Dr. Michael Sanderson
Program Director
sanderm@email.arizona.edu

Pennie Liebig
Program Coordinator
genomics@email.arizona.edu

IGERT Program in Genomics
University of Arizona
Biosciences West. 328
1041 E. Lowell Street
Tucson, AZ 85721-0088
Tel: 520-626-0988
Fax: 520-621-9190




IGERT Recruitment Program

IGERT.org


FACULTY

In addition to the faculty listed here, faculty in other units also participate in IGERT activities. All IGERT activities are open to participation from members of any department.

Participating Faculty in Mathematical Theory and Biological Computation:

Ryan Gutenkunst
Assistant Professor, Molecular and Cellular Biology

The Gutenkunst lab is a computational lab with multiple interests. These include signal transduction, population genetics, and biochemical network evolution. In most cases, our philosophy is to build and analyze detailed biologically-realistic models. Such models typically have many free parameters, but we have developed techniques to deal with this issue.

In signal transduction, we are currently focused on the interplay between signaling and adhesion in immune cells. In collaboration with Bridget Wilson's group at the University of New Mexico, we are studying the activation of mast cells, which release histamine and thus cause allergies. We are particularly interested in the activation of these cells by bilayers presenting monovalent ligand. In this case, activation is driven by transient diffusion-induced receptor interactions, and understanding this process will have broad applications. Also in collaboration with the Wilson lab, we are building a model of crosstalk on mast cells between the activating receptor FceRI and the inhibiting receptor FcgRIIB. This interaction is a potential drug target, and we hope our models will aid in the design of effective therapies. Finally, we are studying the role of mechanical force and receptor accumulation in the activation of T cells.

In population genomics, our focus is on inferring history and natural selection from population variation data. We have developed DaDi, a fast and powerful means for inferring history from multi-population data. We are applying DaDi to data from humans and other species, and we are extending and refining the method. We are particularly interested in developing a diffusion theory for two linked genetic loci. We anticipate that this theory will provide the basis for powerful inference of the timing and strength of natural selection.

Finally, the two arms of the lab converge in studying the evolution of biochemical networks. Recently we have used detailed systems biology models and comparative genetic data to show that proteins with greater influence on network dynamics evolve more slower. We are currently refining this analysis to the domain level, and we are pursuing a number of related projects connecting systems and evolutionary biology.

John Kececioglu
IGERT Steering Committee Member
Associate Professor, Computer Science

Research in the Kececioglu lab focuses on efficient algorithms for fundamental problems in computational genomics, such as shotgun sequencing, physical mapping (Kececioglu et al. 2000), inferring evolutionary history, and multiple sequence alignment. The common theme is the design of algorithms that compute solutions of guaranteed quality and the implementation of these algorithms in useful tools for the community. Current projects include robust software for sequence analysis, multiple alignment of proteins, local alignment of genomes, and discovery of regulatory motifs. Recent results include the first practical algorithm for optimal multiple-sequence alignments (Kececioglu & Starrett 2004). The group also discovered the first efficient algorithm for inverse sequence alignment (Kececioglu & Kim 2006). This work is an example of a fruitful interplay between computer science and genomics: while the algorithm was directly motivated by questions of sequence alignment, it also solves the general problem of inverse parametric optimization for a very broad class of problems in computer science.

Joanna Masel
Associate Professor, Ecology and Evolutionary Biology

I am a theoretical or mathematical biologist. The fields I work in are very diverse. They tend to involve complex systems far from equilibrium, whose emergent properties are not immediately obvious from their component parts.

One question I find interesting is how the rate of evolution can itself evolve. Systems known as evolutionary capacitors are able to store variation in a latent form, releasing it only when necessary. The yeast prion [PSI+] is a good example of an evolutionary capacitor. I use both theoretical population genetics and bioinformatics / comparative genomics to study evolutionary capacitance. For example, why is latent variation not packed full of lethal alleles? The theoretical answer is that latent variation is only 95% latent not 100% latent, and this is enough to screen out most lethal alleles. The comparative genomic approach is then to look for evidence for that 5% expression, for example in the 3'UTR sequences revealed by [PSI+]. I analyze how specific capacitor systems have evolved in the past, predict how capacitance properties should evolve in general, and study how capacitance affects the overall rate of evolution.

