Dr. Michael Sanderson
IGERT Program in Genomics
University of Arizona
Biosciences West. 328
1041 E. Lowell Street
Tucson, AZ 85721-0088
Participating Faculty in Mathematical Theory and Biological Computation:
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.
| 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.
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.
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).
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.
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: