Systems biology approaches in cell signaling research.
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The use of methods for global and quantitative analysis of cells is providing new systems-level insights into signal transduction processes. Recent studies reveal important information about the rates of signal transmission and propagation, help establish some general regulatory characteristics of multi-tiered signaling cascades, and illuminate the combinatorial nature of signaling specificity in cell differentiation. (+info)
Qualitative differences of divalent salts: multidimensional scaling and cluster analysis.
(58/2426)
Sensations from salts of iron, calcium, magnesium, and zinc with different anions were studied using a sorting task and multidimensional scaling (MDS). Ten divalent salts were adjusted in concentrations such that the mean intensity ratings were approximately equal. Stimuli were sorted on the basis of similarity to minimize any semantic influence and were examined with and without nasal occlusion to eliminate retronasal cues. Compounds representing the four primary tastes and astringency were also sorted. Similarity estimates were derived from sorting and were submitted to MDS. Divalent salts fell outside the area of the space defined by the four primary tastes. The nose-open condition showed that some of the divalent salts have unique metallic sensations along with astringency. The groupings obtained were corroborated using single-linkage cluster analysis. An iron group was most distinctive in metallic sensations; calcium and magnesium salts were primarily bitter; and zinc salts were characterized by astringency. When nasal cues were not available, the sensations from the divalent salts were mainly explained by bitterness and astringency. Results were consistent with a previous evaluation of divalent salts using descriptive analysis. (+info)
Interlinked fast and slow positive feedback loops drive reliable cell decisions.
(59/2426)
Positive feedback is a ubiquitous signal transduction motif that allows systems to convert graded inputs into decisive, all-or-none outputs. Here we investigate why the positive feedback switches that regulate polarization of budding yeast, calcium signaling, Xenopus oocyte maturation, and various other processes use multiple interlinked loops rather than single positive feedback loops. Mathematical simulations revealed that linking fast and slow positive feedback loops creates a "dual-time" switch that is both rapidly inducible and resistant to noise in the upstream signaling system. (+info)
Prospects for personalized cardiovascular medicine: the impact of genomics.
(60/2426)
Sequencing of the human genome has ushered in prospects for individualizing cardiovascular health care. There is growing evidence that the practice of cardiovascular medicine might soon have a new toolbox to predict and treat disease more effectively. The Human Genome Project has spawned several important "omic" technologies that allow "whole genome" interrogation of sequence variation (re-sequencing, genotyping, comparative genome hybridization), transcription (expression profiling, tissue arrays), proteins (gas or liquid chromatography and tandem mass spectroscopy [MS]), and metabolites (MS or nuclear magnetic resonance profiling); deoxyribonucleic acid, ribonucleic acid, protein, and metabolic approaches all provide more exacting detail of cardiovascular disease mechanisms and, in some cases, are redefining its taxonomy. Pharmacogenomic approaches are emerging across broad classes of cardiovascular therapeutics to assist practitioners in making more precise decisions about which drugs to give to which patients to optimize the benefit-to-risk ratio. Molecular imaging is developing chemical and biological probes that can sense molecular pathway mechanisms that will allow us to monitor health and disease. Together, these tools will enable a paradigm shift from genetic medicine--on the basis of the study of individual inherited characteristics, most often single genes--to genomic medicine, which by its nature is comprehensive and focuses on the functions and interactions of multiple genes and gene products, among themselves and with their environment. The information gained from such analyses, in combination with clinical data, is now allowing us to assess individual risks and guide clinical management and decision-making, all of which form the basis for cardiovascular genomic medicine. (+info)
Integrating cytomics and proteomics.
(61/2426)
Systems biology along with what is now classified as cytomics provides an excellent opportunity for cytometry to become integrated into studies where identification of functional proteins in complex cellular mixtures is desired. The combination of cell sorting with rapid protein-profiling platforms offers an automated and rapid technique for greater clarity, accuracy, and efficiency in identification of protein expression differences in mixed cell populations. The integration of cell sorting to purify cell populations opens up a new area for proteomic analysis. This article outlines an approach in which well defined cell analysis and separation tools are integrated into the proteomic programs within a core laboratory. In addition we introduce the concepts of flow cytometry sorting to demonstrate the importance of being able to use flow cytometry as a cell separation technology to identify and collect purified cell populations. Data demonstrating the speed and versatility of this combination of flow cytometry-based cell separation and protein separation and subsequent analysis, examples of protein maps from purified sorted cells, and an analysis of the overall procedure will be shown. It is clear that the power of cell sorting to separate heterogeneous populations of cells using specific phenotypic characteristics increases the power of rapid automated protein separation technologies. (+info)
Molecular interaction maps of bioregulatory networks: a general rubric for systems biology.
(62/2426)
A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. "Heuristic" MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. "Explicit" MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing a MIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one's understanding of the structure and function of a network. (+info)
A data integration methodology for systems biology: experimental verification.
(63/2426)
The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets. (+info)
A data integration methodology for systems biology.
(64/2426)
Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST. (+info)