Biomedical subjects

Junhyong Kim

Publications and source records attributed to Junhyong Kim.

5 recordsLinked to original sources

PubMed2003

Unwrapping glial biology: Gcm target genes regulating glial development, diversification, and function.

Glia are the most abundant cell type in the mammalian brain. They regulate neuronal development and function, CNS immune surveillance, and stem cell biology, yet we know surprisingly little about glia in any organism. Here we identify over 40 new Drosophila glial genes. We use glial cells missing (gcm) mutants and misexpression to verify they are Gcm regulated in vivo. Many genes show unique spatiotemporal responsiveness to Gcm in the CNS, and thus glial subtype diversification requires spatially or temporally restricted Gcm cofactors. These genes provide insights into glial biology: we show unc-5 (a repulsive netrin receptor) orients glial migrations and the draper gene mediates glial engulfment of apoptotic neurons and larval locomotion. Many identified Drosophila glial genes have homologs expressed in mammalian glia, revealing conserved molecular features of glial cells. 80% of these Drosophila glial genes have mammalian homologs; these are now excellent candidates for regulating human glial development, function, or disease.

Animals↗

PubMed2003

Reconstructing the temporal ordering of biological samples using microarray data.

MOTIVATION: Accurate time series for biological processes are difficult to estimate due to problems of synchronization, temporal sampling and rate heterogeneity. Methods are needed that can utilize multi-dimensional data, such as those resulting from DNA microarray experiments, in order to reconstruct time series from unordered or poorly ordered sets of observations. RESULTS: We present a set of algorithms for estimating temporal orderings from unordered sets of sample elements. The techniques we describe are based on modifications of a minimum-spanning tree calculated from a weighted, undirected graph. We demonstrate the efficacy of our approach by applying these techniques to an artificial data set as well as several gene expression data sets derived from DNA microarray experiments. In addition to estimating orderings, the techniques we describe also provide useful heuristics for assessing relevant properties of sample datasets such as noise and sampling intensity, and we show how a data structure called a PQ-tree can be used to represent uncertainty in a reconstructed ordering. AVAILABILITY: Academic implementations of the ordering algorithms are available as source code (in the programming language Python) on our web site, along with documentation on their use. The artificial 'jelly roll' data set upon which the algorithm was tested is also available from this web site. The publicly available gene expression data may be found at http://genome-www.stanford.edu/cellcycle/ and http://caulobacter.stanford.edu/CellCycle/.

Algorithms↗

PubMed2003

Evolution of gene expression in the Drosophila melanogaster subgroup.

Little is known about broad patterns of variation and evolution of gene expression during any developmental process. Here we investigate variation in genome-wide gene expression among Drosophila simulans, Drosophila yakuba and four strains of Drosophila melanogaster during a major developmental transition--the start of metamorphosis. Differences in gene activity between these lineages follow a phylogenetic pattern, and 27% of all of the genes in these genomes differ in their developmental gene expression between at least two strains or species. We identify, on a gene-by-gene basis, the evolutionary forces that shape this variation and show that, both within the transcriptional network that controls metamorphosis and across the whole genome, the expression changes of transcription factor genes are relatively stable, whereas those of their downstream targets are more likely to have evolved. Our results demonstrate extensive evolution of developmental gene expression among closely related species.

Algorithms↗

PubMed2002

Gene discovery by e-genetics: Drosophila odor and taste receptors.

A new algorithm that examines DNA databases for proteins that have a particular structure, as opposed to a particular sequence, represents a novel 'e-genetics' approach to gene discovery. The algorithm has successfully identified new G-protein-coupled receptors, which have a characteristic seven-transmembrane-domain structure, from the Drosophila genome database. In particular, it has revealed novel families of odor receptors and taste receptors, which had long eluded identification by other means. The two new gene families, the Or and Gr genes, are expressed in neurons of olfactory and taste sensilla and are highly divergent from all other known G-protein-coupled receptor genes. Modification of the algorithm should allow identification of other classes of multitransmembrane-domain protein.

Algorithms↗

PubMed2002

A combinatorial toolbox for protein sequence design and landscape analysis in the grand canonical model.

In modern biology, one of the most important research problems is to understand how protein sequences fold into their native 3D structures. To investigate this problem at a high level, one wishes to analyze the protein landscapes, i.e., the structures of the space of all protein sequences and their native 3D structures. Perhaps the most basic computational problem at this level is to take a target 3D structure as input and design a fittest protein sequence with respect to one or more fitness functions of the target 3D structure. We develop a toolbox of combinatorial techniques for protein landscape analysis in the Grand Canonical model of Sun, Brem, Chan, and Dill. The toolbox is based on linear programming, network flow, and a linear-size representation of all minimum cuts of a network. It not only substantially expands the network flow technique for protein sequence design in Kleinberg's seminal work but also is applicable to a considerably broader collection of computational problems than those considered by Kleinberg. We have used this toolbox to obtain a number of efficient algorithms and hardness results. We have further used the algorithms to analyze 3D structures drawn from the Protein Data Bank and have discovered some novel relationships between such native 3D structures and the Grand Canonical model.

Algorithms↗