Google Scholar Profile
I am an assistant professor at the departments of Microbiology and Computer Science and Software Engineering at Miami University, Oxford, Ohio.
I am interested in large scale analyses of proteins, genomes and metagenomes.
Proteins facilitate virtually all of life's processes: from the immune system to
embryonic development, from metabolism to cell division, proteins act by binding to
other molecules, serving as structural elements of organisms, catalyzing reactions, and
regulating processes on various levels. On the one hand, Protein structures are diverse
and complex, attesting to the many roles these molecules play in facilitating life. On
the other, there are intriguing commonalities even among the most different proteins.
Most importantly, we are inundated with data coming at us from genomics
projects, which in many cases provides us with little real information as to what
these proteins do. All this presents the computational biologist with unique challenges
I am 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.
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.
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 new and
rapidly growing research field in bioinformatics .
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. A short article on AFP 2006.
My personal interest in function prediction lies in predicting function from protein structure . I have recently started work on predicting
gene function based on its genomic context in bacteria. I am using both
genomic and metagenomic data towards that end.
Metagenomics is a new field defined as 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.
Together with Rachael
Morgan-Kiss's lab, we are studying the microbial communities isolated from
Antarctic lakes. I am also studying how metabolic pathways evolve using the
comparative genomic opportunities offered by environmental genomic data
We are also interested in the impact of microbial communities on the human
body. We have helped developed a method to study the corelation 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 various human diseases.
My CV. (updated: March, 2014)