Catálogo de publicaciones - libros

For studying gene regulatory and protein signaling networks, we have developed an interactive map for the genome. We first represent genes, proteins and their regulatory networks in a relational database. Known regulatory networks of the genome in the PubMed literatures are extracted by a manual and later a semi-automated text-mining method. Then a graphical interface displays these networks upon the query of specific genes, proteins or subsystems (i.e., groups of genes or proteins) on these networks. The interactive map has another capability of browsing those networks. The method can be extended to any other genome. Our objective is to develop this interactive map for the community so that new research results may be ingested into the database, while annotations may be developed incrementally on the existing regulatory elements. Eventually some standards might be necessary for a long-term modeling and compilation of regulatory networks.

The Pathalyzer is a program for analyzing large-scale signal transduction networks. Reactions and their substrates and products are represented as transitions and places in a safe Petri net. The user can interactively specify goal states, such as activation of a particular protein in a particular cell site, and the system will automatically find and display a pathway that results in the goal state – if possible. The user can also require that the pathway be generated without using certian proteins. The system can also find all individual places and all pairs of places which, if knocked out, would prevent the goals from being achieved. The tool is intended to be used by biologists with no significant understanding of Petri nets or any of the other concepts used in the implementation.

We propose a new method for identifying and representing overlapping within a protein interaction network. We develop a graph-theoretical framework that enables automatic construction of such representation. The proposed representation helps in understanding the transitions between functional groups and allows for tracking a protein’s path through a cascade of functional groups. Therefore, depending on the nature of the network, our representation is capable of elucidating temporal relations between functional groups. We illustrate the effectiveness of our method by applying it to TNF/NF-B and pheromone signaling pathways.

Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing protein interaction networks for various species at an ever increasing pace. However, common technologies like yeast two-hybrid can experience high rates of false positive detection. To combat these errors, many methods have been developed which associate confidence scores with each interaction. Here we perform the first comparative analysis and performance assessment among these different methods using the fact that interacting proteins have similar biological attributes such as function, expression, and evolutionary conservation. We also introduce a new measure, the signal to noise ratio of protein complexes embedded in each network, to assess the quality of the different methods. We observe that utilizing any probability scheme is always more beneficial than assuming all observed interactions to be real. Also, schemes that assign probabilities to individual interactions generally perform better than those assessing the reliability of a set of interactions obtained from an experiment or a database.

This work addresses a central question in medical genetics – the distinction between disease-causing SNPs and neutral variations. Unlike previous studies that focused mainly on coding SNPs, our efforts were centered around variations in regulatory regions and specifically within transcription factor (TF) binding sites. We have compiled a comprehensive collection of genome wide TF binding sites and developed computational measures to estimate the effects of binding site variations on the expression profiles of the regulated genes. Applying these measures to binding sites of known TFs, we were able to make predictions that were in line with published experimental evidence and with structural data on DNA-protein interactions. We attempted to generalize the properties of expression-altering substitutions by accumulating statistics from many substitutions across multiple binding sites. We found that in the yeast genome substitutions that abolish a G or a C are on average more severe than substitutions that abolish an A or a T. This may be attributed to the low GC content of the yeast genome, in which G and C may be important for conferring specificity. We found additional factors that are correlated with the severity of a substitution. Such factors can be integrated in order to create a set of rules for the prioritization of regulatory SNPs according to their disease-causing potential.

Many years of experimental and computational molecular biology of model organisms such as and has elucidated the gene regulatory network in these organisms. Relatively little is known about gene regulation in species other than the model organisms, whether gene regulatory networks are conserved, and to what degree our knowledge based on model organisms reflects biological networks occurring in nature as a whole.

In this paper, we describe a first attempt to understand the gene regulatory network in lesser-known organisms, using our knowledge of gene regulation in a well-understood model organism. Such an extrapolation is particularly valuable in the study of disease-causing infectious agents, as well as other organisms that are difficult to grow or handle in a laboratory environment. In addition, comparative systems biology can identify which parts of biological networks are poorly understood and are therefore promising venues for further experimental research.

We analyze the gene regulatory network responsible for the initiation of sporulation in fourteen target organisms, using as the model organism. Instead of focusing on individual transcription factor binding sites, we devise a scoring function that takes into account the effect of multiple transcription factors binding to the regulatory region. Whereas the core gene regulatory network appears to be conserved, the degree of conservation decreases rapidly for more remote organisms, as well as for regulatory relations in the periphery of the network. Our work shows that gene regulation is still poorly understood in species other than the model organisms.

Boolean network is one of the commonly used methods for building gene regulatory networks from time series microarray data. However, it has a major drawback that requires heavy computing times to infer large scale gene networks. This paper proposes a variable selection method to reduce Boolean network computing times using the chi-square statistics for testing independence in two way contingency tables. We compare the computing times and the accuracy of the estimated network structure by the proposed method with those of the original Boolean network method. For the comparative studies, we use simulated data and a real yeast cell-cycle gene expression data (Spellman , 1998). The comparative results show that the proposed variable selection method improves the computing time of Boolean network algorithm. We expect the proposed variable selection method to be more efficient for the large scale gene regulatory network studies.

It has been a challenge to discover transcription factor (TF) binding motifs (TFBMs), which are short -regulatory DNA sequences playing essential roles in transcriptional regulation. We approach the problem of discovering TFBMs from a steganographic perspective. We view the regulatory regions of a genome as if they constituted a stegoscript with conserved words (i.e., TFBMs) being embedded in a covertext, and model the stegoscript with a statistical model consisting of a dictionary and a grammar. We develop an efficient algorithm, , to learn such a model from a stegoscript and to recover conserved motifs. Subsequently, we select biologically meaningful motifs based on a motif’s specificity to the set of genes of interest and/or the expression coherence of the genes whose promoters contain the motif. From the promoters of 645 distinct cell-cycle related genes of , our method is able to identify all known cell-cycle related TFBMs among its top ranking motifs. Our method can also be directly applied to discriminative motif finding. By utilizing the ChIP-chip data of Lee , we predicted potential binding motifs of 113 known transcription factors of budding yeast.

Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. Inspired by epistatic models of multi-locus QTL mapping, we propose a unified model of expression and genotype representing and acting regulation. We demonstrate the power of the model in contrast to standard interval mapping by automatically discovering specific pairs of regulator-target genes in yeast. Our approach’s generality provides a convenient framework for inducing a regulatory network topology of directed and undirected weighted edges.

Phenotypic differences between closely-related species may arise from differential expression regimes, rather than different gene complements. Knowledge of cellular protein levels across a species sample would thus be useful for the inference of the genes underlying such phenotypic differences. dos Reis et al [1] recently proposed the tRNA Adaptation Index to score the optimality of a coding sequence with respect to a species’ cellular tRNA pools. As a preliminary step towards a multi-species analysis that would utilize this index, we examine in this paper its performance in predicting protein expression levels in the yeast and find that it likely predicts maximal potential levels of proteins. We also show that tAI profiles of genes across species carry functional information regarding the interactions between proteins.