[show abstract][hide abstract] ABSTRACT: Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here.
Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed.
SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage.
The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method.
[show abstract][hide abstract] ABSTRACT: P53 is probably the most important tumor suppressor known. Over the years, information about this gene has increased dramatically. We have built a comprehensive knowledgebase of p53, which aims to facilitate wet-lab biologists to formulate their experiments and new-comers to learn whatever they need about the gene and bioinformaticians to make new discoveries through data analysis. Using the information curated, including mutation information, transcription factors, transcriptional targets, and single nucleotide polymorphisms, we have performed extensive bioinformatics analysis, and made several new discoveries about p53. We have identified point missense mutations that are over-represented in cancers, but lack of functional studies. By assessing the capability of six p53 transcriptional targets' tag SNPs selected from HapMap to capture SNPs obtained from National Institute of Environmental Health Sciences (NIEHS) Environmental Genome project and vice versa, we conclude that NIEHS data is a better source for tagSNP selections of these genes in future association studies. Analysis of microRNA regulation in the transcriptional network of the p53 gene reveals potentially important regulatory relationships between oncogenic microRNAs and transcription factors of p53. By mapping transcription factors of p53 to pathways involved in cell cycle and apoptosis, we have identified distinctive transcriptional controls of p53 in these two physiological states.