evolution
of gene regulation and developmental systems
A key property of living objects is that each object, whether they are proteins, cells, or whole organisms, has an associated generating process?that is, a decoding process whereby stored information is converted into a complex functioning biological object. For example, generating a protein involves translation and folding; generating an organism involves a cascade of gene regulatory and cell biological processes. We are interested in such ?bio-generative processes? and understanding general principles of these processes. Questions include how to infer the organizational structure of such generative processes from available data, how do the generative processes evolve, and how the generative processes and selection on the generative processes affect the final form of the biological object. Currently, we have three related projects in this area.
Whole-genome gene expression regulation and evolution of the
transcriptome
Recently, it has become possible to obtain the transcriptional
profiles of the entire genome. We are interested in asking how
we can deduce
the molecular interaction of the genes from such transcriptional
profiles and whether there are organizational regularities to
the structure of molecular interactions. In particular, we are
interested in large-scale properties such as the organization
of connectivity (i.e., how many genes interact with a particular
gene), the modularity of the interactions, and dimensionality
of gene expression (i.e., the degrees of freedom in coordinated
gene expression). We are also interested in the evolution of
the transcriptome. In this area, we are collaborating the Kevin
White at Yale University to study the macro-evolution and mutational
dynamics of the transcriptome in Drosophila species. We have
generated comparative expression data for six lineages of Drosophila
and for mutation accumulation lines.
We are interested in the
tempo and mode of evolution of gene expression, the mutational
effects on gene expression, and changes in functional importance
of gene expression through a developmental trajectory.
Dynamics of whole-genome gene expression
Transcriptional regulation is a dynamic process and a transcriptional
profile has a naturally associated temporal dimension. We have
been
developing computational tools and laboratory experiments to
understand
the dynamics of the whole genome gene expression regulation.
We recently developed tools for visualizing and analyzing a
transcriptome time-series under periodic events such as the cell cycle. With Paul Sniegowski at Penn we are now generating time-series data from natural strains of yeast. We are pursuing the idea that a comparison of changes in the dynamics of gene expression (so-called heterochrony) will allow us to efficiently infer modular co-regulated gene groups involved in generating the cell cycle.
Protein structure evolution
Proteins are generated by a process of translation and statistical
mechanical folding. Proteins also display macroevolutionary diversity
in
shape and function raising the question whether such diversity
is due to chance, molecular function (e.g., particular catalytic
activity), or constraints in the folding process. Here we are
interested in the contribution of the folding process to the
final form. In particular, we are statistically characterizing
the regularity of form?which we define as the presence of self-similar substructures, and relating this regularity to the prevalence of particular structures in nature.
We have found statistical evidence that common protein structures,
so-called superfolds, are more self-similar. We hypothesize that
this self-similarity is due to selection on efficient folding.
We are currently attempting address whether more self-similar
structures are more efficient and robust in their folding properties.
computational infrastructure for deducing the tree of life
Because
biological organization is fundamentally based on a bifurcating
descent-with-modification process, the solution to the problem
of phylogenetic estimation is extremely important to a wide variety
of basic and applied biological problems. Recently, the introduction
of molecular techniques has made available tremendous amount
of data for phylogenetic analysis such that several NSF initiatives
have been generated to elucidate the phylogenetic tree of all
Life. In this area we are concentrating on the following problems:
Investigating the statistical properties of phylogenetic estimators
We
are interested in understanding the large-sample and finite sample
behavior of phylogenetic estimators especially with respect
to their scaling properties both in problem size and data size.
We have developed several geometrical analysis techniques using
the idea of imbedded joint probability space. We are also working
on developing new estimation principles. For example, we have
pioneered the idea of using computational algebraic geometry
techniques for estimating evolutionary trees. Algebraic geometry
techniques can be used to extract invariant functions of the
joint probability distribution of character states that can be
used as efficient direct estimators of phylogeny. More recently
we have been experimenting with using penalized likelihood methods
to connect several distinct classes of phylogenetic estimators
into a single family.
