RESUMO
Experimental time series provide an informative window into the underlying dynamical system, and the timing of the extrema of a time series (or its derivative) contains information about its structure. However, the time series often contain significant measurement errors. We describe a method for characterizing a time series for any assumed level of measurement error [Formula: see text] by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that [Formula: see text]-approximates the time series. Based on the merge tree of a continuous function, we define a new object called the normalized branch decomposition, which allows us to compute intervals for any level [Formula: see text]. We show that there is a well-defined total order on these intervals for a single time series, and that it is naturally extended to a partial order across a collection of time series comprising a dataset. We use the order of the extracted intervals in two applications. First, the partial order describing a single dataset can be used to pattern match against switching model output (Cummins et al. in SIAM J Appl Dyn Syst 17(2):1589-1616, 2018), which allows the rejection of a network model. Second, the comparison between graph distances of the partial orders of different datasets can be used to quantify similarity between biological replicates.
Assuntos
Modelos Biológicos , Algoritmos , Causalidade , Ciclo Celular/genética , Biologia Computacional , Bases de Dados Factuais/estatística & dados numéricos , Redes Reguladoras de Genes , Análise de Séries Temporais Interrompida/estatística & dados numéricos , Modelos Lineares , Conceitos Matemáticos , Modelos Genéticos , Saccharomyces cerevisiae/citologia , Saccharomyces cerevisiae/genética , Razão Sinal-Ruído , Fatores de TempoRESUMO
Burkitt's lymphoma (BL) is a B-cell malignancy associated with the Epstein-Barr virus (EBV). Mounting evidence has implicated heparan sulfate proteoglycans and heparan sulfate-like glycosaminoglycans (HSGAGs) in the initiation, severity, and progression of the malignancy. The importance of HSGAGs in regulating BL cell growth was therefore examined. Extracellular exogenous heparin inhibited cell growth >30%, while heparin internalized with poly(beta-amino ester)s promoted proliferation up to 58%. The growth-modulating effects of heparin and internalized heparin were dependent on cell surface HSGAGs, PI3K, and Erk/Mek. Treatment of cells with protamine sulfate or with heparinases potently inhibited proliferation, with the greatest effects induced by heparinase I. Cell surface HSGAGs therefore play an important role in regulating BL proliferation and may offer a potential target for therapeutic intervention.
Assuntos
Linfoma de Burkitt/química , Linfoma de Burkitt/patologia , Proliferação de Células , Inibidores do Crescimento/fisiologia , Heparina/química , Heparina/fisiologia , Linfoma de Burkitt/metabolismo , Linhagem Celular Tumoral , Regulação para Baixo/fisiologia , Inibidores do Crescimento/farmacologia , Heparina/metabolismo , Heparina/farmacologia , Humanos , Regulação para Cima/fisiologiaRESUMO
The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.