ubiquitin-mediated control of plant hormone signaling. Plant Physiology 160(1): 47-55.
  • Kelley, D.R., Arreola, A., Gallagher, T., and Gasser, C.S. (2012) ETTIN (ARF3) physically interacts with KANADI proteins to form a functional complex essential for integument development and polarity determination in Arabidopsis. Development 139(6): 1105-9.
  • Walley, J.W., Kelley, D.R., Savchenko, T., and Dehesh, K. (2010) Investigating the function of CAF1 deadenylases during plant stress responses. Plant Signal Behav. 5(7):802-5.
  • Walley, J.W., Kelley, D.R., Nestorova G, Hirschberg D, Dehesh, K. (2010) Arabidopsis deadenylases AtCAF1a and AtCAF1b mediate response to environmental stress. Plant Physiology 152(2): 866-75.
  • Kelley, D.R., and Gasser, C.S. (2009) Ovule Development: Genetic Trends and Evolutionary Considerations. Sexual Plant Reproduction 22(4): 229-34.
  • Kelley, D.R., Skinner, D, and Gasser, C.S. (2009) Roles of polarity determinants in ovule development. The Plant Journal 57(6): 1054-64. (Cover article).
  • Kaothien, P., Ok, S.H., Shuai, B., Wengier, D., Cotter, R., Kelley, D.R., Kiriakopolos, S., Muschietti, J., and McCormick, S. (2005) Kinase partner protein interacts with the LePRK1 and LePRK2 receptor kinases and plays a role in polarized pollen tube growth. The Plant Journal 42 (4): 492-503.
  • Tang, W.H., Kelley, D.R., Ezcurra, I., Cotter, R., and McCormick, S. (2004) LeSTIG1, an extracellular binding partner for the pollen receptor kinases LePRK1 and LePRK2, promotes pollen tube growth in vitro. The Plant Journal 39(3): 343-53.

Area of Expertise: 

Using systems integration of large-scale omics approaches to explore how auxin signaling modules control diverse developmental processes in Arabidopsis


  • B.S., Chemistry, University of California Santa Cruz, 2000
  • Ph.D., Plant Biology, University of California Davis, 2009


November 1, 2017

Karin Dorman
Dale D. Grosvenor Chair



Dr. Dorman received her B.S. in Mathematics and Biology from Indiana University in 1994, and her Ph.D. in Biomathematics from UCLA in 2001. Dr. Dorman joined the staff of Iowa State University in 2001 with joint appointments in the Department of Statistics and the Department of Genetics, Development and Cell Biology. Dr. Dorman’s research interests include modeling biological phenomena using mathematical models, with particular emphasis on evolution, genetics, pathogen/host evolution and interactions, and sequence analysis.

Research Description

I build mathematical and statistical models to address a variety of interesting problems mostly related the biology of infectious diseases. My principle focus has been the development of models to extract information from pathogen sequence information. For example, I have developed statistical models to detect the presence of recombination in pathogen sequences. Using these models to identify the location of recombination crossover events, has permitted the identification of possible hotspots of recombination. Another model uses sequence information to identify the historical location of selection events. A selection event associated with the formation of distinct HIV genotypes may ultimately help explain differences in viral pathogenecity.


