A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

<p>Abstract</p> <p>Background</p> <p>The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.</p> <p>Methods</p> <p>In this work, we propose <it>e</it>-CCC-Biclustering, a biclustering algorithm that finds and reports all maximal contiguous column coherent biclusters with approximate expression patterns in time polynomial in the size of the time series gene expression matrix. This polynomial time complexity is achieved by manipulating a discretized version of the original matrix using efficient string processing techniques. We also propose extensions to deal with missing values, discover anticorrelated and scaled expression patterns, and different ways to compute the errors allowed in the expression patterns. We propose a scoring criterion combining the statistical significance of expression patterns with a similarity measure between overlapping biclusters.</p> <p>Results</p> <p>We present results in real data showing the effectiveness of <it>e</it>-CCC-Biclustering and its relevance in the discovery of regulatory modules describing the transcriptomic expression patterns occurring in <it>Saccharomyces cerevisiae </it>in response to heat stress. In particular, the results show the advantage of considering approximate patterns when compared to state of the art methods that require exact matching of gene expression time series.</p> <p>Discussion</p> <p>The identification of co-regulated genes, involved in specific biological processes, remains one of the main avenues open to researchers studying gene regulatory networks. The ability of the proposed methodology to efficiently identify sets of genes with similar expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific regulatory mechanisms.</p> <p>Availability</p> <p>A prototype implementation of the algorithm coded in Java together with the dataset and examples used in the paper is available in <url>http://kdbio.inesc-id.pt/software/e-ccc-biclustering</url>.</p

PHYTOCHEMICAL STANDARDIZATION AND ANTIOXIDANT POTENTIAL OF AYURVEDA FORMULATION DARVYADI RASKRIYA

The study represents phytochemical standardization and antioxidant potential of ayurveda formulation Darvyadi Raskriya. The study involves development of high-performance thin-layer chromatographic (HPTLC) method for analysis of formulation. The study utilizes analysis of glycyrrhizin in formulation which is the phytoconstituent of Glycyrrhiza glabra one of the component of formulation. The sample in ethanol was applied on aluminium TLC plates using Linomat 5 spray (CAMAG). Linear ascending development was performed in twin trough glass chamber saturated with mobile phase. The mobile phase consisted of ethyl acetate-methanol-formic acid (10:5:1 v/v/v). The spectrodensitometric detection was performed at the wavelength of 254 nm. The regression analysis was found to be linear with r2=0.997 in the concentration range 5-25 ppm. The antioxidant activity of formulation was also found to be significant as compared to control. Keywords: Standardization, Glycyrrhiza glabra, Glycyrrhizin, HPTLC, Antioxidant

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

Cure of Chronic Viral Infection and Virus-Induced Type 1 Diabetes by Neutralizing Antibodies

The use of neutralizing antibodies is one of the most successful methods to interfere with receptor–ligand interactions in vivo. In particular blockade of soluble inflammatory mediators or their corresponding cellular receptors was proven an effective way to regulate inflammation and/or prevent its negative consequences. However, one problem that comes along with an effective neutralization of inflammatory mediators is the general systemic immunomodulatory effect. It is, therefore, important to design a treatment regimen in a way to strike at the right place and at the right time in order to achieve maximal effects with minimal duration of immunosuppression or hyperactivation. In this review, we reflect on two examples of how short time administration of such neutralizing antibodies can block two distinct inflammatory consequences of viral infection. First, we review recent findings that blockade of IL-10/IL-10R interaction can resolve chronic viral infection and second, we reflect on how neutralization of the chemokine CXCL10 can abrogate virus-induced type 1 diabetes

Population mechanics: A mathematical framework to study T cell homeostasis

Unlike other cell types, T cells do not form spatially arranged tissues, but move independently throughout the body. Accordingly, the number of T cells in the organism does not depend on physical constraints imposed by the shape or size of specific organs. Instead, it is determined by competition for interleukins. From the perspective of classical population dynamics, competition for resources seems to be at odds with the observed high clone diversity, leading to the so-called diversity paradox. In this work we make use of population mechanics, a non-standard theoretical approach to T cell homeostasis that accounts for clone diversity as arising from competition for interleukins. The proposed models show that carrying capacities of T cell populations naturally emerge from the balance between interleukins production and consumption. These models also suggest remarkable functional differences in the maintenance of diversity in naïve and memory pools. In particular, the distribution of memory clones would be biased towards clones activated more recently, or responding to more aggressive pathogenic threats. In contrast, permanence of naïve T cell clones would be determined by their affinity for cognate antigens. From this viewpoint, positive and negative selection can be understood as mechanisms to maximize naïve T cell diversity

Transformation of Human Mesenchymal Cells and Skin Fibroblasts into Hematopoietic Cells

Patients with prolonged myelosuppression require frequent platelet and occasional granulocyte transfusions. Multi-donor transfusions induce alloimmunization, thereby increasing morbidity and mortality. Therefore, an autologous or HLA-matched allogeneic source of platelets and granulocytes is needed. To determine whether nonhematopoietic cells can be reprogrammed into hematopoietic cells, human mesenchymal stromal cells (MSCs) and skin fibroblasts were incubated with the demethylating agent 5-azacytidine (Aza) and the growth factors (GF) granulocyte-macrophage colony-stimulating factor and stem cell factor. This treatment transformed MSCs to round, non-adherent cells expressing T-, B-, myeloid-, or stem/progenitor-cell markers. The transformed cells engrafted as hematopoietic cells in bone marrow of immunodeficient mice. DNA methylation and mRNA array analysis suggested that Aza and GF treatment demethylated and activated HOXB genes. Indeed, transfection of MSCs or skin fibroblasts with HOXB4, HOXB5, and HOXB2 genes transformed them into hematopoietic cells. Further studies are needed to determine whether transformed MSCs or skin fibroblasts are suitable for therapy

Micropropagation and conservation of selected endangered anticancer medicinal plants from the Western Ghats of India

Globally, cancer is a constant battle which severely affects the human population. The major limitations of the anticancer drugs are the deleterious side effects on the quality of life. Plants play a vital role in curing many diseases with minimal or no side effects. Phytocompounds derived from various medicinal plants serve as the best source of drugs to treat cancer. The global demand for phytomedicines is mostly reached by the medicinal herbs from the tropical nations of the world even though many plant species are threatened with extinction. India is one of the mega diverse countries of the world due to its ecological habitats, latitudinal variation, and diverse climatic range. Western Ghats of India is one of the most important depositories of endemic herbs. It is found along the stretch of south western part of India and constitutes rain forest with more than 4000 diverse medicinal plant species. In recent times, many of these therapeutically valued herbs have become endangered and are being included under the red-listed plant category in this region. Due to a sharp rise in the demand for plant-based products, this rich collection is diminishing at an alarming rate that eventually triggered dangerous to biodiversity. Thus, conservation of the endangered medicinal plants has become a matter of importance. The conservation by using only in situ approaches may not be sufficient enough to safeguard such a huge bio-resource of endangered medicinal plants. Hence, the use of biotechnological methods would be vital to complement the ex vitro protection programs and help to reestablish endangered plant species. In this backdrop, the key tools of biotechnology that could assist plant conservation were developed in terms of in vitro regeneration, seed banking, DNA storage, pollen storage, germplasm storage, gene bank (field gene banking), tissue bank, and cryopreservation. In this chapter, an attempt has been made to critically review major endangered medicinal plants that possess anticancer compounds and their conservation aspects by integrating various biotechnological tool