Background: The growing use of imaging procedures in medicine has raised concerns about exposure to lowdose\r\nionising radiation (LDIR). While the disastrous effects of high dose ionising radiation (HDIR) is well\r\ndocumented, the detrimental effects of LDIR is not well understood and has been a topic of much debate. Since\r\nlittle is known about the effects of LDIR, various kinds of wet-lab and computational analyses are required to\r\nadvance knowledge in this domain. In this paper we carry out an ââ?¬Å?upside-down pyramidââ?¬Â form of systems biology\r\nanalysis of microarray data. We characterised the global genomic response following 10 cGy (low dose) and\r\n100 cGy (high dose) doses of X-ray ionising radiation at four time points by analysing the topology of gene\r\ncoexpression networks. This study includes a rich experimental design and state-of-the-art computational systems\r\nbiology methods of analysis to study the differences in the transcriptional response of skin cells exposed to low\r\nand high doses of radiation.\r\nResults: Using this method we found important genes that have been linked to immune response, cell survival\r\nand apoptosis. Furthermore, we also were able to identify genes such as BRCA1, ABCA1, TNFRSF1B, MLLT11 that\r\nhave been associated with various types of cancers. We were also able to detect many genes known to be\r\nassociated with various medical conditions.\r\nConclusions: Our method of applying network topological differences can aid in identifying the differences among\r\nsimilar (eg: radiation effect) yet very different biological conditions (eg: different dose and time) to generate\r\ntestable hypotheses. This is the first study where a network level analysis was performed across two different\r\nradiation doses at various time points, thereby illustrating changes in the cellular response over time.
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