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Search Results: 1 - 10 of 408277 matches for " Teresa M Przytycka "
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Phenotypic variation meets systems biology
Teresa M Przytycka
Genome Biology , 2009, DOI: 10.1186/gb-2009-10-8-313
Abstract: This year's annual growth factor and signal transduction symposium held at Iowa State University focused on systems-biology approaches to the study and modeling of complex biological processes. The topics discussed covered a wide spectrum of recent advances, including systems-level approaches to understanding transcriptional and posttranscriptional regulation and systems-level analysis and modeling of the responses of biological systems to perturbations. A few of the highlights of the meeting in these fields are reported here.Transcriptional regulation can be broadly defined as the process in which transcription factors interact with each other, with DNA, and with other biomolecules to regulate gene expression. This complex regulatory process is the subject of intensive study utilizing a variety of experimental and computational techniques. Opening the meeting, David Hume (Roslin Institute, University of Edinburgh, UK) described his recent results on gene regulation in macrophages related to the RIKEN genome Network Project and FNTOM 4 (Functional Annotation of Mammalian Genome). Hume and his collaborators analyzed transcriptional control of the human monocytic cell line THP-1 throughout a time course of growth, arrest and differentiation. Using the deepCAGE technique, they measured the dynamics of genome-wide usage of transcription start sites. This analysis was followed with comparative genomics approaches to predict active regulatory motifs throughout time and to predict the key transcription factors driving differentiation and uncover their time-dependent activation. Some of the predicted factors were subsequently confirmed by knockdowns using small interfering RNAs. Challenging the concept of 'master regulators', Hume argued that cellular states are constrained by complex networks that involve a substantial number of both positive and negative regulations.Transcriptional regulation was also the focus of Timothy Ravasi (University of California, San Diego, USA)
Systems-biology dissection of eukaryotic cell growth
Teresa M Przytycka, Justen Andrews
BMC Biology , 2010, DOI: 10.1186/1741-7007-8-62
Abstract: See research article http://jbiol.com/content/6/2/4 webcite and http://www.biomedcentral.com/1741-7007/8/68 webciteIt is generally appreciated that organismal phenotype is a function of both the genotype and the environment. However, most recent studies have focused on understanding the relationship between genotype and phenotype. Indeed, genetic variations are easier to quantify, data are abundant, and new methods continue to emerge. Utilizing genomic-scale gene expression and various types of molecular interaction data, several groups have started to address the challenge of identifying the molecular pathways that underlie the translation of different genotypic perturbations into corresponding phenotypic output, for example, a particular disease. In contrast, little has been done to dissect the relationship between the environment and the phenotype at the systems-biology level.Understating the relationship between an environmental factor and a phenotype involves uncovering biomolecular pathways participating in a given environment-phenotype response. Just as various genotypic variations might lead to the same disease, various environmental perturbations often lead to the same phenotypic response. In such a case it is to be expected that the responses to these signals involve common pathways, which in turn begs several questions. What are they? What are the intermediate steps before the signals converge to such a common pathway? Which pathway is signal specific? Which molecules are involved and what is the crosstalk between different response pathways? Finally, and most important, where do we start tackling this complex problem?Several groups have begun applying systems-level approaches to study the mechanisms that underlie cellular responses to changing environmental conditions, and these studies suggest that we are on the right path. For example, DeRisi et al. [1] investigated the gene-expression response accompanying the metabolic shift from fermentation to resp
eQTL Epistasis – Challenges and Computational Approaches
Stefan Wuchty?,Teresa M. Przytycka
Frontiers in Genetics , 2013, DOI: 10.3389/fgene.2013.00051
Abstract: The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods.