  • K. S. Dorman, A. H. Kaplan, J. S. Sinsheimer. 2002. Bootstrap confidence levels for HIV-1 recombinants. Journal of Molecular Evolution 54(2):200-209.
  • M. Patel, K. S. Dorman, Y.-H. Zhang, B.-L. Huang, A. P. Arnold, J. S. Sinsheimer, E. Vilain, E. R. B. McCabe. 2001. Primate DAX1, SRY, and SOX9: evolutionary stratification of sex determination pathway. American Journal of Human Genetics 68:275-280.
  • K. S. Dorman, A. H. Kaplan, K. Lange, J. S. Sinsheimer. 2000. Mutation takes no vacation: can structured treatment interruptions increase the risk of drug resistant HIV-1?. Journal of Acquired Immune Deficiency Syndrome 25:398-402.
  • Y. L. Yang, G. C. Wang, K. S. Dorman, A. H. Kaplan. 1996. Long polymerase chain reaction amplification of heterogeneous HIV-1 templates produces recombination at relatively high frequency. AIDS Research and Human Retroviruses 12(4):303-306.
  • W. J. Lech, G. Wang, L. Yang, Y. Chee, K. Dorman, D. McCrae, L. C. Lazzeroni, J. W. Erickson, J. S. Sinsheimer, A. H. Kaplan. 1996. In Vivo Sequence Diversity of the Protease of the Human Immunodeficiency Virus Type 1: Presence of Protease Inhibitor Resistant Variants in Untreated Subjects. Journal of Virology 70:2038-2043.



November 8, 2017

Dr. W. Allen Miller


Department:  Plant Pathology & Microbiology


Research Overview

RNA virus replication: from plants to  humans 

We employ plant viruses as easy-to-use model systems to provide basic understanding of how viruses express genes and replicate. Because of similarities in translation and replication strategies across kingdoms, this knowledge may be relevant to major human viruses such as hepatitis A and C viruses, dengue, West Nile, and others.  At a more fundamental level, viruses are fascinating as the smallest, minimal replicating entities. They allow detailed understanding of what it takes to replicate, the essential property that defines life.

How cells decode the genetic code

We investigate the plethora of tricks by which viral messenger RNA usurps and controls the host translational machinery (translation factors and ribosomes).  We focus on the structures of viral RNA sequences that recruit host translation factors in the absence of the normally required “5’ cap” modification.  This research provides a better understanding of protein synthesis mechanisms and how the genetic code in nucleic acids is converted to amino acid sequence in functional proteins.  Who cares?   Well, this knowledge may allow us to modify viral sequences to regulate gene expression in beneficial ways, which may even contribute to the design of new anticancer drugs (stay tuned). 

Sustainable control of crop diseases and pests

Plants.  By sequencing many isolates of barley yellow dwarf and cereal yellow dwarf luteoviruses, we strive to improve the knowledge base of plant pathologists and breeders, who work to manage these viruses.  BYDV and CYDV are the most widespread and economically important viruses of wheat, barley and oats, worldwide.  They are poorly characterized and vary remarkably in sequence.  In a recently finished project, we determined complete nucleotide sequences of dozens of BYDV and CYDV isolates.  This work revealed much genome recombination and an entirely new virus, which we call Maize yellow dwarf virus.

In collaboration with Carolyn Malmstrom, Michigan State University, we are studying evolution of YDVs by sequencing viral genomes from old herbarium samples.

Aphids.  Under the umbrella of the Virus-Insect Interactions Initiative of the Iowa State University Plant Sciences Institute, we have several projects aimed at controlling aphid pests, and understanding causes of honeybee colony collapse. 

In collaboration with Prof. Bryony Bonning (Entomology Dept ISU), we are discovering the first viruses known to infect soybean aphid: a major new pest of soybeans in Iowa.

In collaboration with Prof. Bryony Bonning (Entomology Dept ISU) and Amy Toth (Evolution, Ecology and Organismal Biology Dept, ISU):

  • Surveying honeybee colonies for viruses.  So far, every colony is infected with multiple viruses.
  • Investigating the effect of honeybee nutrition and diet on susceptibility to viruses.

