Biomedical subjects

Ker-Chau Li

Publications and source records attributed to Ker-Chau Li.

4 recordsLinked to original sources

PubMed2005

Inference of transcriptional regulatory network by two-stage constrained space factor analysis.

MOTIVATION: Microarray gene expression and cross-linking chromatin immunoprecipitation data contain voluminous information that can help the identification of transcriptional regulatory networks at the full genome scale. Such high-throughput data are noisy however. In contrast, from the biomedical literature, we can find many evidenced transcription factor (TF)-target gene binding relationships that have been elucidated at the molecular level. But such sporadically generated knowledge only offers glimpses on limited patches of the network. How to incorporate this valuable knowledge resource to build more reliable network models remains a question. RESULTS: We present a modified factor analysis approach. Our algorithm starts with the evidenced TF-gene linkages. It iterates between the network configuration estimation step and the connection strength estimation step, using the high-throughput data, till convergence. We report two comprehensive regulatory networks obtained for Saccharomyces cerevisiae, one under the normal growth condition and the other under the environmental stress condition. SUPPLEMENTARY INFORMATION: http://kiefer.stat.ucla.edu/lap2/download/bti656_supplement.pdf.

Algorithms↗

PubMed2005

Study of coordinative gene expression at the biological process level.

MOTIVATION: Cellular processes are not isolated groups of events. Nevertheless, in most microarray analyses, they tend to be treated as standalone units. To shed light on how various parts of the interlocked biological processes are coordinated at the transcription level, there is a need to study the between-unit expressional relationship directly. RESULTS: We approach this issue by constructing an index of correlation function to convey the global pattern of coexpression between genes from one process and genes from the entire genome. Processes with similar signatures are then identified and projected to a process-to-process association graph. This top-down method allows for detailed gene-level analysis between linked processes to follow up. Using the cell-cycle gene-expression profiles for Saccharomyces cerevisiae, we report well-organized networks of biological processes that would be difficult to find otherwise. Using another dataset, we report a sharply different network structure featuring cellular responses under environmental stress. SUPPLEMENTARY INFORMATION: http://kiefer.stat.ucla.edu/lap2/download/KL_supplement.pdf.

Algorithms↗

PubMed2004

A system for enhancing genome-wide coexpression dynamics study.

Statistical similarity analysis has been instrumental in elucidation of the voluminous microarray data. Genes with correlated expression profiles tend to be functionally associated. However, the majority of functionally associated genes turn out to be uncorrelated. One conceivable reason is that the expression of a gene can be sensitively dependent on the often-varying cellular state. The intrinsic state change has to be plastically accommodated by gene-regulatory mechanisms. To capture such dynamic coexpression between genes, a concept termed "liquid association" (LA) has been introduced recently. LA offers a scoring system to guide a genome-wide search for critical cellular players that may interfere with the coexpression of a pair of genes, thereby weakening their overall correlation. Although the LA method works in many cases, a direct extension to more than two genes is hindered by the "curse of dimensionality." Here we introduce a strategy of finding an informative 2D projection to generalize LA for multiple genes. A web site is constructed that performs on-line LA computation for any user-specified group of genes. We apply this scoring system to study yeast protein complexes by using the Saccharomyces cerevisiae protein complexes database of the Munich Information Center for Protein Sequences. Human genes are also investigated by profiling of 60 cancer cell lines of the National Cancer Institute. In particular, our system links the expression of the Alzheimer's disease hallmark gene APP (amyloid-beta precursor protein) to the beta-site-cleaving enzymes BACE and BACE2, the gamma-site-cleaving enzymes presenilin 1 and 2, apolipoprotein E, and other Alzheimer's disease-related genes.

Alzheimer Disease↗

PubMed2002

Genome-wide coexpression dynamics: theory and application.

High-throughput expression profiling enables the global study of gene activities. Genes with positively correlated expression profiles are likely to encode functionally related proteins. However, all biological processes are interlocked, and each protein may play multiple cellular roles. Thus the coexpression of any two functionally related genes may depend on the constantly varying, yet often-unknown cellular state. To initiate a systematic study on this issue, a theory of coexpression dynamics is presented. This theory is used to rationalize a strategy of conducting a genome-wide search for the most critical cellular players that may affect the coexpression pattern of any two genes. In one example, using a yeast data set, our method reveals how the enzymes associated with the urea cycle are expressed to ensure proper mass flow of the involved metabolites. The correlation between ARG2 and CAR2 is found to change from positive to negative as the expression level of CPA2 increases. This delicate interplay in correlation signifies a remarkable control on the influx and efflux of ornithine and reflects well the intrinsic cellular demand for arginine. In addition to the urea cycle, our examples include SCH9 and CYR1 (both implicated in a recent longevity study), cytochrome c1 (mitochondrial electron transport), calmodulin (main calcium-binding protein), PFK1 and PFK2 (glycolysis), and two genes, ECM1 and YNL101W, the functions of which are newly revealed. The complexity in computation is eased by a new result from mathematical statistics.

Calmodulin↗