Interrogating domain-domain interactions with parsimony based approaches
Katia S Guimar?es, Teresa M Przytycka
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-171
Abstract: Building on the previously published Parsimonious Explanation method (PE) to predict domain-domain interactions, we introduced a new Generalized Parsimonious Explanation (GPE) method, which (i) adjusts the granularity of the domain definition to the granularity of the input data set and (ii) permits domain interactions to have different costs. This allowed for preferential selection of the so-called "co-occurring domains" as possible mediators of interactions between proteins. The performance of both variants of the parsimony method are competitive to the performance of the top algorithms for this problem even though parsimony methods use less information than some of the other methods. We also examined possible enrichment of co-occurring domains and homo-domains among domain interactions mediating the interaction of proteins in the network. The corresponding study was performed by surveying domain interactions predicted by the GPE method as well as by using a combinatorial counting approach independent of any prediction method. Our findings indicate that, while there is a considerable propensity towards these special domain pairs among predicted domain interactions, this overrepresentation is significantly lower than in the iPfam dataset.The Generalized Parsimonious Explanation approach provides a new means to predict and study domain-domain interactions. We showed that, under the assumption that all protein interactions in the network are mediated by domain interactions, there exists a significant deviation of the properties of domain interactions mediating interactions in the network from that of iPfam data.Understanding of protein and domain interactions is necessary to comprehend the functioning of a cell. In the past few years, this area has been the subject of intensive study (surveyed in [1]) As the power and the limitations of methods to predict domain interactions become clear, new and improved approaches have been developed [2,3].Protein interaction data
Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment
Raja Jothi, Teresa M Przytycka, L Aravind
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-173
Abstract: Our experimentation with E. coli and yeast proteins with 16 different carefully composed reference sets of genomes revealed that the phyletic patterns of proteins in prokaryotes alone could be adequate enough to make reasonably accurate functional linkage predictions. A slight improvement in performance is observed on adding few eukaryotes into the reference set, but a noticeable drop-off in performance is observed with increased number of eukaryotes. Inclusion of most parasitic, pathogenic or vertebrate genomes and multiple strains of the same species into the reference set do not necessarily contribute to an improved sensitivity or accuracy. Interestingly, we also found that evolutionary histories of individual pathways have a significant affect on the performance of the PPC approach with respect to a particular reference set. For example, to accurately predict functional links in carbohydrate or lipid metabolism, a reference set solely composed of prokaryotic (or bacterial) genomes performed among the best compared to one composed of genomes from all three super-kingdoms; this is in contrast to predicting functional links in translation for which a reference set composed of prokaryotic (or bacterial) genomes performed the worst. We also demonstrate that the widely used random null model to quantify the statistical significance of profile similarity is incomplete, which could result in an increased number of false-positives.Contrary to previous proposals, it is not merely the number of genomes but a careful selection of informative genomes in the reference set that influences the prediction accuracy of the PPC approach. We note that the predictive power of the PPC approach, especially in eukaryotes, is heavily influenced by the primary endosymbiosis and subsequent bacterial contributions. The over-representation of parasitic unicellular eukaryotes and vertebrates additionally make eukaryotes less useful in the reference sets. Reference sets composed of highly non-
Network integration meets network dynamics
Teresa M Przytycka, Yoo-Ah Kim
BMC Biology , 2010, DOI: 10.1186/1741-7007-8-48
Abstract: New high-throughput experimental techniques, complemented by recently developed computational methods, have facilitated the initial reconstructions of large-scale cellular networks. These reconstructions provide important clues about the topological organization of these networks and elucidate relationships between the topological characteristics and biological properties of the corresponding molecules. In particular, studies of protein-interaction networks have revealed complex relationships between vertex degree (number of neighbors in the network), network modularity (organization of the network into connected subnetworks), gene essentiality, gene pleiotropy, and so on. Importantly, despite considerable noise in the data, the utility of these networks goes beyond merely describing the rough landscape of biomolecular systems. They are being used increasingly to predict functionality of individual molecules in the network, membership in protein complexes, association with signaling pathways, disease-associated subnetworks, and so on (see [1] and references therein).Experimentally and computationally derived networks, such as protein-protein interaction networks, regulatory networks or metabolic networks, provide static depictions of the dynamically changing cellular environment. Therefore, their utility for modeling cellular dynamics might not be clear. However, it is now increasingly recognized that static network topology can be used as a scaffold for studies of network dynamics. In fact, some dynamical properties can be uncovered from network topology alone, or in combination with other types of data, such as gene expression. For example, an analysis of network connectivity in terms of possible ways in which information can be propagated has been used to predict the molecules perturbed as a result of gene knockouts [1,2]. A more recent study combined protein-protein interactions, protein-DNA interactions, and phosphorylation networks with gene-expression profile
Bridging the Gap between Genotype and Phenotype via Network Approaches
Yoo-Ah Kim,Teresa M. Przytycka
Frontiers in Genetics , 2013, DOI: 10.3389/fgene.2012.00227
Abstract: In the last few years we have witnessed tremendous progress in detecting associations between genetic variations and complex traits. While genome-wide association studies have been able to discover genomic regions that may influence many common human diseases, these discoveries created an urgent need for methods that extend the knowledge of genotype-phenotype relationships to the level of the molecular mechanisms behind them. To address this emerging need, computational approaches increasingly utilize a pathway-centric perspective. These new methods often utilize known or predicted interactions between genes and/or gene products. In this review, we survey recently developed network based methods that attempt to bridge the genotype-phenotype gap. We note that although these methods help narrow the gap between genotype and phenotype relationships, these approaches alone cannot provide the precise details of underlying mechanisms and current research is still far from closing the gap.