Recent Publications (by area)


  • Miller WA, Shen R, Staplin WR, Kanodia P (2016) Noncoding RNAs of plant viruses and viroids: sponges of host translation and RNA interference machinery.  Molec Plant-Microbe Interact  (In press).
  • Smirnova E, Firth AE, Miller WA, Scheidecker D, Brault V, Reinbold C, Rakotondrafara AM, Chung B Y-W, Ziegler-Graff V (2015) Discovery of a small non-AUG-initiated ORF in poleroviruses and luteoviruses that is required for long-distance movement.  PLoS Pathogens 11, e1004868.
  • Miller WA, Jackson J, Feng Y (2015) Cis- and trans-regulation of luteovirus gene expression by the 3' end of the viral genome. Virus Research 206, 37-45.
  • Das Sharma S, Kraft JJ, Miller WA, Goss DJ (2015) Recruitment of the 40S ribosomal subunit to the 3'-untranslted region (UTR) of a viral mRNA, via the eIF4 complex, facilitates cap-independent translation. J Biol Chem 290, 11268-11281.
  • Miras M, Sempere RN, Kraft JK, Miller WA, Aranda MA, Truniger V (2015) Determination of the Secondary Structure of an RNA fragment in Solution: Selective 2`-Hydroxyl Acylation Analyzed by Primer Extension Assay (SHAPE).  bio-protocol 5, www.bio-protocol.org/e1386.
  • Miras M, Sempere RN, Kraft JJ, Miller WA, Aranda MA, Truniger V. (2014) Interfamilial recombination between viruses led to acquisition of a novel translation enhancing RNA element that extends viral host range. New Phytologist 202, 233-246.
  • Simon AE, Miller WA (2013) 3' Cap-independent translation enhancers of plant viruses. Annual Review of Microbiology 67, 21–42.
  • Kraft JJ, Treder K, Peterson MS, Miller WA (2013) Cation-dependent folding of 3' cap-independent translation elements facilitates interaction of a 17-nucleotide conserved sequence with eIF4G. Nucleic Acids Research 41, 3398-3413


  • Carrillo-Tripp J, Bonning BC, Miller WA (2015) Challenges associated with research on RNA viruses of insects. Current Opinion in Insect Science 8, 62-68.
  • Miller WA, Carrillo-Tripp J, Bonning BC, Dolezal AG, Toth AL (2014) Conclusive evidence of replication of a plant virus in honeybees is lacking.  MBio 5, e00985-14.
  • Bonning BC, Pal N, Liu S, Wang Z, Sivakumar S, Dixon PM, King GF, Miller WA (2014) Toxin delivery by the coat protein of an aphid-vectored plant virus provides plant resistance to aphids. Nature Biotechnology 32, 102-105.

 Area of Expertise: 

  • plant virology
  • Translation
  • RNA structure


  • Ph.D., Molecular Biology, University of Wisconsin, 1984
  • B.A., Biology, Carleton College, 1978


November 15, 2017

Crystal Lu

Chaoqun (Crystal) Lu, BCB Faculty member in EEOB, will present at the BCB Faculty Seminar on Wednesday, Nov. 15, at 4:10 in 1424 MBB.  She will present on:  Simulating carbon and nitrogen cycling in the Plant-Soil-Water-Atmosphere continuum.  We hope you can join us.  Here is information on Dr. Lu's research:


Ecology, Evolution and Organismal Biology


My primary research interests are to understand and quantify human and natural controls over ecosystem patterns and processes by using a systems approach, ecosystem modeling, and data-model assimilation at various spatial and temporal scales. My current study mainly focuses on

  • Assessment of the dynamics of ecosystem function and services in response to multiple global changes in climate, land use and cover patterns, land management practices, and atmospheric composition (e.g., CO2, O3, nitrogen deposition);
  • Land-atmosphere exchange of greenhouse gases (CO2, CH4 and N2O);
  • Coupled carbon, nitrogen and water cycles in the earth system;
  • Nitrogen regulation to ecosystem productivity, crop yield, carbon sequestration capability, and water quantity and quality;
  • Land-coastal linkage and riverine export of carbon and nutrient.


Research Fellow III (May 2013-present): School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849.

Postdoctoral Fellow (May 2009-2013): School of Forestry and Wildlife Sciences, Auburn

University, Auburn, AL 36849.