Establishing a simulated data set for validating
phylogenetic estimators
One of the fundamental obstacles in developing phylogenetic estimation
algorithms is the lack of an agreed standard for performance
evaluations. Typical evaluation studies are ad hoc and use procedures
that are difficult to replicate. We are part of a NSF funded
project called National Resource for Phyloinformatics and Computational
Phylogenomics. As part of this group, we are developing a suite
of simulated data using a range of models of evolution from simple
models of mutational changes to whole genome evolution. We are
also developing a suite of statistical performance evaluation
tools. The development of the simulated data set and performance
evaluation methods will lead to a unified assessment of phylogenetic
estimation algorithms.
Mining the existing database for phylogenetic
information
The current databases such as Genbank contain a wealth
of information for phylogenetic estimation. For example, in 2002,
Genbank contained
information from ~125,000 organisms. However, this data has been
collected in a haphazard manner, which leads to the interesting
problem of how we might most efficiently use this data. The computational
problems here include obtaining maximal subsets of informative
data, using fragmented datasets to generate a “super” tree
that maximally cover available organisms, and illuminating bottlenecks
in the available data. We are part of a NSF funded “Assembling
the Tree of Life” project that will address these computational
problems.
comparative genomics and molecular evolution
We are engaged in
various projects involving comparative genomics and molecular
evolution. These projects are mostly in collaboration
with other labs around the country. The projects include
developing high-throughput methods for rapid identification
and phylogenetic
placement of novel organisms, functional annotation of
genomic sequences for molecules involved in synaptic transmission,
molecular evolution of the olfactory receptor families,
and
elucidation of novel components in the osteoclast maturation
pathway. Part of this activity is funded by a NIH planning
grant for National Program of Excellence in Biomedical Computing.
selected
publications
Rifkin, S. A., Houle, D., Kim, J. and White, K.P. 2005. A mutation accumulation
assay reveals extensive capacity for rapid gene expression evolution.
Nature, 438: 220-223.
Ge, F., Wang, L.S., Kim, J. 2005. Cobweb
of life revealed by genome-scale estimates of horizontal gene
transfer. PLOS Biology, 3(10):e316.
Hadley, D., Murphy T., Valladares,
O., Hannenhalli, S., Ungar, L., Kim, J. and Bucan,
M. 2006. Patterns of sequence conservation in
presynaptic neural genes.
Genome Biology 7:R105.
Magwene, P. M., Lizardi, P., and J. Kim. 2003. Reconstructing
the temporal order of biological samples using microarray data. Bioinformatics
19(7):842-850.
Rifkin, S. A., J. Kim, and K. P. White. 2003. Evolution
of gene-expression during metamorphosis in the Drosophila melanogaster
subgroup. Nature Genetics 33(2):138-144.
Rifin, S.A. and Kim, J. 2002. Geometry of gene expression
dynamics. Bioinformatics 18:1176-1183.
Kim, J. Computers are from Mars, Organisms are from
Venus: An interrelationship guide to Biology and Computer Science. IEEE
Computer, July 2002.
Aspnes, J., M.-Y. Kao, J. Kim. J. Kreychman, G. Shah.
2002. A Combinatorial Toolbox for Protein Landscapes in the Grand Canonical
Model. J. Comp. Biol. 9(5):721-742.
Kim, J. 2001. Macroevolution of the hairy enhancer
in Drosophila species. J. Exp. Zool. (Mol. Dev. Evol.) 291:175-185.
Kim, J., E. Moriyama, C. G. Warr, P. J. Clyne, and
J. R. Carlson. 2000. Identification of multi-transmembrane proteins
from genomic databases using quasi-periodic structural properties. Bioinformatics
16:767-775.
Kim, J. 2000. Slicing hyperdimensional oranges: The
geometry of phylogenetic estimation. Mol. Phyl. Evol. 17(1): 58-75.
Sanderson, M. J. and J. Kim. 2000. Parametric phylogeny
estimation?. Syst. Biol. 49:817-829.
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