Biomedical subjects

Mark P Brynildsen

Publications and source records attributed to Mark P Brynildsen.

4 recordsLinked to original sources

PubMed2006

A Gibbs sampler for the identification of gene expression and network connectivity consistency.

MOTIVATION: Data from DNA microarrays and ChIP-chip binding assays often form the basis of transcriptional regulatory analyses. However, experimental noise in both data types combined with environmental dependence and uncorrelation between binding and regulation in ChIP-chip binding data complicate analyses that utilize these complimentary data sources. Therefore, to minimize the impact of these inaccuracies on transcription analyses it is desirable to identify instances of gene expression-ChIP-chip agreement, under the premise that inaccuracies are less likely to be present when separate data sources corroborate each other. Current methods for such identification either make key assumptions that limit their applicability and/or yield high false positive and false negative rates. The goal of this work was to develop a method with a minimal amount of assumptions, and thus widely applicable, that can identify agreement between gene expression and ChIP-chip data at a higher confidence level than current methods. RESULTS: We demonstrate in Saccharomyces cerevisiae that currently available ChIP-chip binding data explain microarray data from a variety of environments only as well as randomized networks with the same connectivity density. This suggests a high degree of inconsistency between the two data types and illustrates the need for a method that can identify consistency between the two data sources. Here we have developed a Gibbs sampling technique to identify genes whose expression and ChIP-chip binding data are mutually consistent. Compared to current methods that could perform the same task, the Gibbs sampling method developed here exceeds their ability at high levels (>50%) of transcription network and gene expression error, while performing similarly at lower levels. Using this technique, we show that on average 73% more gene expression features can be captured per gene as compared to the unfiltered use of gene expression and ChIP-chip-derived network connectivity data. It is important to note that the method described here can be generalized to other transcription connectivity data (e.g. sequence analysis, etc.). AVAILABILITY: Our algorithm is available on request from the authors and soon to be posted on the web. See author's homepage for details, http://www.seas.ucla.edu/~liaoj/

Algorithms↗

PubMed2006

Versatility and connectivity efficiency of bipartite transcription networks.

The modulation of promoter activity by DNA-binding transcription regulators forms a bipartite network between the regulators and genes, in which a smaller number of regulators control a much lager number of genes. To facilitate representation of gene expression data with the simplest possible network structure, we have characterized the ability of bipartite networks to describe data. This has led to the classification of two types of bipartite networks, versatile and nonversatile. Versatile networks can describe any data of the same rank, and are indistinguishable from one another. Nonversatile networks require constraints to be present in data they describe, which may be used to distinguish between different network topologies. By quantifying the ability of bipartite networks to represent data we were able to define connectivity efficiency, which is a measure of how economic the use of connections is within a network with respect to data representation and generation. We postulated that it may be desirable for an organism to maximize its gene expression range per network edge, since development of a regulatory connection may have some evolutionary cost. We found that the transcriptional regulatory networks of both Saccharomyces cerevisiae and Escherichia coli lie close to their respective connectivity efficiency maxima, suggesting that connectivity efficiency may have some evolutionary influence.

Escherichia coli Proteins↗

PubMed2005

Inferring yeast cell cycle regulators and interactions using transcription factor activities.

BACKGROUND: Since transcription factors are often regulated at the post-transcriptional level, their activities, rather than expression levels may provide valuable information for investigating functions and their interactions. The recently developed Network Component Analysis (NCA) and its generalized form (gNCA) provide a robust framework for deducing the transcription factor activities (TFAs) from various types of DNA microarray data and transcription factor-gene connectivity. The goal of this work is to demonstrate the utility of TFAs in inferring transcription factor functions and interactions in Saccharomyces cerevisiae cell cycle regulation. RESULTS: Using gNCA, we determined 74 TFAs from both wild type and fkh1 fkh2 deletion mutant microarray data encompassing 1529 ORFs. We hypothesized that transcription factors participating in the cell cycle regulation exhibit cyclic activity profiles. This hypothesis was supported by the TFA profiles of known cell cycle factors and was used as a basis to uncover other potential cell cycle factors. By combining the results from both cluster analysis and periodicity analysis, we recovered nearly 90% of the known cell cycle regulators, and identified 5 putative cell cycle-related transcription factors (Dal81, Hap2, Hir2, Mss11, and Rlm1). In addition, by analyzing expression data from transcription factor knockout strains, we determined 3 verified (Ace2, Ndd1, and Swi5) and 4 putative interaction partners (Cha4, Hap2, Fhl1, and Rts2) of the forkhead transcription factors. Sensitivity of TFAs to connectivity errors was determined to provide confidence level of these predictions. CONCLUSION: By subjecting TFA profiles to analyses based upon physiological signatures we were able to identify cell cycle related transcription factors consistent with current literature, transcription factors with potential cell cycle dependent roles, and interactions between transcription factors.

Cell Cycle↗

PubMed2005

gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation.

Network Component Analysis (NCA) is a network structure-driven framework for deducing regulatory signal dynamics. In contrast to classical approaches such as principal component analysis or independent component analysis, NCA makes use of the connectivity structure from transcriptional regulatory networks to restrict the decomposition to a unique solution. However, the existing version of NCA cannot incorporate information beyond the network topology such as information obtained from regulatory gene knockouts that constrain the dynamics of regulatory signals. The ability of incorporating such information enables a more accurate and self-consistent analysis over different experiments and extends NCA to systems that may not satisfy the identifiability criteria of NCA. In this paper, we derive a generalized form of NCA, gNCA, which significantly expands the capability of transcription network analysis by incorporating regulatory signal constraints arising from genetic knockouts. The theoretical bases including criteria for uniqueness of solution and distinguishability between networks are derived. In addition, numerical techniques for robust decomposition are discussed. gNCA is then demonstrated using an Escherichia coli wild-type strain and an isogenic arcA deletion mutant during a carbon source transition.

Bacterial Outer Membrane Proteins↗