“Early Career Ecologist Award” received from the Asian Ecology Section, Ecological Society of America (ESA) in 2013

“Outstanding Research Award” received in recognition of excellence in Biological and Environmental Research at Auburn University in 2008


SELECTED PEER-REVIEWED PUBLICATIONS (Citations: 1311, h-index: 21)

  • Tian. H, C. Lu, J.Yang, K. banger, D.N. Huntzinger, C.R. Schwalm, A.M. Michalak, and MsTMIP modeling group. 2015. Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions. Global Biogeochemical Cycles 29, doi:10.1002/2014GB005021.
  • Lu, C., H. Tian. 2014. Half-century nitrogen deposition increase across China: A gridded time-series data set for regional environmental assessments. Atmospheric Environment 97, 68-74. DOI: 10.1016/j.atmosenv.2014.07.061
  • Lu, C., Tian, H. 2013. Net Greenhouse Gas Balance in Response to Nitrogen Enrichment: Perspectives from a Coupled Biogeochemical Model. Global Change Biology 19, 571-588. DOI: 10.1111/gcb.12049.
  • Lu, C., H.Q. Tian, M.L. Liu, W. Ren, X.F. Xu, G.S. Chen and C. Zhang. 2012. Effects of nitrogen deposition on carbon fluxes and carbon storage in terrestrial ecosystems of China: from 1901 to 2005. Ecological Applications 22, 53–75.
  • Tian, H., C. Lu, J. Melillo, W. Ren, Y. Huang, X. Xu, M. Liu, C. Zhang, G. Chen, S. Pan, J. Liu, and J. Reilly. 2012. Food benefit and climate warming potential of nitrogen fertilizer uses in China. Environmental Research Letters 7, doi:10.1088/1748-9326/7/4/044020

Area of Expertise: 

  • Macro-systems Ecology
  • Ecosystem Modeling
  • Global Biogeochemistry


  • Ph.D., Ecology, Chinese Academy of Sciences, and Auburn Univ., 2009


BCB Faculty Seminar

Gustavo MacIntosh

BBMB Department

Wednesday, November 29, 2017

1424 MBB

4:10 p.m.

Exploring the molecular basis of new sources of resistance and the effects of gene pyramiding to control soybean aphids

Soybean aphids are one of the main pests of soybean. Current control strategies rely heavily on insecticides, but commercial varieties carrying host resistance genes are starting to be available. However, the discovery of biotypes of soybean aphids that are virulent on available resistant soybean genotypes, greatly threatens the sustainability of aphid resistance in integrated pest management systems. Thus, new sources of resistance and a better understanding of the molecular bases of these traits are needed to develop novel control strategies.

In a screening of 308 plant introduction (PI) lines selected to represent soybean diversity, we identified 15 lines with increased resistance to soybean aphids. Using genome-wide association analyses we found six new loci contributing to resistance, and identified potential candidate genes. Characterization of the type of resistance determined that five PI lines had antibiosis and antixenosis based resistance, five had antibiosis only, and two had only antixenosis. Interestingly, no direct correlation was observed between the resistant alleles in the six loci and each type of resistance, suggesting that genomic context may be important to determine the trait outcome.

We also used transcriptome analysis to assess the molecular basis of increased resistance after gene pyramiding in a soybean line carrying the Rag1 and Rag2 soybean resistance genes. We expected an additive effect manifested as a combination of the molecular changes observed in lines carrying the Rag1 or Rag2 genes individually.  However, we found that gene pyramiding leads to a synergistic interaction between resistant genes, resulting in a novel defense response in the pyramid line. Again, genomic context seems to be an important determinant of the molecular phenotype. Our findings could have important consequences for integrated management strategies based on plant host resistance.


Martha Natukunda1, Jessica Hohenstein1, Teshale Assefa2, Jiaoping Zhang2, Michelle Graham2,3, Asheesh Singh2, Gustavo MacIntosh1*

  • 1Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, USA
  • 2Department of Agronomy, Iowa State University, Ames, IA, USA
  • 3Corn Insects and Crop Genetics Research Unit, USDA-Agricultural Research Service, Ames, IA, USA