Browsing by Subject "systems biology"

Now showing 1 - 5 of 5
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    Computational development of regulatory gene set networks for systems biology applications
    (2014) Suphavilai, Chayaporn; Chen, Jake Y.; Fang, Shiaofen; Al Hasan, Mohammad
    In systems biology study, biological networks were used to gain insights into biological systems. While the traditional approach to studying biological networks is based on the identification of interactions among genes or the identification of a gene set ranking according to differentially expressed gene lists, little is known about interactions between higher order biological systems, a network of gene sets. Several types of gene set network have been proposed including co-membership, linkage, and co-enrichment human gene set networks. However, to our knowledge, none of them contains directionality information. Therefore, in this study we proposed a method to construct a regulatory gene set network, a directed network, which reveals novel relationships among gene sets. A regulatory gene set network was constructed by using publicly available gene regulation data. A directed edge in regulatory gene set networks represents a regulatory relationship from one gene set to the other gene set. A regulatory gene set network was compared with another type of gene set network to show that the regulatory network provides additional information. In order to show that a regulatory gene set network is useful for understand the underlying mechanism of a disease, an Alzheimer's disease (AD) regulatory gene set network was constructed. In addition, we developed Pathway and Annotated Gene-set Electronic Repository (PAGER), an online systems biology tool for constructing and visualizing gene and gene set networks from multiple gene set collections. PAGER is available at http://discern.uits.iu.edu:8340/PAGER/. Global regulatory and global co-membership gene set networks were pre-computed. PAGER contains 166,489 gene sets, 92,108,741 co-membership edges, 697,221,810 regulatory edges, 44,188 genes, 651,586 unique gene regulations, and 650,160 unique gene interactions. PAGER provided several unique features including constructing regulatory gene set networks, generating expanded gene set networks, and constructing gene networks within a gene set. However, tissue specific or disease specific information was not considered in the disease specific network constructing process, so it might not have high accuracy of presenting the high level relationship among gene sets in the disease context. Therefore, our framework can be improved by collecting higher resolution data, such as tissue specific and disease specific gene regulations and gene sets. In addition, experimental gene expression data can be applied to add more information to the gene set network. For the current version of PAGER, the size of gene and gene set networks are limited to 100 nodes due to browser memory constraint. Our future plans is integrating internal gene or proteins interactions inside pathways in order to support future systems biology study.
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    Dissecting the expression dynamics of RNA-binding proteins in posttranscriptional regulatory networks
    (2009-12) Mittal, Nitish; Roy, Nilanjan; Babu, M. Madan; Janga, Sarath Chandra
    In eukaryotic organisms, gene expression requires an additional level of coordination that links transcriptional and posttranslational processes. Messenger RNAs have traditionally been viewed as passive molecules in the pathway from transcription to translation. However, it is now clear that RNA-binding proteins (RBPs) play an important role in cellular homeostasis by controlling gene expression at the posttranscriptional level. Here, we show that RBPs, as a class of proteins, show distinct gene expression dynamics compared to other protein coding genes in the eukaryote Sacchoromyces cerevisiae. We find that RBPs generally exhibit high protein stability, translational efficiency, and protein abundance but their encoding transcripts tend to have a low half-life. We show that RBPs are also most often posttranslationally modified, indicating their potential for regulation at the protein level to control diverse cellular processes. Further analysis of the RBP-RNA interaction network showed that the number of distinct targets bound by an RBP (connectivity) is strongly correlated with its protein stability, translational efficiency, and abundance. We also note that RBPs show less noise in their expression in a population of cells, with highly connected RBPs showing significantly lower noise. Our results indicate that highly connected RBPs are likely to be tightly regulated at the protein level as significant changes in their expression may bring about large-scale changes in global expression levels by affecting their targets. These observations might explain the molecular basis behind the cause of a number of disorders associated with misexpression or mutation in RBPs. Future studies uncovering the posttranscriptional networks in higher eukaryotes can help our understanding of the link between different levels of regulation and their role in pathological conditions.
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    Interfacing systems biology and synthetic biology
    (2009-06) Lister, Allyson; Charoensawan, Varodom; De, Subhajyoti; James, Katherine; Janga, Sarath Chandra; Huppert, Julian
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    Systems and Network-Based Approaches for Personalized Medicine
    (2014-11) Janga, Sarath Chandra; Edupuganti, Mohan Mallikarjuna Rao
    Most biological outcomes in a cell arise from a complex interplay between different cellular entities such as proteins, DNA, RNA and metabolites. Therefore, a key challenge for biology in the twenty-first century is to understand the structure and dynamics of the complex web of interactions in a cell that contribute to its proper functioning. Recent years have seen a surge in the amount of “omics” data and an integration of several disciplines which has influenced all areas of life sciences, from molecular biology to medicine. With the emergence of a number of sophisticated tools and technologies as a result of genomics revolution, we are now in a position to view the molecular aspects of diseases at a systems level by incorporating various cellular entities into a network framework. Such systems/network-based approaches are not only enabling us to develop models of disease and wellness in a population but also contributing to our efforts to reverse engineer the molecular networks corresponding to disease states by perturbing using drug cocktails. These multi-scale personalized medicine approaches are likely to significantly re-shape the health care industry in the coming decades and decrease the division that we currently see between medicine and other biotechnology disciplines.
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    Transcriptional regulation constrains the organization of genes on eukaryotic chromosomes
    (2008-10) Janga, Sarath Chandra; Collado-Vides, Julio; Babu, M. Madan
    Genetic material in eukaryotes is tightly packaged in a hierarchical manner into multiple linear chromosomes within the nucleus. Although it is known that eukaryotic transcriptional regulation is complex and requires an intricate coordination of several molecular events both in space and time, whether the complexity of this process constrains genome organization is still unknown. Here, we present evidence for the existence of a higher-order organization of genes across and within chromosomes that is constrained by transcriptional regulation. In particular, we reveal that the target genes (TGs) of transcription factors (TFs) for the yeast, Saccharomyces cerevisiae, are encoded in a highly ordered manner both across and within the 16 chromosomes. We show that (i) the TGs of a majority of TFs show a strong preference to be encoded on specific chromosomes, (ii) the TGs of a significant number of TFs display a strong preference (or avoidance) to be encoded in regions containing particular chromosomal landmarks such as telomeres and centromeres, and (iii) the TGs of most TFs are positionally clustered within a chromosome. Our results demonstrate that specific organization of genes that allowed for efficient control of transcription within the nuclear space has been selected during evolution. We anticipate that uncovering such higher-order organization of genes in other eukaryotes will provide insights into nuclear architecture, and will have implications in genetic engineering experiments, gene therapy, and understanding disease conditions that involve chromosomal aberrations.