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Modeling Arsenic Accumulation in Plants
University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
University of Skövde, School of Life Sciences.
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2011 (English)In: 2011 Second International Conference on Emerging Applications of Information Technology, IEEE Computer Society, 2011, 133-137 p.Conference paper, (Refereed)
Abstract [en]

Rice growing regions plagued by arsenic-contaminated soils and irrigation water do not have a viable option for producing arsenic-free crops. For instance, in Bangladesh every year more than 30 million people are affected from rice-derived arsenic contamination that contributes to arsenic levels known to cause health-related illnesses. Our strategy is to genetically-modify molecular mechanisms involved in the localization of arsenic to divert it to the non-edible parts of the plant. To identify viable candidate genes, we employed data mining, an in silico analysis based on searching existing genomic databases and in the genetic model plant Arabidopsis thaliana. To assist our investigation, we constructed a kinetic model to outline strategies for developing genetically-modified plants exhibiting a significant reduction in arsenic concentration in the edible parts (straw and grain). This model contains equations for uptake, metabolism and sequestration of different types of arsenic (As (V), As (III), MMAA and DMAA). The model was implemented using XPP and validated against existing data from the literature. From these analysis, we identified four candidate genes that are involved either in uptake, transport or cellular localization of arsenic in plants. But we found only one gene implicated in arsenic metabolism in rice. In parallel, we identified available T-DNA insertion mutants to determine the effects of these genes on arsenic accumulation. Results obtained from in silico data-mining, kinetic modeling, and assays with T-DNA insertion mutants will be used to design gene cloning experiments to study the target genes in yeast, E. coli and Arabidopsis heterologous systems. Upon confirmation of the effectiveness of these candidates, vectors containing the target genes will be constructed for transformation into rice. The new rice varieties produced will be tested under field conditions to assess their effectiveness at reducing or eliminating arsenic from the edible parts of the rice plant.

Place, publisher, year, edition, pages
IEEE Computer Society, 2011. 133-137 p.
Keyword [en]
Arabidopsis, data-mining, rice, arsenic accumulation, arsenic metabolism, arsenic uptake
National Category
Natural Sciences
Research subject
Natural sciences
Identifiers
URN: urn:nbn:se:his:diva-5199DOI: 10.1109/EAIT.2011.91Scopus ID: 2-s2.0-79953867832ISBN: 978-0-7695-4329-1/11 (print)OAI: oai:DiVA.org:his-5199DiVA: diva2:429251
Conference
2nd International Conference on Emerging Applications of Information Technology, EAIT 2011;Kolkata;19 February 2011through20 February 2011
Available from: 2011-07-04 Created: 2011-07-04 Last updated: 2013-08-27

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Mandal, AbulLundh, DanNahar, Noor
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CiteExportLink to record
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Citation style
  • apa
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