In collaboration with Mark Siegal at NYU, I also model networks of transcriptional regulators in order to study the evolutionary properties of canalization (also known as robustness) and genetic assimilation. Robustness that is the product of evolution can have very different properties to robustness that is the product of an engineering process. Complex interacting networks can also act as evolutionary capacitors by concealing and revealing variation. We are constructing a model of gene networks that is both realistic enough to be related to data and simple enough for experimental evolution to be rapidly simulated. This tool will open up a range of questions relating the evolution of robustness to the stochasticity associated with small numbers of molecules in cells.

Another interest is how prions, which lack any DNA or RNA, are able to replicate. The incubation period of prion diseases is incredibly precise, leading to high quality in vivo data. I develop mathematical models of prion replication and compare them to data on prion incubation times. This allows us to study how prions replicate and how best to interfere with this replication.



Sudha Ram
Professor, Management Information Systems

Ram’s group (Advanced Database Research Group) works on database integration, semantic modeling in bioinformatics, provenance management and cyberinfrastructure for plant biology. Recent research focuses on developing techniques to identify and resolve semantic conflicts among diverse databases, understanding pedigree and provenance of heterogeneous databases (Liu and Ram 2010,), using ontologies for biological database integration (Ram & Wei, 2004a, 2004b, 2005), and web/data analytics (Ram and Liu, 2009, Ram & Wei, 2010). Ram’s research involves interdisciplinary collaborations with researchers in plant sciences, ecology and evolutionary biology, hydrology, and oceanography, and geography.



Michael Sanderson
IGERT Program Director
Professor, Ecology and Evolutionary Biology

Sanderson’s research is aimed at developing algorithms and software for assembling data from the large sequence databases, such as GenBank, for the purpose of building comprehensive phylogenetic trees. Sanderson is currently developing tools and techniques for acquiring sequence data and assembling it in a pre-processing pipeline for later phylogenetic inference. He is collaborating with computer scientists and other phylogeneticists to develop and test algorithms for datasets ranging from broad collections across sizeable parts of the tree of life (Driskell et al. 2004; McMahon & Sanderson 2006) to large EST data sets on fewer taxa (Sanderson & McMahon 2006). Analysis of data at these extremes requires novel inference methods such as supertree construction (Burleigh et al. 2006).

 



Bruce Walsh
Professor, Ecology and Evolutionary Biology

The Walsh lab is interested in the interface between quantitative genetics and genomics, focusing on issues such as genome evolution, the analysis of complex genetic data sets, whole-genome scans for linkage and selection (Walsh 2006), and expression array analysis (Walsh & Henderson 2004). Walsh and collaborators are also interested in evolutionary and practical applications of quantitative genetics (Walsh 2005).

Xiangfeng Bryan Wang
Assistant Professor, School of Plant Sciences

We are developing bioinformatic tools and resources to help understand the epigenomic regulatory mechanisms that function during early maize endosperm development. The recent completion of the maize genome has facilitated understanding the epigenetic regulation of endosperm development and the molecular mechanisms underlying gene imprinting at a genomic level. Although application of next-generation sequencing technology for epigenome and transcriptome profiling has allowed accumulation of significant amount of sequence data, bioinformatic approaches are still needed to properly analyze these large datasets. Three main objectives of the ongoing projects in our Lab are to develop computational tools and resources to: 1) Improve maize gene models using active transcription-associated histone modifications, 2) Develop algorithms to screen for core TFs and build regulatory networks using nucleosome-positioning dynamics, and 3) Identify epigenetically modified, imprinted genes at the genome level. Our work will fundamentally advance our understanding of transcriptional and epigenetic regulation, genomic imprinting, and the molecular mechanisms involved in maize endosperm development.

Joseph Watkins
Professor, Mathematics

The Watkins lab is interested in the probability theory and stochastic processes, particularly limit theorems and models of random processes in biology and physics. Examples of research projects:

1) Collaboration with Steve Lansing and Michael Hammer on Austronesian Societies: reading social structure from the genome.
2) Models for moderate sized structured populations with applications to flour beetles.
3) Queen development times and the Africanization of the honey bee population and on the dynamics of swarms with the Carl Hayden Bee Research Center. We are also examining the interaction of honey bees and native bees.




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