Identifying Causal Genes and Dysregulated Pathways in Complex Diseases
Yoo-Ah Kim,Stefan Wuchty,Teresa M. Przytycka
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1001095
Abstract: In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.
Secondary structure spatial conformation footprint: a novel method for fast protein structure comparison and classification
Elena Zotenko, Dianne P O'Leary, Teresa M Przytycka
BMC Structural Biology , 2006, DOI: 10.1186/1472-6807-6-12
Abstract: In this paper we propose a novel projection method that uses secondary structure information to produce the mapping. First, a diverse set of spatial arrangements of triplets of secondary structure elements, a set of structural models, is automatically selected. Then, each protein structure is mapped into a high-dimensional vector of "counts" or footprint, where each count corresponds to the number of times a given structural model is observed in the structure, weighted by the precision with which the model is reproduced. We perform the first comprehensive evaluation of our method together with all other currently known projection methods.The results of our evaluation suggest that the type of structural information used by a projection method affects the ability of the method to detect structural similarity. In particular, our method that uses the spatial conformations of triplets of secondary structure elements outperforms other methods in most of the tests.The extensive collection of protein sequence and structure information has resulted in the creation of numerous classification resources for organizing proteins [1]. Two main structure-based classification databases, SCOP [2] and CATH [3], combine sequence, structural, and functional information to provide a hierarchical classification of known protein structures in the Protein Data Bank (PDB) [4]. In the SCOP database, for example, proteins are organized into a four-level hierarchy: class, fold, super-family, and family. Members of the same family group share a clear common evolutionary origin, supported either by significant sequence similarity or significant structural and functional similarity. The families are grouped into super-families based on structural or functional similarity that suggest a probable common evolutionary origin. The fold level groups proteins based on the arrangement of major secondary structure elements. And finally the class level groups proteins according to their secondary structure
Differences in evolutionary pressure acting within highly conserved ortholog groups
Teresa M Przytycka, Raja Jothi, L Aravind, David J Lipman
BMC Evolutionary Biology , 2008, DOI: 10.1186/1471-2148-8-208
Abstract: Using correlations in entropy measures as a proxy for evolutionary pressure, we observed two distinct behaviors within our ortholog collection. The first subset of ortholog groups, called here informational, consisted mostly of proteins associated with information processing (i.e. translation, transcription, DNA replication) and the second, the non-informational ortholog groups, mostly comprised of proteins involved in metabolic pathways. The evolutionary pressure acting on non-informational proteins is more uniform relative to their informational counterparts. The non-informational proteins show higher level of correlation between entropy profiles and more uniformity across subgroups.The low correlation of entropy profiles in the informational ortholog groups suggest that the evolutionary pressure acting on the informational ortholog groups is not uniform across different clades considered this study. This might suggest "fine-tuning" of informational proteins in each lineage leading to lineage-specific differences in selection. This, in turn, could make these proteins less exchangeable between lineages. In contrast, the uniformity of the selective pressure acting on the non-informational groups might allow the exchange of the genetic material via lateral gene transfer.Previous studies have shown that proteins are under purifying selection which enforces a certain stasis in terms of sequence and function. Much less frequently they are subject to episodes of positive selection, which are typified by accelerated sequence divergence and corresponding functional shifts [1-6]. A basic assumption in molecular evolution is that the selective pressure represents functional constraints and is correlated with evolutionary conservation [2]. Direct measurement of the functional constraints is not straight-forward; however its effects may be estimated through sequence conservation. For closely related species, selective pressure is usually measured using a nucleotide alignment a
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