Biomedical subjects

Linyong Mao

Publications and source records attributed to Linyong Mao.

4 recordsLinked to original sources

PubMed2006

Combining comparative genomics with de novo motif discovery to identify human transcription factor DNA-binding motifs.

BACKGROUND: As more and more genomes are sequenced, comparative genomics approaches provide a methodology for identifying conserved regulatory elements that may be involved in gene regulations. RESULTS: We developed a novel method to combine comparative genomics with de novo motif discovery to identify human transcription factor binding motifs that are overrepresented and conserved in the upstream regions of a set of co-regulated genes. The method is validated by analyzing a well-characterized muscle specific gene set, and the results showed that our approach performed better than the existing programs in terms of sensitivity and prediction rate. CONCLUSION: The newly developed method can be used to extract regulatory signals in co-regulated genes, which can be derived from the microarray clustering analysis.

Amino Acid Motifs↗

PubMed2005

Combining microarray and genomic data to predict DNA binding motifs.

The ability to detect regulatory elements within genome sequences is important in understanding how gene expression is controlled in biological systems. In this work, microarray data analysis is combined with genome sequence analysis to predict DNA sequences in the photosynthetic bacterium Rhodobacter sphaeroides that bind the regulators PrrA, PpsR and FnrL. These predictions were made by using hierarchical clustering to detect genes that share similar expression patterns. The DNA sequences upstream of these genes were then searched for possible transcription factor recognition motifs that may be involved in their co-regulation. The approach used promises to be widely applicable for the prediction of cis-acting DNA binding elements. Using this method the authors were independently able to detect and extend the previously described consensus sequences that have been suggested to bind FnrL and PpsR. In addition, sequences that may be recognized by the global regulator PrrA were predicted. The results support the earlier suggestions that the DNA binding sequence of PrrA may have a variable-sized gap between its conserved block elements. Using the predicted DNA binding sequences, a whole-genome-scale analysis was performed to determine the relative importance of the interplay between the three regulators PpsR, FnrL and PrrA. Results of this analysis showed that, compared to the regulation by PpsR and FnrL, a much larger number of genes are candidates to be regulated by PrrA. The study demonstrates by example that integration of multiple data types can be a powerful approach for inferring transcriptional regulatory patterns in microbial systems, and it allowed the detection of photosynthesis-related regulatory patterns in R. sphaeroides.

Bacterial Proteins↗

PubMed2004

Probabilistic representation of gene regulatory networks.

MOTIVATION: Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. RESULTS: In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. AVAILABILITY: The simulation software is available upon request. SUPPLEMENTARY INFORMATION: Supplementary material will be made available on the OUP server.

Algorithms↗

PubMedDate not supplied

Simple lattice simulation of chiral discrimination in monolayers.

A simulation method based on cellular automata on a hexagonal lattice is applied to model the behavior of chiral molecules on a surface, such as those in a monolayer of amphiphiles each having a single chiral center. The simulation method includes movement and orientation rules, with the objects ("molecules") on the lattice vertices possessing three different groups with one group pointing along each edge. Periodic boundary conditions are employed in the simulations. Interaction strengths are calculated between pairs of groups occupying the edges between vertices and summed for the entire system. The model successfully reproduces the formation of domains as a consequence of the movement rule. The movement rule can be adjusted to simulate homochiral discrimination or heterochiral discrimination for the case of racemic mixtures. The orientation rule results in a preference for orientations of the molecules that minimize the total interaction strength.

Journal Article↗